Original Research

Chronic pain is a worldwide cause of impairment. According to data from the 2016 National Health Interview Survey, an estimated 50 million American adults suffer from chronic pain, with 19.6 million adults suffering from high-impact chronic pain.¹ This phenomenon is particularly prevalent in the older population. More than 25% of adults aged 65 to 74 years reported that they were often in pain in the past 3 months compared with just 10% of those adults between the ages of 18 and 44 years.²

The economic burdens of chronic pain disorders are well known. In 2010, Gaskin and Richard found that chronic pain has far-reaching consequences for the US economy, ranging from direct health care costs to lost productivity. This study estimated additional health care costs at about $300 billion yearly and lost productivity at $300 billion, bringing total annual costs to about $600 billion. This expense is more than heart disease alone ($309 billion), and cancer and diabetes mellitus ($243 billion and $188 billion respectively) combined.³

Opioid medications are powerful and effective pain-reducing agents that are indicated for short-term acute pain or long-term in the management of chronic, severe cancer-related pain.⁴ Although efficacious, use of these medications carries with it the inherent risks of abuse, misuse, addiction, and overdose.⁵ Since 1999, opioid-related overdose deaths have been on the rise. The CDC estimated that > 15,000 deaths were attributable specifically to prescription opioids in 2015.⁶ The estimates had risen to > 17,000 deaths in 2017, with the number increasing since that time.⁷ Cumulatively, the CDC estimates that > 200,000 deaths in the US between 1999 and 2017 are attributed to prescription opioid overdose, clearly marking this trend as a growing nationwide epidemic.⁸

In 2016, Florence and colleagues estimated costs associated with opioid overdose to be just shy of $80 billion in 2013 dollars.⁹ In October 2017, the US Department of Health and Human Services declared the opioid epidemic a public health emergency and committed $900 million to combating the crisis.¹⁰

An abundance of data exist analyzing outpatient prescribing and its impacts on opioid dependence, particularly postoperatively. A study by Brummett and colleagues indicated that the incidence of new persistent opioid use in patients who underwent surgery was 5.9% to 6.5% and did not differ between major and minor surgical procedures. This study concluded that new opioid use could be considered one of the most common complications after elective surgery.¹¹ Similarly, in 2017 Makary and colleagues found that surgeons tend to overprescribe pain medications after procedures; some prescribing as many as 50 to 60 tablets to control pain after simple procedures.¹² This is in stark contrast to pain guideline recommendations of no more than 10 tablets for most standard operative procedures.¹³

Sun and colleagues conducted a retrospective analysis of health care claims data in more than 18 million opioid-naïve patients who did and did not undergo surgery. Seven of the 11 surgical procedures were associated with an increased risk of chronic opioid use. The highest incidence of chronic opioid use in the first postoperative year was for total hip arthroplasty (1.4%, OR 5.10; 95% CI, 1.29-1.53). The study found that the risk factors most associated with chronic opioid use after surgery were male sex, aged > 50 years, and preoperative history of drug abuse, alcohol abuse, or depression, along with benzodiazepine use or antidepressant use.¹⁴ In a 2018 cohort study that evaluated predictors associated with transitioning to incident chronic opioid therapy, 4 factors were identified. These included opioid duration of action (adjusted odds ratio [AOR], 12.28; 95% CI, 8.1-06-18.72), the parent opioid compound (eg, tramadol vs codeine; AOR, 7.26; 95% CI, 5.20-10.13), the presence of conditions that are very likely to cause chronic pain (AOR, 5.47; 95% CI, 3.89-7.68), and drug use disorders (AOR, 4.02; 95% CI, 2.53-6.40).¹⁵

While there has been research into outpatient risk factors and medical practices that may contribute to chronic opioid use, a relative paucity of data exists on the contribution of hospitalization and inpatient opioid use on patient outcomes. A 2014 Canadian study assessed the impact of opioid use in the intensive care unit (ICU) on opioid use after discharge.¹⁶ This study included more than 2,500 patients who were admitted to a Canadian ICU between 2005 and 2008, and then followed after discharge for 48 months to quantify chronic opioid use. Nonopioid users increased from 87.8% in the early post-ICU period to 95.6% at 48 months after discharge. Preadmission chronic opioid use and prolonged hospital length of stay (LOS) were found to be associated with an increased risk of chronic opioid use after discharge.¹⁶ To date, there are no published studies that analyze the incidence of opioid-naïve veterans who convert to chronic opioid use after receiving opioids during an acute hospitalization.

In this retrospective analysis, we analyze the incidence of chronic opioid use after administration of opioids in the ICU as well as a variety of risk factors that may influence conversion to chronic opioid use.

 

Methods

This analysis was a single center, retrospective chart review conducted for patients admitted between July 1, 2017 and December 31, 2017 at the US Department of Veterans Affairs (VA) Michael E. DeBakey VA Medical Center (MEDVAMC) in Houston, Texas. MEDVAMC is a 538-bed academic\teaching hospital serving about 130,000 veterans in Southeast Texas. The hospital has 3 ICUs (medical, cardiovascular, and surgical) and 38 total ICU beds. The study was approved by the Baylor College of Medicine Institutional Review Board and MEDVAMC Research and Development Review Board. Informed consent was not required.

Inclusion criteria consisted of patients aged ≥ 18 years admitted to the ICU in the above-specified time frame, who were administered a continuous infusion of an opioid for at least 12 hours. Patients were excluded if they were not opioid naïve prior to admission, defined as receiving > 30 days of opioids in the prior 12 months. Patients who died during hospital admission, never received an opioid despite having an active order, were hospital-to-hospital transfers, or were still admitted at the time of data collection were excluded from the analysis.

All pertinent data were collected using the VA Computerized Patient Record System (CPRS) and the Critical Care Manager (Picis Clinical Solutions) ICU monitoring application. Critical Care Manager was used to track the time frame, duration, and amounts of opioid infusions administered in the ICU. Patient demographic and preadmission data, including date of birth, age, race, history of substance use/alcohol use disorder (defined as a previous diagnosis) and previous opioid prescriptions within the past year were recorded. For the inpatient admission, the ICU LOS, hospital LOS, primary admission diagnosis, type of opioid medication administered, and total duration and dose of opioid administered were collected. After discharge, opioid medication fill data at 3, 6, and 12 months were collected. This information included names of any outpatient opioids filled, dosage unit, quantity, day supply, and number of refills.

The primary outcome for this study was to determine the incidence of chronic opioid use at 3, 6, and 12 months after discharge, defined as the percentage of patients receiving outpatient opioid prescriptions at each time point. Analyses were conducted to observe the effect of age, race, history of substance use or history of alcohol use (International Classification of Diseases documented diagnosis, 9th edition), ICU type (medical, surgical, or cardiovascular), surgical/nonsurgical admission, ICU LOS, hospital LOS, total time, and amount of opioids administered during admission on risk of conversion to chronic opioid use.

Descriptive statistics were calculated to analyze the incidence of chronic opioid use. Univariate logistic regression analysis, including ORs, 95% CIs, and P values, was conducted to determine the effects of the risk factors noted earlier on chronic opioid use at each time point. A multivariate logistic regression model was performed to assess the effect of multiple independent variables on opioid use at 3, 6, and 12 months. Statistical analysis was performed using StataCorp Stata SE.

Results

During the study period, 330 patients were admitted to the ICU. After applying inclusion/exclusion criteria, 118 patients were included in the final analysis. The most frequent reasons for exclusion from the study were patient death (n = 77), a past history of opioid use (n = 56), and not having received an opioid infusion for at least 12 hours (n = 68). The average age of the patients included was 67 years (Table 1). A total of 14% and 9% of patients, respectively, had a diagnosis of alcohol use disorder or substance use disorder recorded in CPRS. After admission, the most common location for these patients was the surgical ICU (65%). All patients were male. The average hospital LOS was 18.6 days , and the ICU LOS was 8.3 days. The average duration of administration for the opioid (fentanyl) infusion was 63 hours, and the average amount of fentanyl administered to each patient was 57.1 mcg/h.

 

The incidence of opioid-naïve patients receiving opioids after discharge was 76.3% (n = 90) at 3 months, 19.5% (n = 23) at 6 months and 7.6% (n = 9) at 12 months (Figure). The daily morphine milligram equivalent (MME) of patients prescribed opioids at 3, 6, and 12 months was similar (3 months, 22.7; 6 months, 19.7; 12 months, 20.9). In the univariate regression analysis, several variables were found to be associated with converting to chronic opioid use. Prior history of alcohol use disorder (OR, 0.3; 95% CI, 0.10-0.88; P = .03), ICU type (OR, 3.9; 95% CI, 1.73-8.75; P = .001) and ICU LOS (OR, 0.88; 95% CI, 0.81-0.95; P = .01) had a statistically significant association on opioid use at 3 months. (Table 2). No variables evaluated had a statistically significant effect on chronic opioid use at 6 months, and only age (OR 0.93; 95% CI. 0.87-0.99; P = .02) was statistically significant at 12 months. In the multivariate logistic regression analysis, history of alcohol abuse, admission for surgery, and hospital LOS were significant at 3 months (Table 3).

Discussion

In this single-center analysis conducted at a VA academic hospital of opioid-naïve patients who were administered opioids in the ICU, the incidence of patients subsequently receiving outpatient opioid prescriptions at 12 months after discharge was 7.6%. There also was a decrease in the amount of opioids received by patients (daily MME) after discharge at 3, 6, and 12 months. This trend did not demonstrate the propensity for inpatient opioid use to convert opioid-naïve patients to chronic opioid users.

  

The most common outpatient opioids prescribed were hydrocodone/acetaminophen, morphine, and tramadol. Logistic regression showed few factors that correlated significantly with opioid use in the long-term (12 month) period. This finding is a deviation from the findings of Yaffe and colleagues who found hospital LOS to be one of the only predictors of long-term opioid use in their population (defined as use at 48 months).¹⁶ One important difference between our study and the Yaffe and colleagues study was that they evaluated all patients who were admitted to the ICU, regardless of the exposure to opioids during their inpatient stay. Consequently, this difference may have resulted in the differences in findings.

Strengths and Limitations

Location was a strength of our study, as this analysis was conducted at an integrated health care system that provides comprehensive inpatient and outpatient care. The VA uses a closed electronic health record, which allowed patients to be followed both in the inpatient and outpatient settings to determine which medications were prescribed at each time. In other health care systems this information would have been difficult to follow as patients often fill outpatient prescriptions at community pharmacies not affiliated with the treating hospital. However, any patient not using a VA prescriber for subsequent opioid prescriptions or patients who received opioids through other sources would not have had their continued opioid use captured. These data may be available in the states prescription monitoring program, but it was not available to query for research at this time.

 

This study also excluded chronic opioid users, which could have been another confounding factor to account for when analyzing the results. However, the primary objective of the study was to determine the impact of opioids prescribed in the ICU on converting previous opioid-naïve patients to chronic users. Finally, a multivariate logistic regression was incorporated to assess for factors that may predispose certain patients to convert to chronic opioid users. This analysis served to extend the applicability of our study by not only analyzing whether receiving opioids in the ICU contributed to chronic opioid use in the long-term, but also which populations may be at greatest risk. This information can be used in the future to target harm-reduction efforts toward high-risk hospitalized patients.

One limitation of this study was that it was conducted as a retrospective, single-center chart review in Houston, Texas. Because this was not a randomized controlled trial, it is difficult to imply any causation between exposure to opioids in the ICU and chronic use. In addition, because this study was conducted at a single site, the results may not be able to be generalized to other populations. VA populations tend to be elderly and predominantly male, as was reflected by the study population. These factors, along with regional variability in patient characteristics, may limit the generalizability of this study to older male patients located in Southeast Texas or other similar populations. Other limitations of this study also included the small sample size, limited period of follow-up obtained, and that other comorbidity information (pain scores during stay, use of nonopioid pain medications, past history of anxiety or depression, or other acute illnesses or surgeries that may have required opioid therapy during admission) was not collected.

This study was only able to review 118 patients for a follow-up duration of 1 year. In the Yaffe and colleagues study, more than 2,500 patients were followed over 4 years, which provided a more long-term overview of the clinical course of these patients and may be another reason for discrepant findings. However, this study did not actually assess the impact on administration of opioids on the development of chronic opioid use.¹⁶ Finally, the biggest limitation to this study may be the potential for confounding discharge prescriptions. After discharge, patients’ prescriptions were evaluated from discharge to 3 months, in between 3 and 6 months, and between 6 and 12 months for the presence of an opioid prescription. Due to this methodology, any opioid prescription a patient was discharged with was counted in the 3-month time point. Since many patients included in the study were admitted to the surgical ICU (65%), it was logical that they were discharged with opioids after their procedure. While including the immediate postdischarge prescription data was useful for evaluating the decrease in opioid use and incidence over time, it did cause the 3-month time point to appear overly inflated, potentially signaling that at 3 months after discharge many of these patients were still requiring opioid use.

The Society of Critical Care Medicine still recommends opioids as first-line therapy for non-neuropathic pain in the ICU setting.¹⁷ Additionally, postoperative pain can be difficult to manage in the surgical population and is often treated with opioids, though treatment with multimodal pain regimens is becoming more common.¹⁸ It is difficult to imagine that a finding that implicates opioid use in the hospital with conversion to chronic opioid use would prompt a cessation in the use of opioid in these settings, especially in the context of analgosedation related to mechanically ventilated patients. However, it would be plausible to use this knowledge to advocate for opioid-sparing therapies and consideration for weaning individuals at high risk for misuse after discharge from opioid-containing sedation or analgesia regimens in a timelier manner.

Though our findings did not show a clinically relevant increase in chronic opioid users, clinicians can still use this information to encourage targeted education and closer monitoring for those patients deemed as high risk at discharge to prevent unnecessary prolonged opioid use. By having more frequent follow-up in pain clinics, switching patients to nonopioid therapies after discharge, and ensuring high-risk patients are discharged with naloxone rescue kits, it would be possible to drastically reduce the number of potential overdoses for patients who previously required opioid therapy in the ICU.

 

Conclusion

After discharge, 7.6% of previously opioid-naïve patients who were treated with opioids in the ICU were still receiving prescriptions for opioids at 12 months. These findings did not suggest a clinically significant increase in the incidence of chronic opioid use after inpatient administration of opioids. However, these results prompt the need for larger, prospective, multicenter studies to evaluate the effect on hospitalization on converting to chronic opioid use and a deeper evaluation of other potential risk factors that may be present.

Chronic pain is a worldwide cause of impairment. According to data from the 2016 National Health Interview Survey, an estimated 50 million American adults suffer from chronic pain, with 19.6 million adults suffering from high-impact chronic pain.¹ This phenomenon is particularly prevalent in the older population. More than 25% of adults aged 65 to 74 years reported that they were often in pain in the past 3 months compared with just 10% of those adults between the ages of 18 and 44 years.²

The economic burdens of chronic pain disorders are well known. In 2010, Gaskin and Richard found that chronic pain has far-reaching consequences for the US economy, ranging from direct health care costs to lost productivity. This study estimated additional health care costs at about $300 billion yearly and lost productivity at $300 billion, bringing total annual costs to about $600 billion. This expense is more than heart disease alone ($309 billion), and cancer and diabetes mellitus ($243 billion and $188 billion respectively) combined.³

Opioid medications are powerful and effective pain-reducing agents that are indicated for short-term acute pain or long-term in the management of chronic, severe cancer-related pain.⁴ Although efficacious, use of these medications carries with it the inherent risks of abuse, misuse, addiction, and overdose.⁵ Since 1999, opioid-related overdose deaths have been on the rise. The CDC estimated that > 15,000 deaths were attributable specifically to prescription opioids in 2015.⁶ The estimates had risen to > 17,000 deaths in 2017, with the number increasing since that time.⁷ Cumulatively, the CDC estimates that > 200,000 deaths in the US between 1999 and 2017 are attributed to prescription opioid overdose, clearly marking this trend as a growing nationwide epidemic.⁸

In 2016, Florence and colleagues estimated costs associated with opioid overdose to be just shy of $80 billion in 2013 dollars.⁹ In October 2017, the US Department of Health and Human Services declared the opioid epidemic a public health emergency and committed $900 million to combating the crisis.¹⁰

An abundance of data exist analyzing outpatient prescribing and its impacts on opioid dependence, particularly postoperatively. A study by Brummett and colleagues indicated that the incidence of new persistent opioid use in patients who underwent surgery was 5.9% to 6.5% and did not differ between major and minor surgical procedures. This study concluded that new opioid use could be considered one of the most common complications after elective surgery.¹¹ Similarly, in 2017 Makary and colleagues found that surgeons tend to overprescribe pain medications after procedures; some prescribing as many as 50 to 60 tablets to control pain after simple procedures.¹² This is in stark contrast to pain guideline recommendations of no more than 10 tablets for most standard operative procedures.¹³

Sun and colleagues conducted a retrospective analysis of health care claims data in more than 18 million opioid-naïve patients who did and did not undergo surgery. Seven of the 11 surgical procedures were associated with an increased risk of chronic opioid use. The highest incidence of chronic opioid use in the first postoperative year was for total hip arthroplasty (1.4%, OR 5.10; 95% CI, 1.29-1.53). The study found that the risk factors most associated with chronic opioid use after surgery were male sex, aged > 50 years, and preoperative history of drug abuse, alcohol abuse, or depression, along with benzodiazepine use or antidepressant use.¹⁴ In a 2018 cohort study that evaluated predictors associated with transitioning to incident chronic opioid therapy, 4 factors were identified. These included opioid duration of action (adjusted odds ratio [AOR], 12.28; 95% CI, 8.1-06-18.72), the parent opioid compound (eg, tramadol vs codeine; AOR, 7.26; 95% CI, 5.20-10.13), the presence of conditions that are very likely to cause chronic pain (AOR, 5.47; 95% CI, 3.89-7.68), and drug use disorders (AOR, 4.02; 95% CI, 2.53-6.40).¹⁵

While there has been research into outpatient risk factors and medical practices that may contribute to chronic opioid use, a relative paucity of data exists on the contribution of hospitalization and inpatient opioid use on patient outcomes. A 2014 Canadian study assessed the impact of opioid use in the intensive care unit (ICU) on opioid use after discharge.¹⁶ This study included more than 2,500 patients who were admitted to a Canadian ICU between 2005 and 2008, and then followed after discharge for 48 months to quantify chronic opioid use. Nonopioid users increased from 87.8% in the early post-ICU period to 95.6% at 48 months after discharge. Preadmission chronic opioid use and prolonged hospital length of stay (LOS) were found to be associated with an increased risk of chronic opioid use after discharge.¹⁶ To date, there are no published studies that analyze the incidence of opioid-naïve veterans who convert to chronic opioid use after receiving opioids during an acute hospitalization.

In this retrospective analysis, we analyze the incidence of chronic opioid use after administration of opioids in the ICU as well as a variety of risk factors that may influence conversion to chronic opioid use.

 

Methods

This analysis was a single center, retrospective chart review conducted for patients admitted between July 1, 2017 and December 31, 2017 at the US Department of Veterans Affairs (VA) Michael E. DeBakey VA Medical Center (MEDVAMC) in Houston, Texas. MEDVAMC is a 538-bed academic\teaching hospital serving about 130,000 veterans in Southeast Texas. The hospital has 3 ICUs (medical, cardiovascular, and surgical) and 38 total ICU beds. The study was approved by the Baylor College of Medicine Institutional Review Board and MEDVAMC Research and Development Review Board. Informed consent was not required.

Inclusion criteria consisted of patients aged ≥ 18 years admitted to the ICU in the above-specified time frame, who were administered a continuous infusion of an opioid for at least 12 hours. Patients were excluded if they were not opioid naïve prior to admission, defined as receiving > 30 days of opioids in the prior 12 months. Patients who died during hospital admission, never received an opioid despite having an active order, were hospital-to-hospital transfers, or were still admitted at the time of data collection were excluded from the analysis.

All pertinent data were collected using the VA Computerized Patient Record System (CPRS) and the Critical Care Manager (Picis Clinical Solutions) ICU monitoring application. Critical Care Manager was used to track the time frame, duration, and amounts of opioid infusions administered in the ICU. Patient demographic and preadmission data, including date of birth, age, race, history of substance use/alcohol use disorder (defined as a previous diagnosis) and previous opioid prescriptions within the past year were recorded. For the inpatient admission, the ICU LOS, hospital LOS, primary admission diagnosis, type of opioid medication administered, and total duration and dose of opioid administered were collected. After discharge, opioid medication fill data at 3, 6, and 12 months were collected. This information included names of any outpatient opioids filled, dosage unit, quantity, day supply, and number of refills.

The primary outcome for this study was to determine the incidence of chronic opioid use at 3, 6, and 12 months after discharge, defined as the percentage of patients receiving outpatient opioid prescriptions at each time point. Analyses were conducted to observe the effect of age, race, history of substance use or history of alcohol use (International Classification of Diseases documented diagnosis, 9th edition), ICU type (medical, surgical, or cardiovascular), surgical/nonsurgical admission, ICU LOS, hospital LOS, total time, and amount of opioids administered during admission on risk of conversion to chronic opioid use.

Descriptive statistics were calculated to analyze the incidence of chronic opioid use. Univariate logistic regression analysis, including ORs, 95% CIs, and P values, was conducted to determine the effects of the risk factors noted earlier on chronic opioid use at each time point. A multivariate logistic regression model was performed to assess the effect of multiple independent variables on opioid use at 3, 6, and 12 months. Statistical analysis was performed using StataCorp Stata SE.

Results

During the study period, 330 patients were admitted to the ICU. After applying inclusion/exclusion criteria, 118 patients were included in the final analysis. The most frequent reasons for exclusion from the study were patient death (n = 77), a past history of opioid use (n = 56), and not having received an opioid infusion for at least 12 hours (n = 68). The average age of the patients included was 67 years (Table 1). A total of 14% and 9% of patients, respectively, had a diagnosis of alcohol use disorder or substance use disorder recorded in CPRS. After admission, the most common location for these patients was the surgical ICU (65%). All patients were male. The average hospital LOS was 18.6 days , and the ICU LOS was 8.3 days. The average duration of administration for the opioid (fentanyl) infusion was 63 hours, and the average amount of fentanyl administered to each patient was 57.1 mcg/h.

 

The incidence of opioid-naïve patients receiving opioids after discharge was 76.3% (n = 90) at 3 months, 19.5% (n = 23) at 6 months and 7.6% (n = 9) at 12 months (Figure). The daily morphine milligram equivalent (MME) of patients prescribed opioids at 3, 6, and 12 months was similar (3 months, 22.7; 6 months, 19.7; 12 months, 20.9). In the univariate regression analysis, several variables were found to be associated with converting to chronic opioid use. Prior history of alcohol use disorder (OR, 0.3; 95% CI, 0.10-0.88; P = .03), ICU type (OR, 3.9; 95% CI, 1.73-8.75; P = .001) and ICU LOS (OR, 0.88; 95% CI, 0.81-0.95; P = .01) had a statistically significant association on opioid use at 3 months. (Table 2). No variables evaluated had a statistically significant effect on chronic opioid use at 6 months, and only age (OR 0.93; 95% CI. 0.87-0.99; P = .02) was statistically significant at 12 months. In the multivariate logistic regression analysis, history of alcohol abuse, admission for surgery, and hospital LOS were significant at 3 months (Table 3).

Discussion

In this single-center analysis conducted at a VA academic hospital of opioid-naïve patients who were administered opioids in the ICU, the incidence of patients subsequently receiving outpatient opioid prescriptions at 12 months after discharge was 7.6%. There also was a decrease in the amount of opioids received by patients (daily MME) after discharge at 3, 6, and 12 months. This trend did not demonstrate the propensity for inpatient opioid use to convert opioid-naïve patients to chronic opioid users.

  

The most common outpatient opioids prescribed were hydrocodone/acetaminophen, morphine, and tramadol. Logistic regression showed few factors that correlated significantly with opioid use in the long-term (12 month) period. This finding is a deviation from the findings of Yaffe and colleagues who found hospital LOS to be one of the only predictors of long-term opioid use in their population (defined as use at 48 months).¹⁶ One important difference between our study and the Yaffe and colleagues study was that they evaluated all patients who were admitted to the ICU, regardless of the exposure to opioids during their inpatient stay. Consequently, this difference may have resulted in the differences in findings.

Strengths and Limitations

Location was a strength of our study, as this analysis was conducted at an integrated health care system that provides comprehensive inpatient and outpatient care. The VA uses a closed electronic health record, which allowed patients to be followed both in the inpatient and outpatient settings to determine which medications were prescribed at each time. In other health care systems this information would have been difficult to follow as patients often fill outpatient prescriptions at community pharmacies not affiliated with the treating hospital. However, any patient not using a VA prescriber for subsequent opioid prescriptions or patients who received opioids through other sources would not have had their continued opioid use captured. These data may be available in the states prescription monitoring program, but it was not available to query for research at this time.

 

This study also excluded chronic opioid users, which could have been another confounding factor to account for when analyzing the results. However, the primary objective of the study was to determine the impact of opioids prescribed in the ICU on converting previous opioid-naïve patients to chronic users. Finally, a multivariate logistic regression was incorporated to assess for factors that may predispose certain patients to convert to chronic opioid users. This analysis served to extend the applicability of our study by not only analyzing whether receiving opioids in the ICU contributed to chronic opioid use in the long-term, but also which populations may be at greatest risk. This information can be used in the future to target harm-reduction efforts toward high-risk hospitalized patients.

One limitation of this study was that it was conducted as a retrospective, single-center chart review in Houston, Texas. Because this was not a randomized controlled trial, it is difficult to imply any causation between exposure to opioids in the ICU and chronic use. In addition, because this study was conducted at a single site, the results may not be able to be generalized to other populations. VA populations tend to be elderly and predominantly male, as was reflected by the study population. These factors, along with regional variability in patient characteristics, may limit the generalizability of this study to older male patients located in Southeast Texas or other similar populations. Other limitations of this study also included the small sample size, limited period of follow-up obtained, and that other comorbidity information (pain scores during stay, use of nonopioid pain medications, past history of anxiety or depression, or other acute illnesses or surgeries that may have required opioid therapy during admission) was not collected.

This study was only able to review 118 patients for a follow-up duration of 1 year. In the Yaffe and colleagues study, more than 2,500 patients were followed over 4 years, which provided a more long-term overview of the clinical course of these patients and may be another reason for discrepant findings. However, this study did not actually assess the impact on administration of opioids on the development of chronic opioid use.¹⁶ Finally, the biggest limitation to this study may be the potential for confounding discharge prescriptions. After discharge, patients’ prescriptions were evaluated from discharge to 3 months, in between 3 and 6 months, and between 6 and 12 months for the presence of an opioid prescription. Due to this methodology, any opioid prescription a patient was discharged with was counted in the 3-month time point. Since many patients included in the study were admitted to the surgical ICU (65%), it was logical that they were discharged with opioids after their procedure. While including the immediate postdischarge prescription data was useful for evaluating the decrease in opioid use and incidence over time, it did cause the 3-month time point to appear overly inflated, potentially signaling that at 3 months after discharge many of these patients were still requiring opioid use.

The Society of Critical Care Medicine still recommends opioids as first-line therapy for non-neuropathic pain in the ICU setting.¹⁷ Additionally, postoperative pain can be difficult to manage in the surgical population and is often treated with opioids, though treatment with multimodal pain regimens is becoming more common.¹⁸ It is difficult to imagine that a finding that implicates opioid use in the hospital with conversion to chronic opioid use would prompt a cessation in the use of opioid in these settings, especially in the context of analgosedation related to mechanically ventilated patients. However, it would be plausible to use this knowledge to advocate for opioid-sparing therapies and consideration for weaning individuals at high risk for misuse after discharge from opioid-containing sedation or analgesia regimens in a timelier manner.

Though our findings did not show a clinically relevant increase in chronic opioid users, clinicians can still use this information to encourage targeted education and closer monitoring for those patients deemed as high risk at discharge to prevent unnecessary prolonged opioid use. By having more frequent follow-up in pain clinics, switching patients to nonopioid therapies after discharge, and ensuring high-risk patients are discharged with naloxone rescue kits, it would be possible to drastically reduce the number of potential overdoses for patients who previously required opioid therapy in the ICU.

 

Conclusion

After discharge, 7.6% of previously opioid-naïve patients who were treated with opioids in the ICU were still receiving prescriptions for opioids at 12 months. These findings did not suggest a clinically significant increase in the incidence of chronic opioid use after inpatient administration of opioids. However, these results prompt the need for larger, prospective, multicenter studies to evaluate the effect on hospitalization on converting to chronic opioid use and a deeper evaluation of other potential risk factors that may be present.

Chronic pain is a worldwide cause of impairment. According to data from the 2016 National Health Interview Survey, an estimated 50 million American adults suffer from chronic pain, with 19.6 million adults suffering from high-impact chronic pain.¹ This phenomenon is particularly prevalent in the older population. More than 25% of adults aged 65 to 74 years reported that they were often in pain in the past 3 months compared with just 10% of those adults between the ages of 18 and 44 years.²

The economic burdens of chronic pain disorders are well known. In 2010, Gaskin and Richard found that chronic pain has far-reaching consequences for the US economy, ranging from direct health care costs to lost productivity. This study estimated additional health care costs at about $300 billion yearly and lost productivity at $300 billion, bringing total annual costs to about $600 billion. This expense is more than heart disease alone ($309 billion), and cancer and diabetes mellitus ($243 billion and $188 billion respectively) combined.³

Opioid medications are powerful and effective pain-reducing agents that are indicated for short-term acute pain or long-term in the management of chronic, severe cancer-related pain.⁴ Although efficacious, use of these medications carries with it the inherent risks of abuse, misuse, addiction, and overdose.⁵ Since 1999, opioid-related overdose deaths have been on the rise. The CDC estimated that > 15,000 deaths were attributable specifically to prescription opioids in 2015.⁶ The estimates had risen to > 17,000 deaths in 2017, with the number increasing since that time.⁷ Cumulatively, the CDC estimates that > 200,000 deaths in the US between 1999 and 2017 are attributed to prescription opioid overdose, clearly marking this trend as a growing nationwide epidemic.⁸

In 2016, Florence and colleagues estimated costs associated with opioid overdose to be just shy of $80 billion in 2013 dollars.⁹ In October 2017, the US Department of Health and Human Services declared the opioid epidemic a public health emergency and committed $900 million to combating the crisis.¹⁰

An abundance of data exist analyzing outpatient prescribing and its impacts on opioid dependence, particularly postoperatively. A study by Brummett and colleagues indicated that the incidence of new persistent opioid use in patients who underwent surgery was 5.9% to 6.5% and did not differ between major and minor surgical procedures. This study concluded that new opioid use could be considered one of the most common complications after elective surgery.¹¹ Similarly, in 2017 Makary and colleagues found that surgeons tend to overprescribe pain medications after procedures; some prescribing as many as 50 to 60 tablets to control pain after simple procedures.¹² This is in stark contrast to pain guideline recommendations of no more than 10 tablets for most standard operative procedures.¹³

Sun and colleagues conducted a retrospective analysis of health care claims data in more than 18 million opioid-naïve patients who did and did not undergo surgery. Seven of the 11 surgical procedures were associated with an increased risk of chronic opioid use. The highest incidence of chronic opioid use in the first postoperative year was for total hip arthroplasty (1.4%, OR 5.10; 95% CI, 1.29-1.53). The study found that the risk factors most associated with chronic opioid use after surgery were male sex, aged > 50 years, and preoperative history of drug abuse, alcohol abuse, or depression, along with benzodiazepine use or antidepressant use.¹⁴ In a 2018 cohort study that evaluated predictors associated with transitioning to incident chronic opioid therapy, 4 factors were identified. These included opioid duration of action (adjusted odds ratio [AOR], 12.28; 95% CI, 8.1-06-18.72), the parent opioid compound (eg, tramadol vs codeine; AOR, 7.26; 95% CI, 5.20-10.13), the presence of conditions that are very likely to cause chronic pain (AOR, 5.47; 95% CI, 3.89-7.68), and drug use disorders (AOR, 4.02; 95% CI, 2.53-6.40).¹⁵

While there has been research into outpatient risk factors and medical practices that may contribute to chronic opioid use, a relative paucity of data exists on the contribution of hospitalization and inpatient opioid use on patient outcomes. A 2014 Canadian study assessed the impact of opioid use in the intensive care unit (ICU) on opioid use after discharge.¹⁶ This study included more than 2,500 patients who were admitted to a Canadian ICU between 2005 and 2008, and then followed after discharge for 48 months to quantify chronic opioid use. Nonopioid users increased from 87.8% in the early post-ICU period to 95.6% at 48 months after discharge. Preadmission chronic opioid use and prolonged hospital length of stay (LOS) were found to be associated with an increased risk of chronic opioid use after discharge.¹⁶ To date, there are no published studies that analyze the incidence of opioid-naïve veterans who convert to chronic opioid use after receiving opioids during an acute hospitalization.

In this retrospective analysis, we analyze the incidence of chronic opioid use after administration of opioids in the ICU as well as a variety of risk factors that may influence conversion to chronic opioid use.

 

Methods

This analysis was a single center, retrospective chart review conducted for patients admitted between July 1, 2017 and December 31, 2017 at the US Department of Veterans Affairs (VA) Michael E. DeBakey VA Medical Center (MEDVAMC) in Houston, Texas. MEDVAMC is a 538-bed academic\teaching hospital serving about 130,000 veterans in Southeast Texas. The hospital has 3 ICUs (medical, cardiovascular, and surgical) and 38 total ICU beds. The study was approved by the Baylor College of Medicine Institutional Review Board and MEDVAMC Research and Development Review Board. Informed consent was not required.

Inclusion criteria consisted of patients aged ≥ 18 years admitted to the ICU in the above-specified time frame, who were administered a continuous infusion of an opioid for at least 12 hours. Patients were excluded if they were not opioid naïve prior to admission, defined as receiving > 30 days of opioids in the prior 12 months. Patients who died during hospital admission, never received an opioid despite having an active order, were hospital-to-hospital transfers, or were still admitted at the time of data collection were excluded from the analysis.

All pertinent data were collected using the VA Computerized Patient Record System (CPRS) and the Critical Care Manager (Picis Clinical Solutions) ICU monitoring application. Critical Care Manager was used to track the time frame, duration, and amounts of opioid infusions administered in the ICU. Patient demographic and preadmission data, including date of birth, age, race, history of substance use/alcohol use disorder (defined as a previous diagnosis) and previous opioid prescriptions within the past year were recorded. For the inpatient admission, the ICU LOS, hospital LOS, primary admission diagnosis, type of opioid medication administered, and total duration and dose of opioid administered were collected. After discharge, opioid medication fill data at 3, 6, and 12 months were collected. This information included names of any outpatient opioids filled, dosage unit, quantity, day supply, and number of refills.

The primary outcome for this study was to determine the incidence of chronic opioid use at 3, 6, and 12 months after discharge, defined as the percentage of patients receiving outpatient opioid prescriptions at each time point. Analyses were conducted to observe the effect of age, race, history of substance use or history of alcohol use (International Classification of Diseases documented diagnosis, 9th edition), ICU type (medical, surgical, or cardiovascular), surgical/nonsurgical admission, ICU LOS, hospital LOS, total time, and amount of opioids administered during admission on risk of conversion to chronic opioid use.

Descriptive statistics were calculated to analyze the incidence of chronic opioid use. Univariate logistic regression analysis, including ORs, 95% CIs, and P values, was conducted to determine the effects of the risk factors noted earlier on chronic opioid use at each time point. A multivariate logistic regression model was performed to assess the effect of multiple independent variables on opioid use at 3, 6, and 12 months. Statistical analysis was performed using StataCorp Stata SE.

Results

During the study period, 330 patients were admitted to the ICU. After applying inclusion/exclusion criteria, 118 patients were included in the final analysis. The most frequent reasons for exclusion from the study were patient death (n = 77), a past history of opioid use (n = 56), and not having received an opioid infusion for at least 12 hours (n = 68). The average age of the patients included was 67 years (Table 1). A total of 14% and 9% of patients, respectively, had a diagnosis of alcohol use disorder or substance use disorder recorded in CPRS. After admission, the most common location for these patients was the surgical ICU (65%). All patients were male. The average hospital LOS was 18.6 days , and the ICU LOS was 8.3 days. The average duration of administration for the opioid (fentanyl) infusion was 63 hours, and the average amount of fentanyl administered to each patient was 57.1 mcg/h.

 

The incidence of opioid-naïve patients receiving opioids after discharge was 76.3% (n = 90) at 3 months, 19.5% (n = 23) at 6 months and 7.6% (n = 9) at 12 months (Figure). The daily morphine milligram equivalent (MME) of patients prescribed opioids at 3, 6, and 12 months was similar (3 months, 22.7; 6 months, 19.7; 12 months, 20.9). In the univariate regression analysis, several variables were found to be associated with converting to chronic opioid use. Prior history of alcohol use disorder (OR, 0.3; 95% CI, 0.10-0.88; P = .03), ICU type (OR, 3.9; 95% CI, 1.73-8.75; P = .001) and ICU LOS (OR, 0.88; 95% CI, 0.81-0.95; P = .01) had a statistically significant association on opioid use at 3 months. (Table 2). No variables evaluated had a statistically significant effect on chronic opioid use at 6 months, and only age (OR 0.93; 95% CI. 0.87-0.99; P = .02) was statistically significant at 12 months. In the multivariate logistic regression analysis, history of alcohol abuse, admission for surgery, and hospital LOS were significant at 3 months (Table 3).

Discussion

In this single-center analysis conducted at a VA academic hospital of opioid-naïve patients who were administered opioids in the ICU, the incidence of patients subsequently receiving outpatient opioid prescriptions at 12 months after discharge was 7.6%. There also was a decrease in the amount of opioids received by patients (daily MME) after discharge at 3, 6, and 12 months. This trend did not demonstrate the propensity for inpatient opioid use to convert opioid-naïve patients to chronic opioid users.

  

The most common outpatient opioids prescribed were hydrocodone/acetaminophen, morphine, and tramadol. Logistic regression showed few factors that correlated significantly with opioid use in the long-term (12 month) period. This finding is a deviation from the findings of Yaffe and colleagues who found hospital LOS to be one of the only predictors of long-term opioid use in their population (defined as use at 48 months).¹⁶ One important difference between our study and the Yaffe and colleagues study was that they evaluated all patients who were admitted to the ICU, regardless of the exposure to opioids during their inpatient stay. Consequently, this difference may have resulted in the differences in findings.

Strengths and Limitations

Location was a strength of our study, as this analysis was conducted at an integrated health care system that provides comprehensive inpatient and outpatient care. The VA uses a closed electronic health record, which allowed patients to be followed both in the inpatient and outpatient settings to determine which medications were prescribed at each time. In other health care systems this information would have been difficult to follow as patients often fill outpatient prescriptions at community pharmacies not affiliated with the treating hospital. However, any patient not using a VA prescriber for subsequent opioid prescriptions or patients who received opioids through other sources would not have had their continued opioid use captured. These data may be available in the states prescription monitoring program, but it was not available to query for research at this time.

 

This study also excluded chronic opioid users, which could have been another confounding factor to account for when analyzing the results. However, the primary objective of the study was to determine the impact of opioids prescribed in the ICU on converting previous opioid-naïve patients to chronic users. Finally, a multivariate logistic regression was incorporated to assess for factors that may predispose certain patients to convert to chronic opioid users. This analysis served to extend the applicability of our study by not only analyzing whether receiving opioids in the ICU contributed to chronic opioid use in the long-term, but also which populations may be at greatest risk. This information can be used in the future to target harm-reduction efforts toward high-risk hospitalized patients.

One limitation of this study was that it was conducted as a retrospective, single-center chart review in Houston, Texas. Because this was not a randomized controlled trial, it is difficult to imply any causation between exposure to opioids in the ICU and chronic use. In addition, because this study was conducted at a single site, the results may not be able to be generalized to other populations. VA populations tend to be elderly and predominantly male, as was reflected by the study population. These factors, along with regional variability in patient characteristics, may limit the generalizability of this study to older male patients located in Southeast Texas or other similar populations. Other limitations of this study also included the small sample size, limited period of follow-up obtained, and that other comorbidity information (pain scores during stay, use of nonopioid pain medications, past history of anxiety or depression, or other acute illnesses or surgeries that may have required opioid therapy during admission) was not collected.

This study was only able to review 118 patients for a follow-up duration of 1 year. In the Yaffe and colleagues study, more than 2,500 patients were followed over 4 years, which provided a more long-term overview of the clinical course of these patients and may be another reason for discrepant findings. However, this study did not actually assess the impact on administration of opioids on the development of chronic opioid use.¹⁶ Finally, the biggest limitation to this study may be the potential for confounding discharge prescriptions. After discharge, patients’ prescriptions were evaluated from discharge to 3 months, in between 3 and 6 months, and between 6 and 12 months for the presence of an opioid prescription. Due to this methodology, any opioid prescription a patient was discharged with was counted in the 3-month time point. Since many patients included in the study were admitted to the surgical ICU (65%), it was logical that they were discharged with opioids after their procedure. While including the immediate postdischarge prescription data was useful for evaluating the decrease in opioid use and incidence over time, it did cause the 3-month time point to appear overly inflated, potentially signaling that at 3 months after discharge many of these patients were still requiring opioid use.

The Society of Critical Care Medicine still recommends opioids as first-line therapy for non-neuropathic pain in the ICU setting.¹⁷ Additionally, postoperative pain can be difficult to manage in the surgical population and is often treated with opioids, though treatment with multimodal pain regimens is becoming more common.¹⁸ It is difficult to imagine that a finding that implicates opioid use in the hospital with conversion to chronic opioid use would prompt a cessation in the use of opioid in these settings, especially in the context of analgosedation related to mechanically ventilated patients. However, it would be plausible to use this knowledge to advocate for opioid-sparing therapies and consideration for weaning individuals at high risk for misuse after discharge from opioid-containing sedation or analgesia regimens in a timelier manner.

Though our findings did not show a clinically relevant increase in chronic opioid users, clinicians can still use this information to encourage targeted education and closer monitoring for those patients deemed as high risk at discharge to prevent unnecessary prolonged opioid use. By having more frequent follow-up in pain clinics, switching patients to nonopioid therapies after discharge, and ensuring high-risk patients are discharged with naloxone rescue kits, it would be possible to drastically reduce the number of potential overdoses for patients who previously required opioid therapy in the ICU.

 

Conclusion

After discharge, 7.6% of previously opioid-naïve patients who were treated with opioids in the ICU were still receiving prescriptions for opioids at 12 months. These findings did not suggest a clinically significant increase in the incidence of chronic opioid use after inpatient administration of opioids. However, these results prompt the need for larger, prospective, multicenter studies to evaluate the effect on hospitalization on converting to chronic opioid use and a deeper evaluation of other potential risk factors that may be present.

Interactions between industry and physicians, including dermatologists, are widely prevalent.^1-3 Proper reporting of industry relationships is essential for transparency, objectivity, and management of potential biases and conflicts of interest. There has been increasing public scrutiny regarding these interactions.

The Physician Payments Sunshine Act established Open Payments (OP), a publicly available database that collects and displays industry-reported physician-industry interactions.^4,5 For the medical community and public, the OP database may be used to assess transparency by comparing the data with physician self-disclosures. There is a paucity of studies in the literature examining the concordance of industry-reported disclosures and physician self-reported data, with even fewer studies utilizing OP as a source of industry disclosures, and none exists for dermatology.^6-12 It also is not clear to what extent the OP database captures all possible dermatologist-industry interactions, as the Sunshine Act only mandates reporting by applicable US-based manufacturers and group purchasing organizations that produce or purchase drugs or devices that require a prescription and are reimbursable by a government-run health care program.⁵ As a result, certain companies, such as cosmeceuticals, may not be represented.

In this study we aimed to evaluate the concordance of dermatologist self-disclosure of industry relationships and those reported on OP. Specifically, we focused on interactions disclosed by presenters at the American Academy of Dermatology (AAD) 73rd Annual Meeting in San Francisco, California (March 20–24, 2015), and those by industry in the 2014 OP database.

Methods

In this retrospective cohort study, we compared publicly available data from the OP database to presenter disclosures found in the publicly available AAD 73rd Annual Meeting program (AADMP). The AAD required speakers to disclose financial relationships with industry within the 12 months preceding the presentation, as outlined in the Accreditation Council for Continuing Medical Education guidelines.¹³ All AAD presenters who were dermatologists practicing in the United States were included in the analysis, whereas residents, fellows, nonphysicians, nondermatologist physicians, and international dermatologists were excluded.

We examined general, research, and associated research payments to specific dermatologists using the 2014 OP data, which contained industry payments made between January 1 and December 31, 2014. Open Payments defined research payments as direct payment to the physician for different types of research activities and associated research payments as indirect payments made to a research institution or entity where the physician was named the principal investigator.⁵ We chose the 2014 database because it most closely matched the period of required disclosures defined by the AAD for the 2015 meeting. Our review of the OP data occurred after the June 2016 update and thus included the most accurate and up-to-date financial interactions.

We conducted our analysis in 2 major steps. First, we determined whether each industry interaction reported in the OP database was present in the AADMP, which provided an assessment of interaction-level concordance. Second, we determined whether all the industry interactions for any given dermatologist listed in the OP also were present in AADMP, which provided an assessment of dermatologist-level concordance.

First, to establish interaction-level concordance for each industry interaction, the company name and the type of interaction (eg, consultant, speaker, investigator) listed in the AADMP were compared with the data in OP to verify a match. Each interaction was assigned into one of the categories of concordant disclosure (a match of both the company name and type of interaction details in OP and the AADMP), overdisclosure (the presence of an AADMP interaction not found in OP, such as an additional type of interaction or company), or underdisclosure (a company name or type of interaction found in OP but not reported in the AADMP). For underdisclosure, we further classified into company present or company absent based on whether the dermatologist disclosed any relationship with a particular company in the AADMP. We considered the type of interaction to be matching if they were identical or similar in nature (eg, consulting in OP and advisory board in the AADMP), as the types of interactions are reported differently in OP and the AADMP. Otherwise, if they were not similar enough (eg, education in OP and stockholder in the AADMP), it was classified as underdisclosure. Some types of interactions reported in OP were not available on the AAD disclosure form. For example, food and beverage as well as travel and lodging were types of interactions in OP that did not exist in the AADMP. These 2 types of interactions comprised a large majority of OP payment entries but only accounted for a small percentage of the payment amount. Analysis was performed both including and excluding interactions for food, beverage, travel, and lodging (f/b/t/l) to best account for differences in interaction categories between OP and the AADMP.

 

Second, each dermatologist was assigned to an overall disclosure category of dermatologist-level concordance based on the status for all his/her interactions. Categories included no disclosure (no industry interactions in OP and the AADMP), concordant (all industry interactions reported in OP and the AADMP match), overdisclosure only (no industry interactions on OP but self-reported interactions present in the AADMP), and discordant (not all OP interactions were disclosed in the AADMP). The discordant category was further divided into with overdisclosure and without overdisclosure, depending on the presence or absence of industry relationships listed in the AADMP but not in OP, respectively.

To ensure uniformity, one individual (A.F.S.) reviewed and collected the data from OP and the AADMP. Information on gender and academic affiliation of study participants was obtained from information listed in the AADMP and Google searches. Data management was performed with Microsoft Excel software (Microsoft Excel 2010, Version 14.0, Microsoft Corporation). The New York University School of Medicine’s (New York, New York) institutional review board exempted this study.

Results

Of the 938 presenters listed in the AADMP, 768 individuals met the inclusion criteria. The most commonly cited type of relationship with industry listed in the AADMP was serving as an investigator, consultant, or advisory board member, comprising 34%, 26%, and 18%, respectively (Table 1). The forms of payment most frequently reported in the AADMP were honoraria and grants/research funding, comprising 49% and 25%, respectively (Table 2).

In 2014, there were a total of 20,761 industry payments totaling $35,627,365 for general, research, and associated research payments in the OP database related to the dermatologists who met inclusion criteria. There were 8678 payments totaling $466,622 for food and beverage and 3238 payments totaling $1,357,770 for travel and lodging. After excluding payments for f/b/t/l, there were 8845 payments totaling $33,802,973, with highest percentages of payment amounts for associated research (67.1%), consulting fees (11.5%), research (7.9%), and speaker fees (7.2%)(Table 3). For presenters with industry payments, the range of disbursements excluding f/b/t/l was $6.52 to $1,933,705, with a mean (standard deviation) of $107,997 ($249,941), a median of $18,247, and an interquartile range of $3422 to $97,375 (data not shown).

In assessing interaction-level concordance, 63% of all payment amounts in OP were classified as concordant disclosures. Regarding the number of OP payments, 27% were concordant disclosures, 34% were underdisclosures due to f/b/t/l payments, and 39% were underdisclosures due to non–f/b/t/l payments. When f/b/t/l payment entries in OP were excluded, the status of concordant disclosure for the amount and number of OP payments increased to 66% ($22,242,638) and 63% (5549), respectively. The percentage of payment entries with concordant disclosure status ranged from 43% to 71% depending on the payment amount. Payment entries at both ends of the spectrum had the lowest concordant disclosure rates, with 43% for payment entries between $0.01 and $100 and 58% for entries greater than $100,000 (Table 4). The concordance status also differed by the type of interactions. None of the OP payments for gift and royalty or license were disclosed in the AADMP, as there were no suitable corresponding categories. The proportion of payments with concordant disclosure for honoraria (45%), education (48%), and associated research (61%) was lower than the proportion of payments with concordant disclosure for research (90%), speaker fees (75%–79%), and consulting fees (74%)(Table 5).

In assessing dermatologist-level concordance including all OP entries, the number of dermatologists with no disclosure, overdisclosure only, concordant disclosure, discordant with overdisclosure, and discordant without overdisclosure statuses were 234 (30%), 70 (9%), 9 (1%), 251 (33%), and 204 (27%), respectively. When f/b/t/l entries were excluded, those figures changed to 347 (45%), 108 (14%), 79 (10%), 157 (20%), and 77 (10%), respectively. The characteristics of these dermatologists and their associated industry interactions by disclosure status are shown in the eTable. Dermatologists in the discordant with overdisclosure group had the highest median number and amount of OP payments, followed by those in the concordant disclosure and discordant without overdisclosure groups. Additionally, discordant with overdisclosure dermatologists also had the highest median and mean number of unique industry interactions not on OP, followed by those in the overdisclosure only and no disclosure groups. Academic and private practice settings did not impact dermatologists’ disclosure status. The percentage of female and male dermatologists in the discordant group was 25% and 36%, respectively.

Dermatologists reported a total of 1756 unique industry relationships in the AADMP that were not found on OP. Of these, 1440 (82%) relationships were from 236 dermatologists who had industry payments on OP. The remaining 316 relationships were from 108 dermatologists who had no payments on OP. Although 114 companies reported payments to dermatologists on OP, dermatologists in the AADMP reported interactions with an additional 430 companies.

 

Comment

In this study, we demonstrated discordance between dermatologist self-reported financial interactions in the AADMP compared with those reported by industry via OP. After excluding f/b/t/l entries, approximately two-thirds of the total amount and number of payments in OP were disclosed, while 31% of dermatologists had discordant disclosure status.

Prior investigations in other medical fields showed high discrepancy rates between industry-reported and physician-reported relationships ranging from 23% to 62%, with studies utilizing various methodologies.^{6-9,11,12,14,15} Only a few studies have utilized the OP database.^8,12,15 Thompson et al¹² compared OP payment data with physician financial disclosure at an annual gynecology scientific meeting and found although 209 of 335 (62%) physicians had interactions listed in the OP database, only 24 (7%) listed at least 1 company in the meeting financial disclosure section. Of these 24 physicians, only 5 (21%) accurately disclosed financial relationships with all of the companies listed in OP. The investigators found 129 (38.5%) physicians and 33.7% of the $1.99 million OP payments had concordant disclosure status. When they excluded physicians who received less than $100, 53% of individuals had concordant disclosure.¹² Hannon et al⁸ reported on inconsistencies between disclosures in the OP database and the American Academy of Orthopedic Surgeons Annual Meeting and found 259 (23%) of 1113 physicians meeting inclusion criteria had financial interactions listed in the OP database that were not reported in the meeting disclosures. Yee et al¹⁵ also utilized the OP database and compared it with author disclosures in 3 major ophthalmology journals.Of 670 authors, 367 (54.8%) had complete concordance, with 68 (10.1%) more reporting additional overdisclosures, leading to a discordant with underdisclosure rate of 35.1%. Additionally, $1.46 million (44.6%) of the $3.27 million OP payments had concordant disclosure status.¹⁵ Other studies compared individual companies’ online reports of physician payments with physician self-disclosures in annual meeting programs, clinical guidelines, and peer-reviewed publications.^6,7,9,11,14

Our study demonstrated variation in disclosure status. Compared with other groups, dermatologists in the discordant with overdisclosure group on average had more interactions with and received higher payments from industry, which is consistent with studies in the orthopedic surgery literature.^8,9 Male dermatologists had 11% more discordant disclosures than their female counterparts, which may be influenced by men having more industry interactions than women.³ Although small industry payments possessed the lowest concordant rate in our study, which has been observed,¹² payments greater than $100,000 had the second-lowest concordance rate at 58%, which may be skewed by the small sample size. Rates of concordant disclosure differed among types of interactions, such as between research and associated research payments. This particular difference may be attributed to the incorrect listing of dermatologists as principal investigators or reduced awareness of payments, as associated research payments were made to the institution and not the individual.

Reasons for discrepancies between industry-reported and dermatologist-reported disclosures may include reporting time differences, lack of physician awareness of OP, industry reporting inaccuracies, dearth of contextual information associated with individual payment entries, and misunderstandings. Prior research demonstrated that the most common reasons for physician nondisclosure included misunderstanding disclosure requirements, unintentional omission of payment, and a lack of relationship between the industry payment and presentation topic.^9,12 These factors likely contributed to the disclosure inconsistencies in our study. Similarly high rates of inconsistencies across different specialties suggest systemic concerns.

We found a substantial number of dermatologist-industry interactions listed in the AADMP that were not captured by OP, with 108 dermatologists (35%) having overdisclosures even when excluding f/b/t/l entries. The number of companies in these overdisclosures approximated 4 times the number of companies on OP. Other studies have also observed physician-industry interactions not displayed on OP.^8,12,15 Because the Sunshine Act requires reporting only by certain companies, interactions surrounding products such as over-the-counter merchandise, cosmetics, lasers, novel devices, and new medications are generally not included. Further, OP may not capture nonmonetary industry relationships.

There were several limitations to this study. The most notable limitation was the differences in the categorizations of industry relationships by OP and the AADMP. These differences can overemphasize some types of interactions at the expense of other types, such as f/b/t/l. As such, analyses were repeated after excluding f/b/t/l. Another limitation was the inexact overlap of time frames for OP and the AADMP, which may have led to discrepancies. However, we used the best available data and expect the vast majority of interactions to have occurred by the AAD disclosure deadline. It is possible that the presenters may have had a more updated conflict-of-interest disclosure slide at the time of the meeting presentation. The most important limitation was that we were unable to determine whether discrepancies resulted from underreporting by dermatologists or inaccurate reporting by industry. It was unlikely that OP or the AADMP alone completely represented all dermatologist-industry financial relationships.

Conclusion

With a growing emphasis on physician-industry transparency, we identified rates of differences in dermatology consistent with those in other medical fields when comparing the publicly available OP database with disclosures at national conferences. Although the differences in the categorization and requirements for disclosure between the OP database and the AADMP may account for some of the discordance, dermatologists should be aware of potentially negative public perceptions regarding the transparency and prevalence of physician-industry interactions. Dermatologists should continue to disclose conflicts of interest as accurately as possible and review their industry-reported interactions listed in the OP database.



Acknowledgment
The first two authors contributed equally to this research/article.

Interactions between industry and physicians, including dermatologists, are widely prevalent.^1-3 Proper reporting of industry relationships is essential for transparency, objectivity, and management of potential biases and conflicts of interest. There has been increasing public scrutiny regarding these interactions.

The Physician Payments Sunshine Act established Open Payments (OP), a publicly available database that collects and displays industry-reported physician-industry interactions.^4,5 For the medical community and public, the OP database may be used to assess transparency by comparing the data with physician self-disclosures. There is a paucity of studies in the literature examining the concordance of industry-reported disclosures and physician self-reported data, with even fewer studies utilizing OP as a source of industry disclosures, and none exists for dermatology.^6-12 It also is not clear to what extent the OP database captures all possible dermatologist-industry interactions, as the Sunshine Act only mandates reporting by applicable US-based manufacturers and group purchasing organizations that produce or purchase drugs or devices that require a prescription and are reimbursable by a government-run health care program.⁵ As a result, certain companies, such as cosmeceuticals, may not be represented.

In this study we aimed to evaluate the concordance of dermatologist self-disclosure of industry relationships and those reported on OP. Specifically, we focused on interactions disclosed by presenters at the American Academy of Dermatology (AAD) 73rd Annual Meeting in San Francisco, California (March 20–24, 2015), and those by industry in the 2014 OP database.

Methods

In this retrospective cohort study, we compared publicly available data from the OP database to presenter disclosures found in the publicly available AAD 73rd Annual Meeting program (AADMP). The AAD required speakers to disclose financial relationships with industry within the 12 months preceding the presentation, as outlined in the Accreditation Council for Continuing Medical Education guidelines.¹³ All AAD presenters who were dermatologists practicing in the United States were included in the analysis, whereas residents, fellows, nonphysicians, nondermatologist physicians, and international dermatologists were excluded.

We examined general, research, and associated research payments to specific dermatologists using the 2014 OP data, which contained industry payments made between January 1 and December 31, 2014. Open Payments defined research payments as direct payment to the physician for different types of research activities and associated research payments as indirect payments made to a research institution or entity where the physician was named the principal investigator.⁵ We chose the 2014 database because it most closely matched the period of required disclosures defined by the AAD for the 2015 meeting. Our review of the OP data occurred after the June 2016 update and thus included the most accurate and up-to-date financial interactions.

We conducted our analysis in 2 major steps. First, we determined whether each industry interaction reported in the OP database was present in the AADMP, which provided an assessment of interaction-level concordance. Second, we determined whether all the industry interactions for any given dermatologist listed in the OP also were present in AADMP, which provided an assessment of dermatologist-level concordance.

First, to establish interaction-level concordance for each industry interaction, the company name and the type of interaction (eg, consultant, speaker, investigator) listed in the AADMP were compared with the data in OP to verify a match. Each interaction was assigned into one of the categories of concordant disclosure (a match of both the company name and type of interaction details in OP and the AADMP), overdisclosure (the presence of an AADMP interaction not found in OP, such as an additional type of interaction or company), or underdisclosure (a company name or type of interaction found in OP but not reported in the AADMP). For underdisclosure, we further classified into company present or company absent based on whether the dermatologist disclosed any relationship with a particular company in the AADMP. We considered the type of interaction to be matching if they were identical or similar in nature (eg, consulting in OP and advisory board in the AADMP), as the types of interactions are reported differently in OP and the AADMP. Otherwise, if they were not similar enough (eg, education in OP and stockholder in the AADMP), it was classified as underdisclosure. Some types of interactions reported in OP were not available on the AAD disclosure form. For example, food and beverage as well as travel and lodging were types of interactions in OP that did not exist in the AADMP. These 2 types of interactions comprised a large majority of OP payment entries but only accounted for a small percentage of the payment amount. Analysis was performed both including and excluding interactions for food, beverage, travel, and lodging (f/b/t/l) to best account for differences in interaction categories between OP and the AADMP.

 

Second, each dermatologist was assigned to an overall disclosure category of dermatologist-level concordance based on the status for all his/her interactions. Categories included no disclosure (no industry interactions in OP and the AADMP), concordant (all industry interactions reported in OP and the AADMP match), overdisclosure only (no industry interactions on OP but self-reported interactions present in the AADMP), and discordant (not all OP interactions were disclosed in the AADMP). The discordant category was further divided into with overdisclosure and without overdisclosure, depending on the presence or absence of industry relationships listed in the AADMP but not in OP, respectively.

To ensure uniformity, one individual (A.F.S.) reviewed and collected the data from OP and the AADMP. Information on gender and academic affiliation of study participants was obtained from information listed in the AADMP and Google searches. Data management was performed with Microsoft Excel software (Microsoft Excel 2010, Version 14.0, Microsoft Corporation). The New York University School of Medicine’s (New York, New York) institutional review board exempted this study.

Results

Of the 938 presenters listed in the AADMP, 768 individuals met the inclusion criteria. The most commonly cited type of relationship with industry listed in the AADMP was serving as an investigator, consultant, or advisory board member, comprising 34%, 26%, and 18%, respectively (Table 1). The forms of payment most frequently reported in the AADMP were honoraria and grants/research funding, comprising 49% and 25%, respectively (Table 2).

In 2014, there were a total of 20,761 industry payments totaling $35,627,365 for general, research, and associated research payments in the OP database related to the dermatologists who met inclusion criteria. There were 8678 payments totaling $466,622 for food and beverage and 3238 payments totaling $1,357,770 for travel and lodging. After excluding payments for f/b/t/l, there were 8845 payments totaling $33,802,973, with highest percentages of payment amounts for associated research (67.1%), consulting fees (11.5%), research (7.9%), and speaker fees (7.2%)(Table 3). For presenters with industry payments, the range of disbursements excluding f/b/t/l was $6.52 to $1,933,705, with a mean (standard deviation) of $107,997 ($249,941), a median of $18,247, and an interquartile range of $3422 to $97,375 (data not shown).

In assessing interaction-level concordance, 63% of all payment amounts in OP were classified as concordant disclosures. Regarding the number of OP payments, 27% were concordant disclosures, 34% were underdisclosures due to f/b/t/l payments, and 39% were underdisclosures due to non–f/b/t/l payments. When f/b/t/l payment entries in OP were excluded, the status of concordant disclosure for the amount and number of OP payments increased to 66% ($22,242,638) and 63% (5549), respectively. The percentage of payment entries with concordant disclosure status ranged from 43% to 71% depending on the payment amount. Payment entries at both ends of the spectrum had the lowest concordant disclosure rates, with 43% for payment entries between $0.01 and $100 and 58% for entries greater than $100,000 (Table 4). The concordance status also differed by the type of interactions. None of the OP payments for gift and royalty or license were disclosed in the AADMP, as there were no suitable corresponding categories. The proportion of payments with concordant disclosure for honoraria (45%), education (48%), and associated research (61%) was lower than the proportion of payments with concordant disclosure for research (90%), speaker fees (75%–79%), and consulting fees (74%)(Table 5).

In assessing dermatologist-level concordance including all OP entries, the number of dermatologists with no disclosure, overdisclosure only, concordant disclosure, discordant with overdisclosure, and discordant without overdisclosure statuses were 234 (30%), 70 (9%), 9 (1%), 251 (33%), and 204 (27%), respectively. When f/b/t/l entries were excluded, those figures changed to 347 (45%), 108 (14%), 79 (10%), 157 (20%), and 77 (10%), respectively. The characteristics of these dermatologists and their associated industry interactions by disclosure status are shown in the eTable. Dermatologists in the discordant with overdisclosure group had the highest median number and amount of OP payments, followed by those in the concordant disclosure and discordant without overdisclosure groups. Additionally, discordant with overdisclosure dermatologists also had the highest median and mean number of unique industry interactions not on OP, followed by those in the overdisclosure only and no disclosure groups. Academic and private practice settings did not impact dermatologists’ disclosure status. The percentage of female and male dermatologists in the discordant group was 25% and 36%, respectively.

Dermatologists reported a total of 1756 unique industry relationships in the AADMP that were not found on OP. Of these, 1440 (82%) relationships were from 236 dermatologists who had industry payments on OP. The remaining 316 relationships were from 108 dermatologists who had no payments on OP. Although 114 companies reported payments to dermatologists on OP, dermatologists in the AADMP reported interactions with an additional 430 companies.

 

Comment

In this study, we demonstrated discordance between dermatologist self-reported financial interactions in the AADMP compared with those reported by industry via OP. After excluding f/b/t/l entries, approximately two-thirds of the total amount and number of payments in OP were disclosed, while 31% of dermatologists had discordant disclosure status.

Prior investigations in other medical fields showed high discrepancy rates between industry-reported and physician-reported relationships ranging from 23% to 62%, with studies utilizing various methodologies.^{6-9,11,12,14,15} Only a few studies have utilized the OP database.^8,12,15 Thompson et al¹² compared OP payment data with physician financial disclosure at an annual gynecology scientific meeting and found although 209 of 335 (62%) physicians had interactions listed in the OP database, only 24 (7%) listed at least 1 company in the meeting financial disclosure section. Of these 24 physicians, only 5 (21%) accurately disclosed financial relationships with all of the companies listed in OP. The investigators found 129 (38.5%) physicians and 33.7% of the $1.99 million OP payments had concordant disclosure status. When they excluded physicians who received less than $100, 53% of individuals had concordant disclosure.¹² Hannon et al⁸ reported on inconsistencies between disclosures in the OP database and the American Academy of Orthopedic Surgeons Annual Meeting and found 259 (23%) of 1113 physicians meeting inclusion criteria had financial interactions listed in the OP database that were not reported in the meeting disclosures. Yee et al¹⁵ also utilized the OP database and compared it with author disclosures in 3 major ophthalmology journals.Of 670 authors, 367 (54.8%) had complete concordance, with 68 (10.1%) more reporting additional overdisclosures, leading to a discordant with underdisclosure rate of 35.1%. Additionally, $1.46 million (44.6%) of the $3.27 million OP payments had concordant disclosure status.¹⁵ Other studies compared individual companies’ online reports of physician payments with physician self-disclosures in annual meeting programs, clinical guidelines, and peer-reviewed publications.^6,7,9,11,14

Our study demonstrated variation in disclosure status. Compared with other groups, dermatologists in the discordant with overdisclosure group on average had more interactions with and received higher payments from industry, which is consistent with studies in the orthopedic surgery literature.^8,9 Male dermatologists had 11% more discordant disclosures than their female counterparts, which may be influenced by men having more industry interactions than women.³ Although small industry payments possessed the lowest concordant rate in our study, which has been observed,¹² payments greater than $100,000 had the second-lowest concordance rate at 58%, which may be skewed by the small sample size. Rates of concordant disclosure differed among types of interactions, such as between research and associated research payments. This particular difference may be attributed to the incorrect listing of dermatologists as principal investigators or reduced awareness of payments, as associated research payments were made to the institution and not the individual.

Reasons for discrepancies between industry-reported and dermatologist-reported disclosures may include reporting time differences, lack of physician awareness of OP, industry reporting inaccuracies, dearth of contextual information associated with individual payment entries, and misunderstandings. Prior research demonstrated that the most common reasons for physician nondisclosure included misunderstanding disclosure requirements, unintentional omission of payment, and a lack of relationship between the industry payment and presentation topic.^9,12 These factors likely contributed to the disclosure inconsistencies in our study. Similarly high rates of inconsistencies across different specialties suggest systemic concerns.

We found a substantial number of dermatologist-industry interactions listed in the AADMP that were not captured by OP, with 108 dermatologists (35%) having overdisclosures even when excluding f/b/t/l entries. The number of companies in these overdisclosures approximated 4 times the number of companies on OP. Other studies have also observed physician-industry interactions not displayed on OP.^8,12,15 Because the Sunshine Act requires reporting only by certain companies, interactions surrounding products such as over-the-counter merchandise, cosmetics, lasers, novel devices, and new medications are generally not included. Further, OP may not capture nonmonetary industry relationships.

There were several limitations to this study. The most notable limitation was the differences in the categorizations of industry relationships by OP and the AADMP. These differences can overemphasize some types of interactions at the expense of other types, such as f/b/t/l. As such, analyses were repeated after excluding f/b/t/l. Another limitation was the inexact overlap of time frames for OP and the AADMP, which may have led to discrepancies. However, we used the best available data and expect the vast majority of interactions to have occurred by the AAD disclosure deadline. It is possible that the presenters may have had a more updated conflict-of-interest disclosure slide at the time of the meeting presentation. The most important limitation was that we were unable to determine whether discrepancies resulted from underreporting by dermatologists or inaccurate reporting by industry. It was unlikely that OP or the AADMP alone completely represented all dermatologist-industry financial relationships.

Conclusion

With a growing emphasis on physician-industry transparency, we identified rates of differences in dermatology consistent with those in other medical fields when comparing the publicly available OP database with disclosures at national conferences. Although the differences in the categorization and requirements for disclosure between the OP database and the AADMP may account for some of the discordance, dermatologists should be aware of potentially negative public perceptions regarding the transparency and prevalence of physician-industry interactions. Dermatologists should continue to disclose conflicts of interest as accurately as possible and review their industry-reported interactions listed in the OP database.



Acknowledgment
The first two authors contributed equally to this research/article.

Interactions between industry and physicians, including dermatologists, are widely prevalent.^1-3 Proper reporting of industry relationships is essential for transparency, objectivity, and management of potential biases and conflicts of interest. There has been increasing public scrutiny regarding these interactions.

The Physician Payments Sunshine Act established Open Payments (OP), a publicly available database that collects and displays industry-reported physician-industry interactions.^4,5 For the medical community and public, the OP database may be used to assess transparency by comparing the data with physician self-disclosures. There is a paucity of studies in the literature examining the concordance of industry-reported disclosures and physician self-reported data, with even fewer studies utilizing OP as a source of industry disclosures, and none exists for dermatology.^6-12 It also is not clear to what extent the OP database captures all possible dermatologist-industry interactions, as the Sunshine Act only mandates reporting by applicable US-based manufacturers and group purchasing organizations that produce or purchase drugs or devices that require a prescription and are reimbursable by a government-run health care program.⁵ As a result, certain companies, such as cosmeceuticals, may not be represented.

In this study we aimed to evaluate the concordance of dermatologist self-disclosure of industry relationships and those reported on OP. Specifically, we focused on interactions disclosed by presenters at the American Academy of Dermatology (AAD) 73rd Annual Meeting in San Francisco, California (March 20–24, 2015), and those by industry in the 2014 OP database.

Methods

In this retrospective cohort study, we compared publicly available data from the OP database to presenter disclosures found in the publicly available AAD 73rd Annual Meeting program (AADMP). The AAD required speakers to disclose financial relationships with industry within the 12 months preceding the presentation, as outlined in the Accreditation Council for Continuing Medical Education guidelines.¹³ All AAD presenters who were dermatologists practicing in the United States were included in the analysis, whereas residents, fellows, nonphysicians, nondermatologist physicians, and international dermatologists were excluded.

We examined general, research, and associated research payments to specific dermatologists using the 2014 OP data, which contained industry payments made between January 1 and December 31, 2014. Open Payments defined research payments as direct payment to the physician for different types of research activities and associated research payments as indirect payments made to a research institution or entity where the physician was named the principal investigator.⁵ We chose the 2014 database because it most closely matched the period of required disclosures defined by the AAD for the 2015 meeting. Our review of the OP data occurred after the June 2016 update and thus included the most accurate and up-to-date financial interactions.

We conducted our analysis in 2 major steps. First, we determined whether each industry interaction reported in the OP database was present in the AADMP, which provided an assessment of interaction-level concordance. Second, we determined whether all the industry interactions for any given dermatologist listed in the OP also were present in AADMP, which provided an assessment of dermatologist-level concordance.

First, to establish interaction-level concordance for each industry interaction, the company name and the type of interaction (eg, consultant, speaker, investigator) listed in the AADMP were compared with the data in OP to verify a match. Each interaction was assigned into one of the categories of concordant disclosure (a match of both the company name and type of interaction details in OP and the AADMP), overdisclosure (the presence of an AADMP interaction not found in OP, such as an additional type of interaction or company), or underdisclosure (a company name or type of interaction found in OP but not reported in the AADMP). For underdisclosure, we further classified into company present or company absent based on whether the dermatologist disclosed any relationship with a particular company in the AADMP. We considered the type of interaction to be matching if they were identical or similar in nature (eg, consulting in OP and advisory board in the AADMP), as the types of interactions are reported differently in OP and the AADMP. Otherwise, if they were not similar enough (eg, education in OP and stockholder in the AADMP), it was classified as underdisclosure. Some types of interactions reported in OP were not available on the AAD disclosure form. For example, food and beverage as well as travel and lodging were types of interactions in OP that did not exist in the AADMP. These 2 types of interactions comprised a large majority of OP payment entries but only accounted for a small percentage of the payment amount. Analysis was performed both including and excluding interactions for food, beverage, travel, and lodging (f/b/t/l) to best account for differences in interaction categories between OP and the AADMP.

 

Second, each dermatologist was assigned to an overall disclosure category of dermatologist-level concordance based on the status for all his/her interactions. Categories included no disclosure (no industry interactions in OP and the AADMP), concordant (all industry interactions reported in OP and the AADMP match), overdisclosure only (no industry interactions on OP but self-reported interactions present in the AADMP), and discordant (not all OP interactions were disclosed in the AADMP). The discordant category was further divided into with overdisclosure and without overdisclosure, depending on the presence or absence of industry relationships listed in the AADMP but not in OP, respectively.

To ensure uniformity, one individual (A.F.S.) reviewed and collected the data from OP and the AADMP. Information on gender and academic affiliation of study participants was obtained from information listed in the AADMP and Google searches. Data management was performed with Microsoft Excel software (Microsoft Excel 2010, Version 14.0, Microsoft Corporation). The New York University School of Medicine’s (New York, New York) institutional review board exempted this study.

Results

Of the 938 presenters listed in the AADMP, 768 individuals met the inclusion criteria. The most commonly cited type of relationship with industry listed in the AADMP was serving as an investigator, consultant, or advisory board member, comprising 34%, 26%, and 18%, respectively (Table 1). The forms of payment most frequently reported in the AADMP were honoraria and grants/research funding, comprising 49% and 25%, respectively (Table 2).

In 2014, there were a total of 20,761 industry payments totaling $35,627,365 for general, research, and associated research payments in the OP database related to the dermatologists who met inclusion criteria. There were 8678 payments totaling $466,622 for food and beverage and 3238 payments totaling $1,357,770 for travel and lodging. After excluding payments for f/b/t/l, there were 8845 payments totaling $33,802,973, with highest percentages of payment amounts for associated research (67.1%), consulting fees (11.5%), research (7.9%), and speaker fees (7.2%)(Table 3). For presenters with industry payments, the range of disbursements excluding f/b/t/l was $6.52 to $1,933,705, with a mean (standard deviation) of $107,997 ($249,941), a median of $18,247, and an interquartile range of $3422 to $97,375 (data not shown).

In assessing interaction-level concordance, 63% of all payment amounts in OP were classified as concordant disclosures. Regarding the number of OP payments, 27% were concordant disclosures, 34% were underdisclosures due to f/b/t/l payments, and 39% were underdisclosures due to non–f/b/t/l payments. When f/b/t/l payment entries in OP were excluded, the status of concordant disclosure for the amount and number of OP payments increased to 66% ($22,242,638) and 63% (5549), respectively. The percentage of payment entries with concordant disclosure status ranged from 43% to 71% depending on the payment amount. Payment entries at both ends of the spectrum had the lowest concordant disclosure rates, with 43% for payment entries between $0.01 and $100 and 58% for entries greater than $100,000 (Table 4). The concordance status also differed by the type of interactions. None of the OP payments for gift and royalty or license were disclosed in the AADMP, as there were no suitable corresponding categories. The proportion of payments with concordant disclosure for honoraria (45%), education (48%), and associated research (61%) was lower than the proportion of payments with concordant disclosure for research (90%), speaker fees (75%–79%), and consulting fees (74%)(Table 5).

In assessing dermatologist-level concordance including all OP entries, the number of dermatologists with no disclosure, overdisclosure only, concordant disclosure, discordant with overdisclosure, and discordant without overdisclosure statuses were 234 (30%), 70 (9%), 9 (1%), 251 (33%), and 204 (27%), respectively. When f/b/t/l entries were excluded, those figures changed to 347 (45%), 108 (14%), 79 (10%), 157 (20%), and 77 (10%), respectively. The characteristics of these dermatologists and their associated industry interactions by disclosure status are shown in the eTable. Dermatologists in the discordant with overdisclosure group had the highest median number and amount of OP payments, followed by those in the concordant disclosure and discordant without overdisclosure groups. Additionally, discordant with overdisclosure dermatologists also had the highest median and mean number of unique industry interactions not on OP, followed by those in the overdisclosure only and no disclosure groups. Academic and private practice settings did not impact dermatologists’ disclosure status. The percentage of female and male dermatologists in the discordant group was 25% and 36%, respectively.

Dermatologists reported a total of 1756 unique industry relationships in the AADMP that were not found on OP. Of these, 1440 (82%) relationships were from 236 dermatologists who had industry payments on OP. The remaining 316 relationships were from 108 dermatologists who had no payments on OP. Although 114 companies reported payments to dermatologists on OP, dermatologists in the AADMP reported interactions with an additional 430 companies.

 

Comment

In this study, we demonstrated discordance between dermatologist self-reported financial interactions in the AADMP compared with those reported by industry via OP. After excluding f/b/t/l entries, approximately two-thirds of the total amount and number of payments in OP were disclosed, while 31% of dermatologists had discordant disclosure status.

Prior investigations in other medical fields showed high discrepancy rates between industry-reported and physician-reported relationships ranging from 23% to 62%, with studies utilizing various methodologies.^{6-9,11,12,14,15} Only a few studies have utilized the OP database.^8,12,15 Thompson et al¹² compared OP payment data with physician financial disclosure at an annual gynecology scientific meeting and found although 209 of 335 (62%) physicians had interactions listed in the OP database, only 24 (7%) listed at least 1 company in the meeting financial disclosure section. Of these 24 physicians, only 5 (21%) accurately disclosed financial relationships with all of the companies listed in OP. The investigators found 129 (38.5%) physicians and 33.7% of the $1.99 million OP payments had concordant disclosure status. When they excluded physicians who received less than $100, 53% of individuals had concordant disclosure.¹² Hannon et al⁸ reported on inconsistencies between disclosures in the OP database and the American Academy of Orthopedic Surgeons Annual Meeting and found 259 (23%) of 1113 physicians meeting inclusion criteria had financial interactions listed in the OP database that were not reported in the meeting disclosures. Yee et al¹⁵ also utilized the OP database and compared it with author disclosures in 3 major ophthalmology journals.Of 670 authors, 367 (54.8%) had complete concordance, with 68 (10.1%) more reporting additional overdisclosures, leading to a discordant with underdisclosure rate of 35.1%. Additionally, $1.46 million (44.6%) of the $3.27 million OP payments had concordant disclosure status.¹⁵ Other studies compared individual companies’ online reports of physician payments with physician self-disclosures in annual meeting programs, clinical guidelines, and peer-reviewed publications.^6,7,9,11,14

Our study demonstrated variation in disclosure status. Compared with other groups, dermatologists in the discordant with overdisclosure group on average had more interactions with and received higher payments from industry, which is consistent with studies in the orthopedic surgery literature.^8,9 Male dermatologists had 11% more discordant disclosures than their female counterparts, which may be influenced by men having more industry interactions than women.³ Although small industry payments possessed the lowest concordant rate in our study, which has been observed,¹² payments greater than $100,000 had the second-lowest concordance rate at 58%, which may be skewed by the small sample size. Rates of concordant disclosure differed among types of interactions, such as between research and associated research payments. This particular difference may be attributed to the incorrect listing of dermatologists as principal investigators or reduced awareness of payments, as associated research payments were made to the institution and not the individual.

Reasons for discrepancies between industry-reported and dermatologist-reported disclosures may include reporting time differences, lack of physician awareness of OP, industry reporting inaccuracies, dearth of contextual information associated with individual payment entries, and misunderstandings. Prior research demonstrated that the most common reasons for physician nondisclosure included misunderstanding disclosure requirements, unintentional omission of payment, and a lack of relationship between the industry payment and presentation topic.^9,12 These factors likely contributed to the disclosure inconsistencies in our study. Similarly high rates of inconsistencies across different specialties suggest systemic concerns.

We found a substantial number of dermatologist-industry interactions listed in the AADMP that were not captured by OP, with 108 dermatologists (35%) having overdisclosures even when excluding f/b/t/l entries. The number of companies in these overdisclosures approximated 4 times the number of companies on OP. Other studies have also observed physician-industry interactions not displayed on OP.^8,12,15 Because the Sunshine Act requires reporting only by certain companies, interactions surrounding products such as over-the-counter merchandise, cosmetics, lasers, novel devices, and new medications are generally not included. Further, OP may not capture nonmonetary industry relationships.

There were several limitations to this study. The most notable limitation was the differences in the categorizations of industry relationships by OP and the AADMP. These differences can overemphasize some types of interactions at the expense of other types, such as f/b/t/l. As such, analyses were repeated after excluding f/b/t/l. Another limitation was the inexact overlap of time frames for OP and the AADMP, which may have led to discrepancies. However, we used the best available data and expect the vast majority of interactions to have occurred by the AAD disclosure deadline. It is possible that the presenters may have had a more updated conflict-of-interest disclosure slide at the time of the meeting presentation. The most important limitation was that we were unable to determine whether discrepancies resulted from underreporting by dermatologists or inaccurate reporting by industry. It was unlikely that OP or the AADMP alone completely represented all dermatologist-industry financial relationships.

Conclusion

With a growing emphasis on physician-industry transparency, we identified rates of differences in dermatology consistent with those in other medical fields when comparing the publicly available OP database with disclosures at national conferences. Although the differences in the categorization and requirements for disclosure between the OP database and the AADMP may account for some of the discordance, dermatologists should be aware of potentially negative public perceptions regarding the transparency and prevalence of physician-industry interactions. Dermatologists should continue to disclose conflicts of interest as accurately as possible and review their industry-reported interactions listed in the OP database.



Acknowledgment
The first two authors contributed equally to this research/article.

To the Editor:

Dermatology clinical case-viewing (CCV) sessions, commonly referred to as Grand Rounds, are of core educational importance in teaching residents, fellows, and medical students. The traditional format includes the viewing of patient cases followed by resident- and faculty-led group discussions. Clinical case-viewing sessions often involve several health professionals simultaneously observing and interacting with a patient. Although these sessions are highly academically enriching, they may be ill-perceived by patients. The objective of this study was to evaluate patients’ perception of CCV sessions.

This study was approved by the Wake Forest School of Medicine (Winston-Salem, North Carolina) institutional review board and was conducted from February 2017 to August 2017. Following informed consent, 18 patients older than 18 years who were present at the Wake Forest Department of Dermatology CCV sessions were recruited. Patients were each assigned to a private clinical examination room, and CCV attendees briefly visited each room to assess the pathologic findings of interest. Patients received written quantitative surveys before and after the CCV sessions assessing their perspectives on the session (Table 1). Quantitative surveys were assessed using a 10-point Likert scale (1=least willing; 10=most willing). Patients also received qualitative surveys following the session (Table 2). Scores on a 10-item Likert scale were evaluated using a 2-tailed t test.

The mean age of patients was 57.6 years, and women comprised 66.7% (12/18). Patient willingness to attend CCV sessions was relatively unchanged before and after the session, with a mean willingness of 9.7 before the session and 9.0 after the session (P=.09). There was a significant difference in the extent to which patients perceived themselves as experimental subjects prior to the session compared to after the session (2.9 vs 4.2)(P=.046). Following the session, 94.4% (17/18) of patients had the impression that the session met their expectations, and 72.2% (13/18) of patients felt they directly benefitted from the session.

Clinical case-viewing sessions are the foundation of any dermatology residency program^1-3; however, characterizing the sessions’ psychosocial implications on patients is important too. Although some patients did feel part of a “science experiment,” this finding may be of less importance, as patients generally considered the sessions to be a positive experience and were willing to take part again.

Limitations of the study were typical of survey-based research. All participants were patients at a single center, which may limit the generalization of the results, in addition to the small sample size. Clinical case-viewing sessions also are conducted slightly differently between dermatology programs, which may further limit the generalization of the results. Future studies may aim to assess varying CCV formats to optimize both medical education as well as patient satisfaction.

To the Editor:

Dermatology clinical case-viewing (CCV) sessions, commonly referred to as Grand Rounds, are of core educational importance in teaching residents, fellows, and medical students. The traditional format includes the viewing of patient cases followed by resident- and faculty-led group discussions. Clinical case-viewing sessions often involve several health professionals simultaneously observing and interacting with a patient. Although these sessions are highly academically enriching, they may be ill-perceived by patients. The objective of this study was to evaluate patients’ perception of CCV sessions.

This study was approved by the Wake Forest School of Medicine (Winston-Salem, North Carolina) institutional review board and was conducted from February 2017 to August 2017. Following informed consent, 18 patients older than 18 years who were present at the Wake Forest Department of Dermatology CCV sessions were recruited. Patients were each assigned to a private clinical examination room, and CCV attendees briefly visited each room to assess the pathologic findings of interest. Patients received written quantitative surveys before and after the CCV sessions assessing their perspectives on the session (Table 1). Quantitative surveys were assessed using a 10-point Likert scale (1=least willing; 10=most willing). Patients also received qualitative surveys following the session (Table 2). Scores on a 10-item Likert scale were evaluated using a 2-tailed t test.

The mean age of patients was 57.6 years, and women comprised 66.7% (12/18). Patient willingness to attend CCV sessions was relatively unchanged before and after the session, with a mean willingness of 9.7 before the session and 9.0 after the session (P=.09). There was a significant difference in the extent to which patients perceived themselves as experimental subjects prior to the session compared to after the session (2.9 vs 4.2)(P=.046). Following the session, 94.4% (17/18) of patients had the impression that the session met their expectations, and 72.2% (13/18) of patients felt they directly benefitted from the session.

Clinical case-viewing sessions are the foundation of any dermatology residency program^1-3; however, characterizing the sessions’ psychosocial implications on patients is important too. Although some patients did feel part of a “science experiment,” this finding may be of less importance, as patients generally considered the sessions to be a positive experience and were willing to take part again.

Limitations of the study were typical of survey-based research. All participants were patients at a single center, which may limit the generalization of the results, in addition to the small sample size. Clinical case-viewing sessions also are conducted slightly differently between dermatology programs, which may further limit the generalization of the results. Future studies may aim to assess varying CCV formats to optimize both medical education as well as patient satisfaction.

To the Editor:

Dermatology clinical case-viewing (CCV) sessions, commonly referred to as Grand Rounds, are of core educational importance in teaching residents, fellows, and medical students. The traditional format includes the viewing of patient cases followed by resident- and faculty-led group discussions. Clinical case-viewing sessions often involve several health professionals simultaneously observing and interacting with a patient. Although these sessions are highly academically enriching, they may be ill-perceived by patients. The objective of this study was to evaluate patients’ perception of CCV sessions.

This study was approved by the Wake Forest School of Medicine (Winston-Salem, North Carolina) institutional review board and was conducted from February 2017 to August 2017. Following informed consent, 18 patients older than 18 years who were present at the Wake Forest Department of Dermatology CCV sessions were recruited. Patients were each assigned to a private clinical examination room, and CCV attendees briefly visited each room to assess the pathologic findings of interest. Patients received written quantitative surveys before and after the CCV sessions assessing their perspectives on the session (Table 1). Quantitative surveys were assessed using a 10-point Likert scale (1=least willing; 10=most willing). Patients also received qualitative surveys following the session (Table 2). Scores on a 10-item Likert scale were evaluated using a 2-tailed t test.

The mean age of patients was 57.6 years, and women comprised 66.7% (12/18). Patient willingness to attend CCV sessions was relatively unchanged before and after the session, with a mean willingness of 9.7 before the session and 9.0 after the session (P=.09). There was a significant difference in the extent to which patients perceived themselves as experimental subjects prior to the session compared to after the session (2.9 vs 4.2)(P=.046). Following the session, 94.4% (17/18) of patients had the impression that the session met their expectations, and 72.2% (13/18) of patients felt they directly benefitted from the session.

Clinical case-viewing sessions are the foundation of any dermatology residency program^1-3; however, characterizing the sessions’ psychosocial implications on patients is important too. Although some patients did feel part of a “science experiment,” this finding may be of less importance, as patients generally considered the sessions to be a positive experience and were willing to take part again.

Limitations of the study were typical of survey-based research. All participants were patients at a single center, which may limit the generalization of the results, in addition to the small sample size. Clinical case-viewing sessions also are conducted slightly differently between dermatology programs, which may further limit the generalization of the results. Future studies may aim to assess varying CCV formats to optimize both medical education as well as patient satisfaction.

Over the last decade, the use of mobile phones has changed drastically with the advent of more technologically advanced smartphones.¹ Mobile phones are no longer used primarily as devices for talking but rather for text messaging, reading the news, drafting emails, browsing websites, and connecting with others on social media. Considering the increased utility and popularity of social media along with the greater reliance on smartphones, individuals in the United States and worldwide are undoubtedly spending more time on their handheld devices.² With the increase in use and overuse of smartphones, many aspects of society and health are likely affected. Many celebrities who frequently post on social media platforms also have alluded to or directly discussed changes in their dermatologic health secondary to their increased use of smartphones.³ Numerous studies have investigated the positive and negative effects of smartphone use on various musculoskeletal conditions of the upper extremities^4,5 and the social effects of smartphone use on behavior and child development.^6,7 Lee et al⁸ studied the effects of smartphone use on upper extremity muscle pain and activity in relation to 1- or 2-handed operation. In this study, Lee et al⁸ measured the muscle activity and tenderness in 10 women aged 20 to 22 years after a series of timed periods of smartphone use. They concluded that smartphone use resulted in greater muscle activity and tenderness, especially in 1-handed use compared to 2-handed use.⁸ Inal et al⁹ investigated smartphone overuse effects on hand strength and function in 102 college students and discovered that smartphone overuse was correlated with decreased pinch strength, increased median nerve cross-sectional area, and pain in the first digits.⁹

However, few articles have been published investigating skin changes to the digits in relation to smartphone use (Figure 1). In a PubMed search of articles indexed for MEDLINE using the terms smartphone, phone, cell phone, electronic device, handheld device, fifth digit, or skin changes, the authors were unable to find any studies in the literature that involved smartphone use and skin changes to the digits. Based on informal clinical observation and personal experiences, we hypothesized that changes to the fifth digit, likely due to holding a smartphone, would be prevalent and would correlate with amount of time spent on smartphones per day (Figure 2). We also were interested in investigating any other potential correlations with changes to the fifth digit, such as type of smartphone used.

Methods

The study used a cross-sectional design. From September 2018 to December 2018, 374 individuals 18 years or older were recruited to complete a 5-minute anonymous survey online. Using email referrals and social media, participants were presented with a link to a Google survey that only allowed 1 submission per account. On the first page of the survey, participants were presented with a letter explaining that completion of the survey was entirely voluntary, participants were free to withdraw from the study at any time, and participants were providing consent in completing the survey. The protocol was determined to be exempt by the institutional review board at Nova Southeastern University (Fort Lauderdale, Florida) in September 2018.

 

Survey Design
A 20-item survey was designed to measure the amount of time spent using smartphones per day, classify the type of phone used, and quantify skin changes noticed by each respondent. Demographic information for each respondent also was gathered using the survey. The survey was pilot tested to ensure that respondents were able to understand the items.

One item asked if respondents owned a handheld smartphone. Two items assessed how much time was spent on smartphones per day (ie, <1 hour, 1–2 hours, 2–3 hours, 3–4 hours, 4–5 hours, >5 hours) and the type of smartphone used (ie, Apple iPhone, Samsung Galaxy, Google Pixel, Huawei, LG, other). Six items assessed skin changes to the digits, namely the fifth digit (eg, Do you notice any changes to your fifth digit [pinky finger] that would likely be contributed to how you hold your smartphone, such as divot, callus, bruise, wound, misalignment, bend?). Eleven items were used to collect basic demographic information, including age, sex, legal marital status, ethnicity, race, annual household income, highest-earned educational degree, current employment status, health insurance status, and state of residence.

Statistical Analysis
All data were analyzed using IBM SPSS Statistics 23. The association between changes to the fifth digit and time spent on the phone, hand dominance, and socioeconomic factors (ie, age, sex, legal marital status, ethnicity, race, highest-earned educational degree, current employment status, health insurance status, annual household income, state of residence) was analyzed using logistic regression, with changes to the fifth digit as the dependent variable and time spent on the phone, dominant hand, and socioeconomic factors as independent variables. Measures of central tendency, frequencies, and other descriptive analyses were used to define the characteristics of the sample. The bivariate associations between changes to the fifth digit and time spent on the phone, hand dominance, and socioeconomic factors were examined using χ² analysis, correlational analysis, and t tests. Statistical significance was set at P≤.05.

Results

The mean age of the 374 respondents was 33.8 years (range, 18–72 years). One hundred nine respondents were men (29.1%), 262 were women (70.1%), and 3 did not specify (0.8%). Two hundred thirty-four respondents (62.6%) were single, 271 (72.5%) were white, 171 (45.7%) had a bachelor’s degree, and174 (46.5%) were employed full time. Annual household income was normally distributed among the respondents, with 28 (7.5%) earning less than $10,000 per year, 130 (34.8%) earning $10,000 to$49,999 per year, 136 (36.4%) earning $50,000 to $99,999 per year, 52 (13.9%) earning $100,000 to$149,999 per year, and 28 (7.5%) earning more than $150,000 per year. The demographic characteristics of the respondents are presented in Table 1.

Eighty-five (22.7%) respondents admitted to changes to the fifth digit that they associated with holding a smartphone, whereas 289 (77.3%) reported no changes. When asked about the average amount of time spent on their smartphone per day, 17 (4.5%) respondents answered less than 1 hour, 70 (18.7%) answered 1 to 2 hours, 69 (18.4%) answered 2 to 3 hours, 77 (20.6%) answered 3 to 4 hours, 57 (15.2%) answered 4 to 5 hours, and 84 (22.5%) answered more than 5 hours. One hundred ninety-nine (53.2%) respondents indicated they used an Apple iPhone, 95 (25.4%) used a Samsung Galaxy phone, 9 (2.4%) used a Google Pixel phone, 3 (0.8%) used a Huawei phone, 23 (6.1%) used an LG phone, and 45 (12.0%) used another type of smartphone. The characteristics of smartphone use as reported by the respondents are presented in Table 2.

 

Comment

Consistent with our hypothesis, changes to the fifth digit were prevalent in the surveyed population, with 85 (22.7%) respondents admitting to changes to their fifth digit from holding a smartphone. The changes to the fifth digit were described as 1 or more of the following: divot (impression), callus (skin thickening), bruise, wound, misalignment, or bending. Most respondents who noted skin changes on the survey endorsed changes consistent with calluses and/or divots. These changes can be described as scaly, lichenified, well-demarcated papules or plaques with variable overlying hyperpigmentation and surrounding erythema. In cases with resulting chronic indentations of the skin, one also would observe localized sclerosis, atrophy, and/or induration of the area, which we found to be less prevalent than expected considering the popularity and notable reliance on smartphones.²

The most commonly reported chronic skin changes to the fifth digit are similar to those of lichen simplex chronicus and/or exogenous lobular panniculitis, which can be both symptomatically and cosmetically troubling for a patient. Functional impairment in movement of the fifth digit may result from the overlying lichenification and induration, as well as from lipoatrophy of the underlying traumatized subcutaneous fat, especially if the affected area is overlying the proximal interphalangeal joint of the fifth digit. These resulting alterations in the skin of the fifth digit also may be cosmetically displeasing to the patient.

On histology, we would expect similar changes to that of lichen simplex chronicus—compact hyperkeratosis and hypergranulosis—and/or an exogenous lobular panniculitis. Lobular panniculitis demonstrates necrosis of the fat lobule; vacuolated spaces; and lipomembranous changes such as fatty cystic degeneration with feathery eosinophilic material in an arabesque pattern, which has been described as frost on a windowpane, or a ferning pattern at the edge of the lipid vacuole.¹⁰

We also were correct in our hypothesis that prevalence of changes to the fifth digit correlate with amount of time spent on smartphones per day. Bivariate and multivariate logistic regression analysis showed that a change to the fifth digit was not significantly associated with hand dominance or socioeconomic factors (ie, age, sex, legal marital status, ethnicity, race, annual household income, highest-earned educational degree, current employment status, health insurance status, and state of residence). Controlling for all other factors, the only factor that significantly increased the odds of experiencing a change to the fifth digit was the amount of time spent on the phone per day. The respondents who spent more than 5 hours per day on their phones had 5-times greater odds of experiencing a change to their fifth digit compared with respondents who spent less than 1 hour per day on their phones (P=.045).

Although no other correlations with changes to the fifth digit, such as type of smartphone used, were found in our study, future studies should continue to investigate other potential factors that play a role in smartphone use changing the appearance and function of the digits. Our lack of significant correlations with changes to the fifth digit could be attributed to a small sample size and other possible factors, such as the frequent design changes of smartphones by manufacturers. Our study also is limited by the possibility of other factors contributing to these observed skin changes. Although we have anecdotally observed these skin changes and have hypothesized that smartphones are the culprit, other causes, such as holding certain tools, could lead to these skin changes. In addition, there are many different ways to hold a smartphone, and certain hand positionings may be more or less prone to skin changes described in our study. Various accessories, such as cases and gripping devices, also may change the way smartphones are held and would skew the results of our survey. Future studies could examine different ways smartphones are held, how various accessories affect these skin changes, and the size or model of phones that make these skin changes more or less prevalent.

Conclusion

Our study is an initial step in uncovering a possible phenomenon of smartphone use affecting the digits, namely the fifth digit. Our findings demonstrate that the amount of time spent on the phone per day significantly increases the odds of experiencing a change to the fifth digit. We expect these potential skin changes as well as other musculoskeletal changes to increase in prevalence as daily smartphone use continues to increase. With the lack of studies investigating skin changes to the digits in relation to smartphone use, future studies are needed to verify our results and confirm the presence of this issue.

Over the last decade, the use of mobile phones has changed drastically with the advent of more technologically advanced smartphones.¹ Mobile phones are no longer used primarily as devices for talking but rather for text messaging, reading the news, drafting emails, browsing websites, and connecting with others on social media. Considering the increased utility and popularity of social media along with the greater reliance on smartphones, individuals in the United States and worldwide are undoubtedly spending more time on their handheld devices.² With the increase in use and overuse of smartphones, many aspects of society and health are likely affected. Many celebrities who frequently post on social media platforms also have alluded to or directly discussed changes in their dermatologic health secondary to their increased use of smartphones.³ Numerous studies have investigated the positive and negative effects of smartphone use on various musculoskeletal conditions of the upper extremities^4,5 and the social effects of smartphone use on behavior and child development.^6,7 Lee et al⁸ studied the effects of smartphone use on upper extremity muscle pain and activity in relation to 1- or 2-handed operation. In this study, Lee et al⁸ measured the muscle activity and tenderness in 10 women aged 20 to 22 years after a series of timed periods of smartphone use. They concluded that smartphone use resulted in greater muscle activity and tenderness, especially in 1-handed use compared to 2-handed use.⁸ Inal et al⁹ investigated smartphone overuse effects on hand strength and function in 102 college students and discovered that smartphone overuse was correlated with decreased pinch strength, increased median nerve cross-sectional area, and pain in the first digits.⁹

However, few articles have been published investigating skin changes to the digits in relation to smartphone use (Figure 1). In a PubMed search of articles indexed for MEDLINE using the terms smartphone, phone, cell phone, electronic device, handheld device, fifth digit, or skin changes, the authors were unable to find any studies in the literature that involved smartphone use and skin changes to the digits. Based on informal clinical observation and personal experiences, we hypothesized that changes to the fifth digit, likely due to holding a smartphone, would be prevalent and would correlate with amount of time spent on smartphones per day (Figure 2). We also were interested in investigating any other potential correlations with changes to the fifth digit, such as type of smartphone used.

Methods

The study used a cross-sectional design. From September 2018 to December 2018, 374 individuals 18 years or older were recruited to complete a 5-minute anonymous survey online. Using email referrals and social media, participants were presented with a link to a Google survey that only allowed 1 submission per account. On the first page of the survey, participants were presented with a letter explaining that completion of the survey was entirely voluntary, participants were free to withdraw from the study at any time, and participants were providing consent in completing the survey. The protocol was determined to be exempt by the institutional review board at Nova Southeastern University (Fort Lauderdale, Florida) in September 2018.

 

Survey Design
A 20-item survey was designed to measure the amount of time spent using smartphones per day, classify the type of phone used, and quantify skin changes noticed by each respondent. Demographic information for each respondent also was gathered using the survey. The survey was pilot tested to ensure that respondents were able to understand the items.

One item asked if respondents owned a handheld smartphone. Two items assessed how much time was spent on smartphones per day (ie, <1 hour, 1–2 hours, 2–3 hours, 3–4 hours, 4–5 hours, >5 hours) and the type of smartphone used (ie, Apple iPhone, Samsung Galaxy, Google Pixel, Huawei, LG, other). Six items assessed skin changes to the digits, namely the fifth digit (eg, Do you notice any changes to your fifth digit [pinky finger] that would likely be contributed to how you hold your smartphone, such as divot, callus, bruise, wound, misalignment, bend?). Eleven items were used to collect basic demographic information, including age, sex, legal marital status, ethnicity, race, annual household income, highest-earned educational degree, current employment status, health insurance status, and state of residence.

Statistical Analysis
All data were analyzed using IBM SPSS Statistics 23. The association between changes to the fifth digit and time spent on the phone, hand dominance, and socioeconomic factors (ie, age, sex, legal marital status, ethnicity, race, highest-earned educational degree, current employment status, health insurance status, annual household income, state of residence) was analyzed using logistic regression, with changes to the fifth digit as the dependent variable and time spent on the phone, dominant hand, and socioeconomic factors as independent variables. Measures of central tendency, frequencies, and other descriptive analyses were used to define the characteristics of the sample. The bivariate associations between changes to the fifth digit and time spent on the phone, hand dominance, and socioeconomic factors were examined using χ² analysis, correlational analysis, and t tests. Statistical significance was set at P≤.05.

Results

The mean age of the 374 respondents was 33.8 years (range, 18–72 years). One hundred nine respondents were men (29.1%), 262 were women (70.1%), and 3 did not specify (0.8%). Two hundred thirty-four respondents (62.6%) were single, 271 (72.5%) were white, 171 (45.7%) had a bachelor’s degree, and174 (46.5%) were employed full time. Annual household income was normally distributed among the respondents, with 28 (7.5%) earning less than $10,000 per year, 130 (34.8%) earning $10,000 to$49,999 per year, 136 (36.4%) earning $50,000 to $99,999 per year, 52 (13.9%) earning $100,000 to$149,999 per year, and 28 (7.5%) earning more than $150,000 per year. The demographic characteristics of the respondents are presented in Table 1.

Eighty-five (22.7%) respondents admitted to changes to the fifth digit that they associated with holding a smartphone, whereas 289 (77.3%) reported no changes. When asked about the average amount of time spent on their smartphone per day, 17 (4.5%) respondents answered less than 1 hour, 70 (18.7%) answered 1 to 2 hours, 69 (18.4%) answered 2 to 3 hours, 77 (20.6%) answered 3 to 4 hours, 57 (15.2%) answered 4 to 5 hours, and 84 (22.5%) answered more than 5 hours. One hundred ninety-nine (53.2%) respondents indicated they used an Apple iPhone, 95 (25.4%) used a Samsung Galaxy phone, 9 (2.4%) used a Google Pixel phone, 3 (0.8%) used a Huawei phone, 23 (6.1%) used an LG phone, and 45 (12.0%) used another type of smartphone. The characteristics of smartphone use as reported by the respondents are presented in Table 2.

 

Comment

Consistent with our hypothesis, changes to the fifth digit were prevalent in the surveyed population, with 85 (22.7%) respondents admitting to changes to their fifth digit from holding a smartphone. The changes to the fifth digit were described as 1 or more of the following: divot (impression), callus (skin thickening), bruise, wound, misalignment, or bending. Most respondents who noted skin changes on the survey endorsed changes consistent with calluses and/or divots. These changes can be described as scaly, lichenified, well-demarcated papules or plaques with variable overlying hyperpigmentation and surrounding erythema. In cases with resulting chronic indentations of the skin, one also would observe localized sclerosis, atrophy, and/or induration of the area, which we found to be less prevalent than expected considering the popularity and notable reliance on smartphones.²

The most commonly reported chronic skin changes to the fifth digit are similar to those of lichen simplex chronicus and/or exogenous lobular panniculitis, which can be both symptomatically and cosmetically troubling for a patient. Functional impairment in movement of the fifth digit may result from the overlying lichenification and induration, as well as from lipoatrophy of the underlying traumatized subcutaneous fat, especially if the affected area is overlying the proximal interphalangeal joint of the fifth digit. These resulting alterations in the skin of the fifth digit also may be cosmetically displeasing to the patient.

On histology, we would expect similar changes to that of lichen simplex chronicus—compact hyperkeratosis and hypergranulosis—and/or an exogenous lobular panniculitis. Lobular panniculitis demonstrates necrosis of the fat lobule; vacuolated spaces; and lipomembranous changes such as fatty cystic degeneration with feathery eosinophilic material in an arabesque pattern, which has been described as frost on a windowpane, or a ferning pattern at the edge of the lipid vacuole.¹⁰

We also were correct in our hypothesis that prevalence of changes to the fifth digit correlate with amount of time spent on smartphones per day. Bivariate and multivariate logistic regression analysis showed that a change to the fifth digit was not significantly associated with hand dominance or socioeconomic factors (ie, age, sex, legal marital status, ethnicity, race, annual household income, highest-earned educational degree, current employment status, health insurance status, and state of residence). Controlling for all other factors, the only factor that significantly increased the odds of experiencing a change to the fifth digit was the amount of time spent on the phone per day. The respondents who spent more than 5 hours per day on their phones had 5-times greater odds of experiencing a change to their fifth digit compared with respondents who spent less than 1 hour per day on their phones (P=.045).

Although no other correlations with changes to the fifth digit, such as type of smartphone used, were found in our study, future studies should continue to investigate other potential factors that play a role in smartphone use changing the appearance and function of the digits. Our lack of significant correlations with changes to the fifth digit could be attributed to a small sample size and other possible factors, such as the frequent design changes of smartphones by manufacturers. Our study also is limited by the possibility of other factors contributing to these observed skin changes. Although we have anecdotally observed these skin changes and have hypothesized that smartphones are the culprit, other causes, such as holding certain tools, could lead to these skin changes. In addition, there are many different ways to hold a smartphone, and certain hand positionings may be more or less prone to skin changes described in our study. Various accessories, such as cases and gripping devices, also may change the way smartphones are held and would skew the results of our survey. Future studies could examine different ways smartphones are held, how various accessories affect these skin changes, and the size or model of phones that make these skin changes more or less prevalent.

Conclusion

Our study is an initial step in uncovering a possible phenomenon of smartphone use affecting the digits, namely the fifth digit. Our findings demonstrate that the amount of time spent on the phone per day significantly increases the odds of experiencing a change to the fifth digit. We expect these potential skin changes as well as other musculoskeletal changes to increase in prevalence as daily smartphone use continues to increase. With the lack of studies investigating skin changes to the digits in relation to smartphone use, future studies are needed to verify our results and confirm the presence of this issue.

Over the last decade, the use of mobile phones has changed drastically with the advent of more technologically advanced smartphones.¹ Mobile phones are no longer used primarily as devices for talking but rather for text messaging, reading the news, drafting emails, browsing websites, and connecting with others on social media. Considering the increased utility and popularity of social media along with the greater reliance on smartphones, individuals in the United States and worldwide are undoubtedly spending more time on their handheld devices.² With the increase in use and overuse of smartphones, many aspects of society and health are likely affected. Many celebrities who frequently post on social media platforms also have alluded to or directly discussed changes in their dermatologic health secondary to their increased use of smartphones.³ Numerous studies have investigated the positive and negative effects of smartphone use on various musculoskeletal conditions of the upper extremities^4,5 and the social effects of smartphone use on behavior and child development.^6,7 Lee et al⁸ studied the effects of smartphone use on upper extremity muscle pain and activity in relation to 1- or 2-handed operation. In this study, Lee et al⁸ measured the muscle activity and tenderness in 10 women aged 20 to 22 years after a series of timed periods of smartphone use. They concluded that smartphone use resulted in greater muscle activity and tenderness, especially in 1-handed use compared to 2-handed use.⁸ Inal et al⁹ investigated smartphone overuse effects on hand strength and function in 102 college students and discovered that smartphone overuse was correlated with decreased pinch strength, increased median nerve cross-sectional area, and pain in the first digits.⁹

However, few articles have been published investigating skin changes to the digits in relation to smartphone use (Figure 1). In a PubMed search of articles indexed for MEDLINE using the terms smartphone, phone, cell phone, electronic device, handheld device, fifth digit, or skin changes, the authors were unable to find any studies in the literature that involved smartphone use and skin changes to the digits. Based on informal clinical observation and personal experiences, we hypothesized that changes to the fifth digit, likely due to holding a smartphone, would be prevalent and would correlate with amount of time spent on smartphones per day (Figure 2). We also were interested in investigating any other potential correlations with changes to the fifth digit, such as type of smartphone used.

Methods

The study used a cross-sectional design. From September 2018 to December 2018, 374 individuals 18 years or older were recruited to complete a 5-minute anonymous survey online. Using email referrals and social media, participants were presented with a link to a Google survey that only allowed 1 submission per account. On the first page of the survey, participants were presented with a letter explaining that completion of the survey was entirely voluntary, participants were free to withdraw from the study at any time, and participants were providing consent in completing the survey. The protocol was determined to be exempt by the institutional review board at Nova Southeastern University (Fort Lauderdale, Florida) in September 2018.

 

Survey Design
A 20-item survey was designed to measure the amount of time spent using smartphones per day, classify the type of phone used, and quantify skin changes noticed by each respondent. Demographic information for each respondent also was gathered using the survey. The survey was pilot tested to ensure that respondents were able to understand the items.

One item asked if respondents owned a handheld smartphone. Two items assessed how much time was spent on smartphones per day (ie, <1 hour, 1–2 hours, 2–3 hours, 3–4 hours, 4–5 hours, >5 hours) and the type of smartphone used (ie, Apple iPhone, Samsung Galaxy, Google Pixel, Huawei, LG, other). Six items assessed skin changes to the digits, namely the fifth digit (eg, Do you notice any changes to your fifth digit [pinky finger] that would likely be contributed to how you hold your smartphone, such as divot, callus, bruise, wound, misalignment, bend?). Eleven items were used to collect basic demographic information, including age, sex, legal marital status, ethnicity, race, annual household income, highest-earned educational degree, current employment status, health insurance status, and state of residence.

Statistical Analysis
All data were analyzed using IBM SPSS Statistics 23. The association between changes to the fifth digit and time spent on the phone, hand dominance, and socioeconomic factors (ie, age, sex, legal marital status, ethnicity, race, highest-earned educational degree, current employment status, health insurance status, annual household income, state of residence) was analyzed using logistic regression, with changes to the fifth digit as the dependent variable and time spent on the phone, dominant hand, and socioeconomic factors as independent variables. Measures of central tendency, frequencies, and other descriptive analyses were used to define the characteristics of the sample. The bivariate associations between changes to the fifth digit and time spent on the phone, hand dominance, and socioeconomic factors were examined using χ² analysis, correlational analysis, and t tests. Statistical significance was set at P≤.05.

Results

The mean age of the 374 respondents was 33.8 years (range, 18–72 years). One hundred nine respondents were men (29.1%), 262 were women (70.1%), and 3 did not specify (0.8%). Two hundred thirty-four respondents (62.6%) were single, 271 (72.5%) were white, 171 (45.7%) had a bachelor’s degree, and174 (46.5%) were employed full time. Annual household income was normally distributed among the respondents, with 28 (7.5%) earning less than $10,000 per year, 130 (34.8%) earning $10,000 to$49,999 per year, 136 (36.4%) earning $50,000 to $99,999 per year, 52 (13.9%) earning $100,000 to$149,999 per year, and 28 (7.5%) earning more than $150,000 per year. The demographic characteristics of the respondents are presented in Table 1.

Eighty-five (22.7%) respondents admitted to changes to the fifth digit that they associated with holding a smartphone, whereas 289 (77.3%) reported no changes. When asked about the average amount of time spent on their smartphone per day, 17 (4.5%) respondents answered less than 1 hour, 70 (18.7%) answered 1 to 2 hours, 69 (18.4%) answered 2 to 3 hours, 77 (20.6%) answered 3 to 4 hours, 57 (15.2%) answered 4 to 5 hours, and 84 (22.5%) answered more than 5 hours. One hundred ninety-nine (53.2%) respondents indicated they used an Apple iPhone, 95 (25.4%) used a Samsung Galaxy phone, 9 (2.4%) used a Google Pixel phone, 3 (0.8%) used a Huawei phone, 23 (6.1%) used an LG phone, and 45 (12.0%) used another type of smartphone. The characteristics of smartphone use as reported by the respondents are presented in Table 2.

 

Comment

Consistent with our hypothesis, changes to the fifth digit were prevalent in the surveyed population, with 85 (22.7%) respondents admitting to changes to their fifth digit from holding a smartphone. The changes to the fifth digit were described as 1 or more of the following: divot (impression), callus (skin thickening), bruise, wound, misalignment, or bending. Most respondents who noted skin changes on the survey endorsed changes consistent with calluses and/or divots. These changes can be described as scaly, lichenified, well-demarcated papules or plaques with variable overlying hyperpigmentation and surrounding erythema. In cases with resulting chronic indentations of the skin, one also would observe localized sclerosis, atrophy, and/or induration of the area, which we found to be less prevalent than expected considering the popularity and notable reliance on smartphones.²

The most commonly reported chronic skin changes to the fifth digit are similar to those of lichen simplex chronicus and/or exogenous lobular panniculitis, which can be both symptomatically and cosmetically troubling for a patient. Functional impairment in movement of the fifth digit may result from the overlying lichenification and induration, as well as from lipoatrophy of the underlying traumatized subcutaneous fat, especially if the affected area is overlying the proximal interphalangeal joint of the fifth digit. These resulting alterations in the skin of the fifth digit also may be cosmetically displeasing to the patient.

On histology, we would expect similar changes to that of lichen simplex chronicus—compact hyperkeratosis and hypergranulosis—and/or an exogenous lobular panniculitis. Lobular panniculitis demonstrates necrosis of the fat lobule; vacuolated spaces; and lipomembranous changes such as fatty cystic degeneration with feathery eosinophilic material in an arabesque pattern, which has been described as frost on a windowpane, or a ferning pattern at the edge of the lipid vacuole.¹⁰

We also were correct in our hypothesis that prevalence of changes to the fifth digit correlate with amount of time spent on smartphones per day. Bivariate and multivariate logistic regression analysis showed that a change to the fifth digit was not significantly associated with hand dominance or socioeconomic factors (ie, age, sex, legal marital status, ethnicity, race, annual household income, highest-earned educational degree, current employment status, health insurance status, and state of residence). Controlling for all other factors, the only factor that significantly increased the odds of experiencing a change to the fifth digit was the amount of time spent on the phone per day. The respondents who spent more than 5 hours per day on their phones had 5-times greater odds of experiencing a change to their fifth digit compared with respondents who spent less than 1 hour per day on their phones (P=.045).

Although no other correlations with changes to the fifth digit, such as type of smartphone used, were found in our study, future studies should continue to investigate other potential factors that play a role in smartphone use changing the appearance and function of the digits. Our lack of significant correlations with changes to the fifth digit could be attributed to a small sample size and other possible factors, such as the frequent design changes of smartphones by manufacturers. Our study also is limited by the possibility of other factors contributing to these observed skin changes. Although we have anecdotally observed these skin changes and have hypothesized that smartphones are the culprit, other causes, such as holding certain tools, could lead to these skin changes. In addition, there are many different ways to hold a smartphone, and certain hand positionings may be more or less prone to skin changes described in our study. Various accessories, such as cases and gripping devices, also may change the way smartphones are held and would skew the results of our survey. Future studies could examine different ways smartphones are held, how various accessories affect these skin changes, and the size or model of phones that make these skin changes more or less prevalent.

Conclusion

Our study is an initial step in uncovering a possible phenomenon of smartphone use affecting the digits, namely the fifth digit. Our findings demonstrate that the amount of time spent on the phone per day significantly increases the odds of experiencing a change to the fifth digit. We expect these potential skin changes as well as other musculoskeletal changes to increase in prevalence as daily smartphone use continues to increase. With the lack of studies investigating skin changes to the digits in relation to smartphone use, future studies are needed to verify our results and confirm the presence of this issue.

From Northwell Health System, Lake Success, NY.

Abstract

• Objective: To explore factors associated with lower-extremity amputation (LEA) in patients with diabetic foot ulcers using data from the Online Wound Electronic Medical Record Database.
• Design: Retrospective analysis of medical records.
• Setting and participants: Data from 169 individuals with previously diagnosed diabetes mellitus who received wound care for a 6-month period within a span of 2 years was analyzed. A baseline evaluation was obtained and wound(s) were treated, managed, and monitored. Treatment continued until the patient healed, required an LEA, or phased out of the study, neither healing nor undergoing an amputation. Of the 149 patients who completed the study, 38 had healed ulcers, 14 underwent amputation, and 97 neither healed nor underwent an amputation. All patients were treated under the care of vascular and/or podiatric surgeons.
• Measurements: Variables included wound status (healed, amputated, and unhealed/non-amputated); size of wound area; age, gender, race, and ethnicity; white blood cell (WBC) count, hemoglobin A1c (HbA1c), blood glucose, and body mass index (BMI); and presence of osteomyelitis, gangrene, and peripheral vascular disease.
• Results: As compared to the healed and unhealed/non-amputated group, the group of patients who underwent LEA was older and had higher percentages of males, Hispanics, and African Americans; had a higher WBC count, larger wound area, and higher rates of wound infection, osteomyelitis, and neuropathy; and had lower average values of HbA1c, blood glucose, and BMI and a lower rate of peripheral vascular disease.
• Conclusion: The association between HbA1c and LEA highlights a window of relative safety among an at-risk population. By identifying and focusing on factors associated with LEA, health care professionals may be able to decrease the prevalence of LEA in patients with diabetes.

Keywords: diabetic foot ulcer; lower-extremity amputation; risk factors; HbA1c.

An estimated 30.3 million people, or 9.4% of the US population, has diabetes. In 2014, approximately 108,000 amputations were performed on adults with diagnosed diabetes.¹ Furthermore, patients with diabetes have a 10-fold increased risk for lower-extremity amputation (LEA), as compared with patients without diabetes.² The frequency of amputations in the diabetic population is a public health crisis.

Amputation has significant, life-altering consequences. Patients who undergo LEA often face debilitation in their daily activities and must undergo intense rehabilitation to learn basic tasks. Amputations can also impact individuals’ psychological well-being as they come to terms with their altered body and may face challenges in self-perception, confidence, self-esteem, work life, and relationships. In addition, the mortality rate for patients with diabetes 5 years after undergoing LEA is 30%.² However, public health studies estimate that more than half of LEAs in patients with diabetes are preventable.³

Although studies have explored the relationship between diabetes and LEA, few have sought to identify factors directly correlated with wound care. In the United States, patients with diabetic ulcerations are typically treated in wound care facilities; however, previous studies have concentrated on the conditions that lead to the formation of an ulcer or amputation, viewing amputation and ulcer as 2 separate entities. Our study took into account systemic variables, patient demographics, and specific wound characteristics to explore factors associated with LEA in a high-risk group of patients with diabetes. This study was designed to assess ailments that are prevalent in patients who require a LEA.

 

Methods

Patients and Setting

A total of 169 patients who were treated at the Comprehensive Wound Healing and Hyperbaric Center (Lake Success, NY), a tertiary facility of the Northwell Health system, participated in this retrospective study. The data for this study were obtained in conjunction with the development of the New York University School of Medicine’s Online Wound Electronic Medical Record to Decrease Limb Amputations in Persons with Diabetes (OWEMR) database. The OWEMR collects individual patient data from satellite locations across the country. Using this database, researchers can analyze similarities and differences between patients who undergo LEA.

This study utilized patient data specific to the Northwell Health facility. All of the patients in our study were enrolled under the criteria of the OWEMR database. In order to be included in the OWEMR database, patients had to be diagnosed with type 1 or type 2 diabetes; have a break in the skin ≥ 0.5 cm²; be 18 years of age or older; and have a measured hemoglobin A1c (HbA1c) value within the past 120 days. Study patients signed an informed consent and committed to being available for follow-up visits to the wound care facility for 6 months after entering the study. Patients were enrolled between 2012 and 2014, and each patient was monitored for a period of 6 months within this time period. Participants were treated with current standards of care using diet, lifestyle, and pharmacologic interventions. This study was approved by the Northwell Health System Institutional Review Board Human Research Protection Program (Manhasset, NY).

Data Collection

On their first visit to the facility, patients were given a physical examination and initial interview regarding their medical history. Clinicians were required to select 1 ulcer that would be examined for the duration of the study. The selection of the ulcer was based on a point system that awarded points for pedal pulses, the ability to be probed to the bone, the location of the ulcer (ie, located on the foot rather than a toe), and the presence of multiple ulcerations. The ulcer with the highest score was selected for the study. If numerous ulcers were evaluated with the same score, the largest and deepest was selected. Wagner classification of the wound was recorded at baseline and taken at each subsequent patient visit. In addition, peripheral sensation was assessed for signs of neuropathy using Semmes-Weinstein monofilament testing.

Once selected, the wound was clinically evaluated, samples for culture were obtained, and blood tests were performed to detect the presence of wound infection. The patient’s blood was drawn for a full laboratory analysis, including white blood cell (WBC) count and measurement of blood glucose and HbA1c levels. Bone biopsy, magnetic resonance imaging, and bone scans were used to detect the presence of osteomyelitis at the discretion of the health care provider. Wounds suspected of infection, underlying osteomyelitis, or gangrene at baseline were excluded. Patients would then return for follow-up visits at least once every 6 weeks, plus or minus 2 weeks, for a maximum of 6 months.

Statistical Analysis

Utilizing SAS version 9.3 (Cary, NC), descriptive statistics (minimum, maximum, mean, median, and SD) were calculated for the following variables: age, WBC count, wound area, HbA1c, blood glucose, and body mass index (BMI). These variables were collected for each patient as per the OWEMR protocol and provided a basis for which to compare patients who underwent amputation and those who did not. Twenty patients were lost to follow-up, and therefore we altered the window of our statistics from 6 months to 3 months to provide the most accurate data, as 6-month follow-up data were limited. The patients were classified into the following categories: healed, amputated, and unhealed/non-amputated. Descriptive statistics were calculated for these 3 groups, analyzing the same variables (age, WBC count, wound area, HbA1c, blood glucose, and BMI). Additional statistical computations were utilized in order to show the prevalence and frequency of our categorical variables: gender, race, ethnicity, osteomyelitis, gangrene, and peripheral vascular disease. The baseline values of WBC count, HbA1c, wound area, and BMI of the 3 groups were analyzed with descriptive statistics for comparison. A multinomial logistic regression was then performed using a 3-level outcome variable: healed, amputated, or unhealed/non-amputated. Each predictor variable was analyzed independently due to the small sample size.

 

Results

Of the 169 registered patients treated at the Northwell Health facility, all qualified for the OWEMR study and met the study criteria. In the original 169 patients, there were 19 amputations: 6 toe, 6 trans-metatarsal, 6 below knee, and 1 above knee (Table 1).

The descriptive statistics of 149 patients grouped into 3 categories (healed, amputated, unhealed/non-amputated) are shown in Table 2.

The results of the logistic regression exploring the differences between the amputation and healed groups and the unhealed/non-amputated group are shown in Table 3. The amputation group had a higher mean age and WBC count and greater wound area. Increased age was determined to be a significant predictor of the odds of amputation (P = 0.0089). For each year increase in age, the odds of amputation increased by 6.5% (odds ratio, 1.07 [95% confidence interval {CI}, 1.02-1.12]). Patients in the amputation group were more likely to be male, Hispanic, and African American and to have wound infections and comorbidities (osteomyelitis, neuropathy, and gangrene).

The presence of gangrene was significantly associated with LEA (P = 0.03). Specifically, the odds of patients without gangrene undergoing a LEA were substantially lower compared with their counterparts with gangrene (odds ratio, 0.17; 95% CI, 0.04-0.68; P = 0.0131). However, the presence of gangrene was not associated with the odds of healing compared with the odds of neither healing nor undergoing amputation (P = 0.84; not shown in Table 3).

The amputation group had lower mean values for HbA1c, BMI, and blood glucose levels and a lower rate of peripheral vascular disease. Only the relationship between lower HbA1c and increased odds of amputation versus not healing/non-amputation was found to be statistically significant (95% CI, 0.27-0.78; P = 0.009).

 

Discussion

This retrospective study was undertaken to evaluate factors associated with LEA in patients with diabetic foot ulcers. Patients with diabetes being treated at a wound care facility often require continuous surgical and metabolic intervention to promote optimal healing: drainage, surgical debridement, irrigation, culturing for infection, and monitoring of blood glucose levels. This treatment requires strict compliance with medical directions and, oftentimes, additional care, such as home-care nursing visits, to maintain a curative environment for the wound. Frequently, wounds on the lower extremity further complicate the healing process by reducing the patient’s mobility and daily life. Due to these factors, many patients progress to LEA. The link between diabetic ulcers and amputation has already been well described in previous studies, with studies showing that history of diabetic foot ulcer significantly predisposes an individual to LEA.⁴ However, few studies have further investigated demographic factors associated with risk for an amputation. Our study analyzed several categories of patient data taken from a baseline visit. We found that those with highly elevated HbA1c values were less likely to have an amputation than persons with relatively lower levels, a finding that is contrary to previous studies.

Our study’s findings suggest a higher risk for LEA with increased age. The amputation group was, on average, 7 years older than the other 2 groups. A recent study showed that risk for amputation is directly correlated to patient age, as is the mortality rate after undergoing LEA (2.3%; P < 0.05).⁵ Our study found that with each increase in age of 1 year, the odds of amputation increased by 6.5%. However, recent evidence on LEA risk and aging suggests that age is of less consequence than the duration of diabetes. One study found that the propensity to develop diabetic foot ulcers increases with the duration of diabetes.⁶ The same study found that prevalence of ulceration was correlated with age, but the relationship between age and LEA was less significant. A follow-up study for LEA could be done to examine the role of disease duration versus age in LEA.

A consensus among previous studies is that men have a higher risk for LEA.^5,7 Men comprised the majority in all 3 groups in our study. In addition, the amputation group in our study had the lowest BMI. Higher BMI generally is associated with an increased risk for health complications. However, a past study conducted in Taiwan reported that obese patients with diabetes were less likely to undergo LEA than those within the normal range for BMI.⁸ Neither study suggests that obesity is a deterrent for LEA, but both studies may suggest that risk of amputation may approach a maximum frequency at a specific BMI range, and then decrease. This unconfirmed “cyclic” relationship should be evaluated further in a larger sample size.

Most patients in our analysis were Caucasian, followed by African American and South Asian. African Americans were the only racial group with an increased frequency in the amputation group. This finding is supported by a previous study that found that the rate of LEA among patients with diabetes in low-income, predominantly African-American neighborhoods was nearly double that in wealthier, predominantly Caucasian areas.⁹ A potential problem in the comparison between our data with previous studies is that the studies did not analyze patients with our inclusion criteria. All patients with diabetes in previous investigations were grouped by race, but were not necessarily required to have 1 or more ulcers. Multiple ulcers may predispose an individual to a greater risk for amputation.

Multinomial logistic regression did not suggest an association between initial size of a patient’s wound and the risk of amputation. However, the descriptive data suggests a trend. Patients who did not heal or require an amputation had the largest average wound area. This finding is not surprising in that our study followed individuals for only 3 months. Many wounds require a long course of treatment, especially in patients with diabetes, who may have poor vascularization. However, in comparison to the healed patients, the patients who required an amputation had a larger average wound area. A larger wound requires a plentiful vascular supply for the delivery of clotting factors and nutrients to the damaged area. As wound size increases, an individual’s body must transmit an increased quantity of these factors and nutrients for the regeneration of tissue. In addition, wounds that possess a larger surface area require more debridement and present a greater opportunity for infection. This may also foreshadow a longer, more costly course of treatment. Additionally, individuals coping with large ulcerations are burdened by more elaborate and complex wound dressings.

Elevated levels of HbA1c are associated with increased adverse effects of diabetes, including end-stage renal disease, neuropathy, and infection.¹⁰ In a previous study, the risk for amputation was 1.2 times higher in patients with elevated HbA1c.¹¹ In contrast, our study suggested the odds of LEA versus not healing/not undergoing amputation decreased as HbA1c increased. As a patient’s HbA1c level increased by a value of 1, their odds for LEA decreased by 54.3%. This finding contradicts prior studies that have found a positive association between HbA1c and LEA risk, including a study where each percentage increase in HbA1c correlated with a 13% to 15% increased risk of LEA.¹² The finding that patients who underwent amputation in our study had lower levels of HbA1c and blood glucose cannot be fully explained. The maximum HbA1c value in the amputated group was 7.9%. The average values for healed patients and those who underwent LEA were 8.75% and 6.77%, respectively.

Blood glucose levels were also found to be the lowest in the amputated group in our study (mean, 149.29 mg/dL vs 163.19 mg/dL in the healed group). Similar results were found in a Brazilian study, in which patients who did not require amputation had higher HbA1c levels. This study also found an association between blood glucose levels above 200 mg/dL and amputations.³ These findings provide interesting opportunities for repeat studies, preferably with a larger number of participants.

Our study is limited by the small sample size. The sample population had to be reduced, as many patients were lost to follow-up. Although this paring down of the sample size can introduce bias, we are confident that our study is representative of the demographic of patients treated in our facility. The loss of patients to follow-up in turn caused the window of analysis to be narrowed, as long-term outcome data were not available. A multisite study observing various population samples can better explore the relationship between HbA1c and risk of amputation.

 

 

Conclusion

This retrospective study exploring factors associated with LEA was unique in that all our participants had 1 or more diabetic foot ulcerations, and thus already had an extremely high risk for amputation, in contrast to previous studies that followed persons at risk for developing diabetic foot ulcerations. In contrast to several previous studies, we found that the risk for amputation actually decreased as baseline measurements of HbA1c increased. The results of this study offer many opportunities for future investigations, preferably with a larger sample size. By further isolating and scrutinizing specific factors associated with LEA, researchers can help clinicians focus on providing wound care that promotes limb salvage.

Corresponding author: Alisha Oropallo, MD, MS, Northwell Health Comprehensive Wound Care Healing Center and Hyperbarics, 1999 Marcus Avenue, Suite M6, Lake Success, NY 11042; aoropallo@northwell.edu.

Financial disclosures: Funding for this research was provided by a multi-institutional AHRQ governmental grant.

From Northwell Health System, Lake Success, NY.

Abstract

• Objective: To explore factors associated with lower-extremity amputation (LEA) in patients with diabetic foot ulcers using data from the Online Wound Electronic Medical Record Database.
• Design: Retrospective analysis of medical records.
• Setting and participants: Data from 169 individuals with previously diagnosed diabetes mellitus who received wound care for a 6-month period within a span of 2 years was analyzed. A baseline evaluation was obtained and wound(s) were treated, managed, and monitored. Treatment continued until the patient healed, required an LEA, or phased out of the study, neither healing nor undergoing an amputation. Of the 149 patients who completed the study, 38 had healed ulcers, 14 underwent amputation, and 97 neither healed nor underwent an amputation. All patients were treated under the care of vascular and/or podiatric surgeons.
• Measurements: Variables included wound status (healed, amputated, and unhealed/non-amputated); size of wound area; age, gender, race, and ethnicity; white blood cell (WBC) count, hemoglobin A1c (HbA1c), blood glucose, and body mass index (BMI); and presence of osteomyelitis, gangrene, and peripheral vascular disease.
• Results: As compared to the healed and unhealed/non-amputated group, the group of patients who underwent LEA was older and had higher percentages of males, Hispanics, and African Americans; had a higher WBC count, larger wound area, and higher rates of wound infection, osteomyelitis, and neuropathy; and had lower average values of HbA1c, blood glucose, and BMI and a lower rate of peripheral vascular disease.
• Conclusion: The association between HbA1c and LEA highlights a window of relative safety among an at-risk population. By identifying and focusing on factors associated with LEA, health care professionals may be able to decrease the prevalence of LEA in patients with diabetes.

Keywords: diabetic foot ulcer; lower-extremity amputation; risk factors; HbA1c.

An estimated 30.3 million people, or 9.4% of the US population, has diabetes. In 2014, approximately 108,000 amputations were performed on adults with diagnosed diabetes.¹ Furthermore, patients with diabetes have a 10-fold increased risk for lower-extremity amputation (LEA), as compared with patients without diabetes.² The frequency of amputations in the diabetic population is a public health crisis.

Amputation has significant, life-altering consequences. Patients who undergo LEA often face debilitation in their daily activities and must undergo intense rehabilitation to learn basic tasks. Amputations can also impact individuals’ psychological well-being as they come to terms with their altered body and may face challenges in self-perception, confidence, self-esteem, work life, and relationships. In addition, the mortality rate for patients with diabetes 5 years after undergoing LEA is 30%.² However, public health studies estimate that more than half of LEAs in patients with diabetes are preventable.³

Although studies have explored the relationship between diabetes and LEA, few have sought to identify factors directly correlated with wound care. In the United States, patients with diabetic ulcerations are typically treated in wound care facilities; however, previous studies have concentrated on the conditions that lead to the formation of an ulcer or amputation, viewing amputation and ulcer as 2 separate entities. Our study took into account systemic variables, patient demographics, and specific wound characteristics to explore factors associated with LEA in a high-risk group of patients with diabetes. This study was designed to assess ailments that are prevalent in patients who require a LEA.

 

Methods

Patients and Setting

A total of 169 patients who were treated at the Comprehensive Wound Healing and Hyperbaric Center (Lake Success, NY), a tertiary facility of the Northwell Health system, participated in this retrospective study. The data for this study were obtained in conjunction with the development of the New York University School of Medicine’s Online Wound Electronic Medical Record to Decrease Limb Amputations in Persons with Diabetes (OWEMR) database. The OWEMR collects individual patient data from satellite locations across the country. Using this database, researchers can analyze similarities and differences between patients who undergo LEA.

This study utilized patient data specific to the Northwell Health facility. All of the patients in our study were enrolled under the criteria of the OWEMR database. In order to be included in the OWEMR database, patients had to be diagnosed with type 1 or type 2 diabetes; have a break in the skin ≥ 0.5 cm²; be 18 years of age or older; and have a measured hemoglobin A1c (HbA1c) value within the past 120 days. Study patients signed an informed consent and committed to being available for follow-up visits to the wound care facility for 6 months after entering the study. Patients were enrolled between 2012 and 2014, and each patient was monitored for a period of 6 months within this time period. Participants were treated with current standards of care using diet, lifestyle, and pharmacologic interventions. This study was approved by the Northwell Health System Institutional Review Board Human Research Protection Program (Manhasset, NY).

Data Collection

On their first visit to the facility, patients were given a physical examination and initial interview regarding their medical history. Clinicians were required to select 1 ulcer that would be examined for the duration of the study. The selection of the ulcer was based on a point system that awarded points for pedal pulses, the ability to be probed to the bone, the location of the ulcer (ie, located on the foot rather than a toe), and the presence of multiple ulcerations. The ulcer with the highest score was selected for the study. If numerous ulcers were evaluated with the same score, the largest and deepest was selected. Wagner classification of the wound was recorded at baseline and taken at each subsequent patient visit. In addition, peripheral sensation was assessed for signs of neuropathy using Semmes-Weinstein monofilament testing.

Once selected, the wound was clinically evaluated, samples for culture were obtained, and blood tests were performed to detect the presence of wound infection. The patient’s blood was drawn for a full laboratory analysis, including white blood cell (WBC) count and measurement of blood glucose and HbA1c levels. Bone biopsy, magnetic resonance imaging, and bone scans were used to detect the presence of osteomyelitis at the discretion of the health care provider. Wounds suspected of infection, underlying osteomyelitis, or gangrene at baseline were excluded. Patients would then return for follow-up visits at least once every 6 weeks, plus or minus 2 weeks, for a maximum of 6 months.

Statistical Analysis

Utilizing SAS version 9.3 (Cary, NC), descriptive statistics (minimum, maximum, mean, median, and SD) were calculated for the following variables: age, WBC count, wound area, HbA1c, blood glucose, and body mass index (BMI). These variables were collected for each patient as per the OWEMR protocol and provided a basis for which to compare patients who underwent amputation and those who did not. Twenty patients were lost to follow-up, and therefore we altered the window of our statistics from 6 months to 3 months to provide the most accurate data, as 6-month follow-up data were limited. The patients were classified into the following categories: healed, amputated, and unhealed/non-amputated. Descriptive statistics were calculated for these 3 groups, analyzing the same variables (age, WBC count, wound area, HbA1c, blood glucose, and BMI). Additional statistical computations were utilized in order to show the prevalence and frequency of our categorical variables: gender, race, ethnicity, osteomyelitis, gangrene, and peripheral vascular disease. The baseline values of WBC count, HbA1c, wound area, and BMI of the 3 groups were analyzed with descriptive statistics for comparison. A multinomial logistic regression was then performed using a 3-level outcome variable: healed, amputated, or unhealed/non-amputated. Each predictor variable was analyzed independently due to the small sample size.

 

Results

Of the 169 registered patients treated at the Northwell Health facility, all qualified for the OWEMR study and met the study criteria. In the original 169 patients, there were 19 amputations: 6 toe, 6 trans-metatarsal, 6 below knee, and 1 above knee (Table 1).

The descriptive statistics of 149 patients grouped into 3 categories (healed, amputated, unhealed/non-amputated) are shown in Table 2.

The results of the logistic regression exploring the differences between the amputation and healed groups and the unhealed/non-amputated group are shown in Table 3. The amputation group had a higher mean age and WBC count and greater wound area. Increased age was determined to be a significant predictor of the odds of amputation (P = 0.0089). For each year increase in age, the odds of amputation increased by 6.5% (odds ratio, 1.07 [95% confidence interval {CI}, 1.02-1.12]). Patients in the amputation group were more likely to be male, Hispanic, and African American and to have wound infections and comorbidities (osteomyelitis, neuropathy, and gangrene).

The presence of gangrene was significantly associated with LEA (P = 0.03). Specifically, the odds of patients without gangrene undergoing a LEA were substantially lower compared with their counterparts with gangrene (odds ratio, 0.17; 95% CI, 0.04-0.68; P = 0.0131). However, the presence of gangrene was not associated with the odds of healing compared with the odds of neither healing nor undergoing amputation (P = 0.84; not shown in Table 3).

The amputation group had lower mean values for HbA1c, BMI, and blood glucose levels and a lower rate of peripheral vascular disease. Only the relationship between lower HbA1c and increased odds of amputation versus not healing/non-amputation was found to be statistically significant (95% CI, 0.27-0.78; P = 0.009).

 

Discussion

This retrospective study was undertaken to evaluate factors associated with LEA in patients with diabetic foot ulcers. Patients with diabetes being treated at a wound care facility often require continuous surgical and metabolic intervention to promote optimal healing: drainage, surgical debridement, irrigation, culturing for infection, and monitoring of blood glucose levels. This treatment requires strict compliance with medical directions and, oftentimes, additional care, such as home-care nursing visits, to maintain a curative environment for the wound. Frequently, wounds on the lower extremity further complicate the healing process by reducing the patient’s mobility and daily life. Due to these factors, many patients progress to LEA. The link between diabetic ulcers and amputation has already been well described in previous studies, with studies showing that history of diabetic foot ulcer significantly predisposes an individual to LEA.⁴ However, few studies have further investigated demographic factors associated with risk for an amputation. Our study analyzed several categories of patient data taken from a baseline visit. We found that those with highly elevated HbA1c values were less likely to have an amputation than persons with relatively lower levels, a finding that is contrary to previous studies.

Our study’s findings suggest a higher risk for LEA with increased age. The amputation group was, on average, 7 years older than the other 2 groups. A recent study showed that risk for amputation is directly correlated to patient age, as is the mortality rate after undergoing LEA (2.3%; P < 0.05).⁵ Our study found that with each increase in age of 1 year, the odds of amputation increased by 6.5%. However, recent evidence on LEA risk and aging suggests that age is of less consequence than the duration of diabetes. One study found that the propensity to develop diabetic foot ulcers increases with the duration of diabetes.⁶ The same study found that prevalence of ulceration was correlated with age, but the relationship between age and LEA was less significant. A follow-up study for LEA could be done to examine the role of disease duration versus age in LEA.

A consensus among previous studies is that men have a higher risk for LEA.^5,7 Men comprised the majority in all 3 groups in our study. In addition, the amputation group in our study had the lowest BMI. Higher BMI generally is associated with an increased risk for health complications. However, a past study conducted in Taiwan reported that obese patients with diabetes were less likely to undergo LEA than those within the normal range for BMI.⁸ Neither study suggests that obesity is a deterrent for LEA, but both studies may suggest that risk of amputation may approach a maximum frequency at a specific BMI range, and then decrease. This unconfirmed “cyclic” relationship should be evaluated further in a larger sample size.

Most patients in our analysis were Caucasian, followed by African American and South Asian. African Americans were the only racial group with an increased frequency in the amputation group. This finding is supported by a previous study that found that the rate of LEA among patients with diabetes in low-income, predominantly African-American neighborhoods was nearly double that in wealthier, predominantly Caucasian areas.⁹ A potential problem in the comparison between our data with previous studies is that the studies did not analyze patients with our inclusion criteria. All patients with diabetes in previous investigations were grouped by race, but were not necessarily required to have 1 or more ulcers. Multiple ulcers may predispose an individual to a greater risk for amputation.

Multinomial logistic regression did not suggest an association between initial size of a patient’s wound and the risk of amputation. However, the descriptive data suggests a trend. Patients who did not heal or require an amputation had the largest average wound area. This finding is not surprising in that our study followed individuals for only 3 months. Many wounds require a long course of treatment, especially in patients with diabetes, who may have poor vascularization. However, in comparison to the healed patients, the patients who required an amputation had a larger average wound area. A larger wound requires a plentiful vascular supply for the delivery of clotting factors and nutrients to the damaged area. As wound size increases, an individual’s body must transmit an increased quantity of these factors and nutrients for the regeneration of tissue. In addition, wounds that possess a larger surface area require more debridement and present a greater opportunity for infection. This may also foreshadow a longer, more costly course of treatment. Additionally, individuals coping with large ulcerations are burdened by more elaborate and complex wound dressings.

Elevated levels of HbA1c are associated with increased adverse effects of diabetes, including end-stage renal disease, neuropathy, and infection.¹⁰ In a previous study, the risk for amputation was 1.2 times higher in patients with elevated HbA1c.¹¹ In contrast, our study suggested the odds of LEA versus not healing/not undergoing amputation decreased as HbA1c increased. As a patient’s HbA1c level increased by a value of 1, their odds for LEA decreased by 54.3%. This finding contradicts prior studies that have found a positive association between HbA1c and LEA risk, including a study where each percentage increase in HbA1c correlated with a 13% to 15% increased risk of LEA.¹² The finding that patients who underwent amputation in our study had lower levels of HbA1c and blood glucose cannot be fully explained. The maximum HbA1c value in the amputated group was 7.9%. The average values for healed patients and those who underwent LEA were 8.75% and 6.77%, respectively.

Blood glucose levels were also found to be the lowest in the amputated group in our study (mean, 149.29 mg/dL vs 163.19 mg/dL in the healed group). Similar results were found in a Brazilian study, in which patients who did not require amputation had higher HbA1c levels. This study also found an association between blood glucose levels above 200 mg/dL and amputations.³ These findings provide interesting opportunities for repeat studies, preferably with a larger number of participants.

Our study is limited by the small sample size. The sample population had to be reduced, as many patients were lost to follow-up. Although this paring down of the sample size can introduce bias, we are confident that our study is representative of the demographic of patients treated in our facility. The loss of patients to follow-up in turn caused the window of analysis to be narrowed, as long-term outcome data were not available. A multisite study observing various population samples can better explore the relationship between HbA1c and risk of amputation.

 

 

Conclusion

This retrospective study exploring factors associated with LEA was unique in that all our participants had 1 or more diabetic foot ulcerations, and thus already had an extremely high risk for amputation, in contrast to previous studies that followed persons at risk for developing diabetic foot ulcerations. In contrast to several previous studies, we found that the risk for amputation actually decreased as baseline measurements of HbA1c increased. The results of this study offer many opportunities for future investigations, preferably with a larger sample size. By further isolating and scrutinizing specific factors associated with LEA, researchers can help clinicians focus on providing wound care that promotes limb salvage.

Corresponding author: Alisha Oropallo, MD, MS, Northwell Health Comprehensive Wound Care Healing Center and Hyperbarics, 1999 Marcus Avenue, Suite M6, Lake Success, NY 11042; aoropallo@northwell.edu.

Financial disclosures: Funding for this research was provided by a multi-institutional AHRQ governmental grant.

From Northwell Health System, Lake Success, NY.

Abstract

• Objective: To explore factors associated with lower-extremity amputation (LEA) in patients with diabetic foot ulcers using data from the Online Wound Electronic Medical Record Database.
• Design: Retrospective analysis of medical records.
• Setting and participants: Data from 169 individuals with previously diagnosed diabetes mellitus who received wound care for a 6-month period within a span of 2 years was analyzed. A baseline evaluation was obtained and wound(s) were treated, managed, and monitored. Treatment continued until the patient healed, required an LEA, or phased out of the study, neither healing nor undergoing an amputation. Of the 149 patients who completed the study, 38 had healed ulcers, 14 underwent amputation, and 97 neither healed nor underwent an amputation. All patients were treated under the care of vascular and/or podiatric surgeons.
• Measurements: Variables included wound status (healed, amputated, and unhealed/non-amputated); size of wound area; age, gender, race, and ethnicity; white blood cell (WBC) count, hemoglobin A1c (HbA1c), blood glucose, and body mass index (BMI); and presence of osteomyelitis, gangrene, and peripheral vascular disease.
• Results: As compared to the healed and unhealed/non-amputated group, the group of patients who underwent LEA was older and had higher percentages of males, Hispanics, and African Americans; had a higher WBC count, larger wound area, and higher rates of wound infection, osteomyelitis, and neuropathy; and had lower average values of HbA1c, blood glucose, and BMI and a lower rate of peripheral vascular disease.
• Conclusion: The association between HbA1c and LEA highlights a window of relative safety among an at-risk population. By identifying and focusing on factors associated with LEA, health care professionals may be able to decrease the prevalence of LEA in patients with diabetes.

Keywords: diabetic foot ulcer; lower-extremity amputation; risk factors; HbA1c.

An estimated 30.3 million people, or 9.4% of the US population, has diabetes. In 2014, approximately 108,000 amputations were performed on adults with diagnosed diabetes.¹ Furthermore, patients with diabetes have a 10-fold increased risk for lower-extremity amputation (LEA), as compared with patients without diabetes.² The frequency of amputations in the diabetic population is a public health crisis.

Amputation has significant, life-altering consequences. Patients who undergo LEA often face debilitation in their daily activities and must undergo intense rehabilitation to learn basic tasks. Amputations can also impact individuals’ psychological well-being as they come to terms with their altered body and may face challenges in self-perception, confidence, self-esteem, work life, and relationships. In addition, the mortality rate for patients with diabetes 5 years after undergoing LEA is 30%.² However, public health studies estimate that more than half of LEAs in patients with diabetes are preventable.³

Although studies have explored the relationship between diabetes and LEA, few have sought to identify factors directly correlated with wound care. In the United States, patients with diabetic ulcerations are typically treated in wound care facilities; however, previous studies have concentrated on the conditions that lead to the formation of an ulcer or amputation, viewing amputation and ulcer as 2 separate entities. Our study took into account systemic variables, patient demographics, and specific wound characteristics to explore factors associated with LEA in a high-risk group of patients with diabetes. This study was designed to assess ailments that are prevalent in patients who require a LEA.

 

Methods

Patients and Setting

A total of 169 patients who were treated at the Comprehensive Wound Healing and Hyperbaric Center (Lake Success, NY), a tertiary facility of the Northwell Health system, participated in this retrospective study. The data for this study were obtained in conjunction with the development of the New York University School of Medicine’s Online Wound Electronic Medical Record to Decrease Limb Amputations in Persons with Diabetes (OWEMR) database. The OWEMR collects individual patient data from satellite locations across the country. Using this database, researchers can analyze similarities and differences between patients who undergo LEA.

This study utilized patient data specific to the Northwell Health facility. All of the patients in our study were enrolled under the criteria of the OWEMR database. In order to be included in the OWEMR database, patients had to be diagnosed with type 1 or type 2 diabetes; have a break in the skin ≥ 0.5 cm²; be 18 years of age or older; and have a measured hemoglobin A1c (HbA1c) value within the past 120 days. Study patients signed an informed consent and committed to being available for follow-up visits to the wound care facility for 6 months after entering the study. Patients were enrolled between 2012 and 2014, and each patient was monitored for a period of 6 months within this time period. Participants were treated with current standards of care using diet, lifestyle, and pharmacologic interventions. This study was approved by the Northwell Health System Institutional Review Board Human Research Protection Program (Manhasset, NY).

Data Collection

On their first visit to the facility, patients were given a physical examination and initial interview regarding their medical history. Clinicians were required to select 1 ulcer that would be examined for the duration of the study. The selection of the ulcer was based on a point system that awarded points for pedal pulses, the ability to be probed to the bone, the location of the ulcer (ie, located on the foot rather than a toe), and the presence of multiple ulcerations. The ulcer with the highest score was selected for the study. If numerous ulcers were evaluated with the same score, the largest and deepest was selected. Wagner classification of the wound was recorded at baseline and taken at each subsequent patient visit. In addition, peripheral sensation was assessed for signs of neuropathy using Semmes-Weinstein monofilament testing.

Once selected, the wound was clinically evaluated, samples for culture were obtained, and blood tests were performed to detect the presence of wound infection. The patient’s blood was drawn for a full laboratory analysis, including white blood cell (WBC) count and measurement of blood glucose and HbA1c levels. Bone biopsy, magnetic resonance imaging, and bone scans were used to detect the presence of osteomyelitis at the discretion of the health care provider. Wounds suspected of infection, underlying osteomyelitis, or gangrene at baseline were excluded. Patients would then return for follow-up visits at least once every 6 weeks, plus or minus 2 weeks, for a maximum of 6 months.

Statistical Analysis

Utilizing SAS version 9.3 (Cary, NC), descriptive statistics (minimum, maximum, mean, median, and SD) were calculated for the following variables: age, WBC count, wound area, HbA1c, blood glucose, and body mass index (BMI). These variables were collected for each patient as per the OWEMR protocol and provided a basis for which to compare patients who underwent amputation and those who did not. Twenty patients were lost to follow-up, and therefore we altered the window of our statistics from 6 months to 3 months to provide the most accurate data, as 6-month follow-up data were limited. The patients were classified into the following categories: healed, amputated, and unhealed/non-amputated. Descriptive statistics were calculated for these 3 groups, analyzing the same variables (age, WBC count, wound area, HbA1c, blood glucose, and BMI). Additional statistical computations were utilized in order to show the prevalence and frequency of our categorical variables: gender, race, ethnicity, osteomyelitis, gangrene, and peripheral vascular disease. The baseline values of WBC count, HbA1c, wound area, and BMI of the 3 groups were analyzed with descriptive statistics for comparison. A multinomial logistic regression was then performed using a 3-level outcome variable: healed, amputated, or unhealed/non-amputated. Each predictor variable was analyzed independently due to the small sample size.

 

Results

Of the 169 registered patients treated at the Northwell Health facility, all qualified for the OWEMR study and met the study criteria. In the original 169 patients, there were 19 amputations: 6 toe, 6 trans-metatarsal, 6 below knee, and 1 above knee (Table 1).

The descriptive statistics of 149 patients grouped into 3 categories (healed, amputated, unhealed/non-amputated) are shown in Table 2.

The results of the logistic regression exploring the differences between the amputation and healed groups and the unhealed/non-amputated group are shown in Table 3. The amputation group had a higher mean age and WBC count and greater wound area. Increased age was determined to be a significant predictor of the odds of amputation (P = 0.0089). For each year increase in age, the odds of amputation increased by 6.5% (odds ratio, 1.07 [95% confidence interval {CI}, 1.02-1.12]). Patients in the amputation group were more likely to be male, Hispanic, and African American and to have wound infections and comorbidities (osteomyelitis, neuropathy, and gangrene).

The presence of gangrene was significantly associated with LEA (P = 0.03). Specifically, the odds of patients without gangrene undergoing a LEA were substantially lower compared with their counterparts with gangrene (odds ratio, 0.17; 95% CI, 0.04-0.68; P = 0.0131). However, the presence of gangrene was not associated with the odds of healing compared with the odds of neither healing nor undergoing amputation (P = 0.84; not shown in Table 3).

The amputation group had lower mean values for HbA1c, BMI, and blood glucose levels and a lower rate of peripheral vascular disease. Only the relationship between lower HbA1c and increased odds of amputation versus not healing/non-amputation was found to be statistically significant (95% CI, 0.27-0.78; P = 0.009).

 

Discussion

This retrospective study was undertaken to evaluate factors associated with LEA in patients with diabetic foot ulcers. Patients with diabetes being treated at a wound care facility often require continuous surgical and metabolic intervention to promote optimal healing: drainage, surgical debridement, irrigation, culturing for infection, and monitoring of blood glucose levels. This treatment requires strict compliance with medical directions and, oftentimes, additional care, such as home-care nursing visits, to maintain a curative environment for the wound. Frequently, wounds on the lower extremity further complicate the healing process by reducing the patient’s mobility and daily life. Due to these factors, many patients progress to LEA. The link between diabetic ulcers and amputation has already been well described in previous studies, with studies showing that history of diabetic foot ulcer significantly predisposes an individual to LEA.⁴ However, few studies have further investigated demographic factors associated with risk for an amputation. Our study analyzed several categories of patient data taken from a baseline visit. We found that those with highly elevated HbA1c values were less likely to have an amputation than persons with relatively lower levels, a finding that is contrary to previous studies.

Our study’s findings suggest a higher risk for LEA with increased age. The amputation group was, on average, 7 years older than the other 2 groups. A recent study showed that risk for amputation is directly correlated to patient age, as is the mortality rate after undergoing LEA (2.3%; P < 0.05).⁵ Our study found that with each increase in age of 1 year, the odds of amputation increased by 6.5%. However, recent evidence on LEA risk and aging suggests that age is of less consequence than the duration of diabetes. One study found that the propensity to develop diabetic foot ulcers increases with the duration of diabetes.⁶ The same study found that prevalence of ulceration was correlated with age, but the relationship between age and LEA was less significant. A follow-up study for LEA could be done to examine the role of disease duration versus age in LEA.

A consensus among previous studies is that men have a higher risk for LEA.^5,7 Men comprised the majority in all 3 groups in our study. In addition, the amputation group in our study had the lowest BMI. Higher BMI generally is associated with an increased risk for health complications. However, a past study conducted in Taiwan reported that obese patients with diabetes were less likely to undergo LEA than those within the normal range for BMI.⁸ Neither study suggests that obesity is a deterrent for LEA, but both studies may suggest that risk of amputation may approach a maximum frequency at a specific BMI range, and then decrease. This unconfirmed “cyclic” relationship should be evaluated further in a larger sample size.

Most patients in our analysis were Caucasian, followed by African American and South Asian. African Americans were the only racial group with an increased frequency in the amputation group. This finding is supported by a previous study that found that the rate of LEA among patients with diabetes in low-income, predominantly African-American neighborhoods was nearly double that in wealthier, predominantly Caucasian areas.⁹ A potential problem in the comparison between our data with previous studies is that the studies did not analyze patients with our inclusion criteria. All patients with diabetes in previous investigations were grouped by race, but were not necessarily required to have 1 or more ulcers. Multiple ulcers may predispose an individual to a greater risk for amputation.

Multinomial logistic regression did not suggest an association between initial size of a patient’s wound and the risk of amputation. However, the descriptive data suggests a trend. Patients who did not heal or require an amputation had the largest average wound area. This finding is not surprising in that our study followed individuals for only 3 months. Many wounds require a long course of treatment, especially in patients with diabetes, who may have poor vascularization. However, in comparison to the healed patients, the patients who required an amputation had a larger average wound area. A larger wound requires a plentiful vascular supply for the delivery of clotting factors and nutrients to the damaged area. As wound size increases, an individual’s body must transmit an increased quantity of these factors and nutrients for the regeneration of tissue. In addition, wounds that possess a larger surface area require more debridement and present a greater opportunity for infection. This may also foreshadow a longer, more costly course of treatment. Additionally, individuals coping with large ulcerations are burdened by more elaborate and complex wound dressings.

Elevated levels of HbA1c are associated with increased adverse effects of diabetes, including end-stage renal disease, neuropathy, and infection.¹⁰ In a previous study, the risk for amputation was 1.2 times higher in patients with elevated HbA1c.¹¹ In contrast, our study suggested the odds of LEA versus not healing/not undergoing amputation decreased as HbA1c increased. As a patient’s HbA1c level increased by a value of 1, their odds for LEA decreased by 54.3%. This finding contradicts prior studies that have found a positive association between HbA1c and LEA risk, including a study where each percentage increase in HbA1c correlated with a 13% to 15% increased risk of LEA.¹² The finding that patients who underwent amputation in our study had lower levels of HbA1c and blood glucose cannot be fully explained. The maximum HbA1c value in the amputated group was 7.9%. The average values for healed patients and those who underwent LEA were 8.75% and 6.77%, respectively.

Blood glucose levels were also found to be the lowest in the amputated group in our study (mean, 149.29 mg/dL vs 163.19 mg/dL in the healed group). Similar results were found in a Brazilian study, in which patients who did not require amputation had higher HbA1c levels. This study also found an association between blood glucose levels above 200 mg/dL and amputations.³ These findings provide interesting opportunities for repeat studies, preferably with a larger number of participants.

Our study is limited by the small sample size. The sample population had to be reduced, as many patients were lost to follow-up. Although this paring down of the sample size can introduce bias, we are confident that our study is representative of the demographic of patients treated in our facility. The loss of patients to follow-up in turn caused the window of analysis to be narrowed, as long-term outcome data were not available. A multisite study observing various population samples can better explore the relationship between HbA1c and risk of amputation.

 

 

Conclusion

This retrospective study exploring factors associated with LEA was unique in that all our participants had 1 or more diabetic foot ulcerations, and thus already had an extremely high risk for amputation, in contrast to previous studies that followed persons at risk for developing diabetic foot ulcerations. In contrast to several previous studies, we found that the risk for amputation actually decreased as baseline measurements of HbA1c increased. The results of this study offer many opportunities for future investigations, preferably with a larger sample size. By further isolating and scrutinizing specific factors associated with LEA, researchers can help clinicians focus on providing wound care that promotes limb salvage.

Corresponding author: Alisha Oropallo, MD, MS, Northwell Health Comprehensive Wound Care Healing Center and Hyperbarics, 1999 Marcus Avenue, Suite M6, Lake Success, NY 11042; aoropallo@northwell.edu.

Financial disclosures: Funding for this research was provided by a multi-institutional AHRQ governmental grant.

From the Fraser Health Authority, Surrey, British Columbia, Canada.

Abstract

• Objective: To study care outcomes associated with a network of hospitalist services compared to traditional providers.
• Design: Retrospective review of administrative data.
• Setting and participants: Patients from a large integrated health care system in British Columbia in western Canada admitted and cared for by 3 provider groups between April 1, 2012, and March 31, 2018: hospitalists, family physicians (FP), and internal medicine (IM) physicians:
• Measurements: Average total length of stay (LOS), 30-day readmission, in-hospital mortality, and hospital standardized mortality ratio (HSMR) were the study outcome measures. Multiple logistic regression or generalized regression were completed to determine the relationship between provider groups and outcomes.
• Results: A total of 248,412 hospitalizations were included. Compared to patients admitted to hospitalists, patients admitted to other providers had higher odds of mortality (odds ratio [OR] for FP, 1.29; 95% confidence interval [CI], 1.21-1.37; OR for IM, 1.24; 95% CI, 1.15-1.33). Compared to hospitalist care, FP care was associated with higher readmission (OR, 1.27; 95% CI, 1.22-1.33), while IM care showed lower odds of readmission (OR, 0.83; 95% CI, 0.79-0.87). Patients admitted to the IM group had significantly lower total LOS (mean, 5.13 days; 95% CI, 5.04-5.21) compared to patients admitted to hospitalists (mean, 7.37 days; CI, 7.26-7.49) and FPs (mean, 7.30 days; 95% CI, 7.19-7.41). In a subgroup analysis of patients presenting with congestive heart failure, chronic obstructive pulmonary disease, and pneumonia, these general tendencies broadly persisted for mortality and LOS comparisons between FPs and hospitalists, but results were mixed for hospital readmissions.
• Conclusion: Care provided by hospitalists was associated with lower mortality and readmission rates compared with care provided by FPs, despite similar LOS. These findings may reflect differences in volume of services delivered by individual physicians, on-site availability to address urgent medical issues, and evolving specialization of clinical and nonclinical care processes in the acute care setting.

Keywords: hospital medicine; length of stay; readmission; mortality.

The hospitalist model of care has undergone rapid growth globally in recent years.¹ The first hospitalist programs in Canada began around the same time as those in the United States and share many similarities in design and operations with their counterparts.^2-4 However, unlike in the United States, where the hospitalist model has successfully established itself as an emerging specialty, debates about the merits of the model and its value proposition continue among Canadian observers.^5-9

Historically, the type of physicians who acted as the most responsible provider (MRP) in Canadian hospitals depended on setting and geography.¹⁰ In large urban areas, groups of general internists or specialists have historically looked after general medicine patients as part of university-affiliated teaching services.^11,12 Patients admitted to community hospitals have traditionally been cared for by their own primary care providers, typically general practitioners or family physicians (FPs). In the mid-1990s, many primary care providers in urban centers began to withdraw from inpatient care and primarily focused their practices in the outpatient setting.^13-15 Hospitalist programs emerged as health care administrators sought to fill the resulting gap in MRP coverage.^2,10

To date, attempts to understand the impact of hospitalist programs in Canada have been limited. A number of early studies aimed to describe¹⁶ the role of hospitalists in Canada and suggested improvements in length of stay (LOS) and staff satisfaction.¹⁷ However, these studies relied on unadjusted before-after comparisons and lacked methodological rigor to draw robust conclusions. More recently, a few studies have evaluated care outcomes associated with hospitalists using administrative databases, which attempted to control for potential confounding factors.^18-21

While these studies are beginning to shed some light on the impact of hospital medicine programs in Canada, there are a number of issues that limit their generalizability. For example, the majority of studies to date focus on hospital medicine programs in Canada’s largest province (Ontario), and most describe experiences from single institutions. Since each of the 13 provincial and territorial governments organizes its health care system differently,²² results from 1 province may not be generalizable to other parts of the country. Moreover, hospitalists in Ontario are more diverse in their training backgrounds, with a larger percentage having trained in general internal medicine (IM), as compared to other parts of Canada, where the majority of hospitalists are overwhelmingly trained as FPs.³

We aimed to study care outcomes associated with a network of hospitalist services compared to “traditional” providers (community-based FPs and IM specialists) in a large integrated health care system in the province of British Columbia in western Canada. The hospital medicine services in this network span a range of community and academic hospitals, and collectively constitute 1 of the largest regional programs in the country. This provides a unique opportunity to understand the impact of hospitalists on outcome measures across a range of acute care institutions.

 

 

Methods

Setting and Population

Fraser Health Authority is 1 of 5 regional health authorities in British Columbia that emerged in 2001.^23,24 It operates a network of hospitalist programs in 10 of its 12 acute care hospitals. In addition to hospitalists, there are a variable number of “traditional” physician providers who continue to act as MRPs. These include community-based FPs who continue to see their own patients in the hospital, either as part of a solo-practice model or a clinic-based call group. There are also a number of general internists and other subspecialists who accept MRP roles for general medicine patients who may present with higher-acuity conditions. As a result, patients requiring hospitalization due to nonsurgical or noncritical care conditions at each Fraser Health hospital may be cared for by a physician belonging to 1 of 3 groups, depending on local circumstances: an FP, a hospitalist, or an internist.

Inclusion and Exclusion Criteria

In order to evaluate comparative outcomes associated with hospitalist care, we included all patients admitted to a physician in each of the 3 provider groups between April 1, 2012, and March 31, 2018. We chose this time period for 2 reasons: first, we wanted to ensure comparability over an extended period of time, given the methodological changes implemented in 2009 by the Canadian Institute for Health Information (CIHI), the federal organization in the country responsible for setting standards for health care measures.²⁵ Second, previous internal reviews had suggested that data quality prior to this year was inconsistent. We only considered hospitalizations where patients were admitted to and discharged by the same service, and excluded 2 acute care facilities and 1 free-standing rehabilitation facility without a hospitalist service during this period. We also excluded patients who resided in a location beyond the geographic catchment area of Fraser Health. Further details about data collection are outlined in the Appendix.

Measures

We used the framework developed by White and Glazier²⁶ to inform the selection of our outcome measures, as well as relevant variables that may impact them. This framework proposes that the design of the inpatient care model (structures and processes of care) directly affects care outcomes. The model also proposes that patient and provider attributes can modulate this relationship, and suggests that a comprehensive evaluation of hospitalist performance needs to take these factors into account. We identified average total LOS, 30-day readmission rate, in-hospital mortality, and hospital standardized mortality ratio (HSMR)²⁷ as primary outcome measures. HSMR is defined as actual over expected mortality and is measured by CIHI through a formula that takes into account patient illness attributes (eg, the most responsible diagnosis, comorbidity levels) and baseline population mortality rates.²⁷ We chose these measures because they are clinically relevant and easy to obtain and have been utilized in previous similar studies in Canada and the United States.^18-21,26

Statistical Analysis

Baseline demographic and clinical differences in patient outcomes were examined using independent t-tests or chi-square tests. Furthermore, baseline differences based on provider groups were explored using analysis of variance or chi-square tests. Multiple logistic regression analyses were completed to determine the relationship between provider groups and readmission and mortality, while the relationship between provider groups and hospital LOS was determined with generalized linear regression (using gamma distribution and a log link). Gamma distribution with a log link analysis is appropriate with outcome measures that are positively skewed (eg, hospital LOS). It assumes that data are sampled from an exponential family of distributions, thus mimicking a log-normal distribution, and minimizes estimation bias and standard errors. These analyses were completed while controlling for the effects of age, gender, and other potential confounding factors.

We initially attempted to control for case mix by incorporating case-mix groups (CMGs) in our multivariate analysis. However, we identified 475 CMGs with at least 1 patient in our study population. We then explored the inclusion of major clinical categories (MCCs) that broadly group CMGs into various higher order/organ-system level categories (eg, diseases of the respiratory system); however, we could not aggregate them into sufficiently homogenous groups to be entered into regression models. Instead, we conducted subgroup analyses on patients in our study population who were hospitalized with 1 of the following 3 CMGs: chronic obstructive pulmonary disease (COPD, n = 11,404 patients), congestive heart failure without coronary angiography (CHF, n = 7680), and pneumonia (itself an aggregate of 3 separate CMGs: aspiration pneumonia, bacterial pneumonia, viral/unspecified pneumonia, n = 11,155). We chose these CMGs as they are among the top 8 presentations for all 3 provider groups.

For all outcome measures, we excluded atypical patients (defined by CIHI as those with atypically long stays) and patients who had been transferred between facilities. For the readmission analysis, we also excluded patients who died in the hospital (Appendix A). Data analyses were completed in IBM SPSS, version 21. For all analyses, significance was determined using 2-tailed test and alpha < 0.05.

Ethics

The Fraser Health Department of Research and Evaluation reviewed this project to determine need for formal Ethics Review Board review, and granted an exemption based on institutional guidelines for program evaluations.

 

Results

A total of 132,178 patients were admitted to and discharged by 1 of the 3 study provider groups during the study period, accounting for a total of 248,412 hospitalizations. After excluding patients cared for in Fraser Health facilities without a hospitalist service and those who resided in a geographic area beyond Fraser Health, a total of 224,214 admissions were included in the final analysis.

Patient Characteristics

The demographic and clinical characteristics of patients by provider group are summarized in Table 1. Patients admitted to IM providers were substantially younger than those admitted to either FPs or hospitalists (61.00 vs 70.86 and 71.22 years, respectively; P < 0.005). However, patients admitted to hospitalists had higher degrees of complexity (as measured by higher comorbidity levels, number of secondary diagnoses, and higher resource intensity weights [RIWs]; P < 000.1 for all comparisons). Overall, the most common CMGs seen by FPs and hospitalists were similar, while IM providers primarily saw patients with cardiac conditions (Table 2).

Trends Over Time

During the study period, the number of patients admitted to the hospitalist services increased by 24%, while admissions to FPs and IM providers declined steadily (Figure). During this time, LOS for hospitalists progressively declined, while LOS for FPs and IM providers increased. Similar trends were observed for measures of mortality, while readmission rates remained constant for FPs, despite a decline observed for other providers.

 

 

Mortality

Table 3 summarizes the relationship between provider groups and in-hospital mortality (n = 183,779). Controlling for other variables, patients admitted to FP and IM providers had higher odds of mortality when compared to hospitalists (odds ratio [OR] for FPs, 1.29; 95% confidence interval [CI], 1.21-1.37; OR for IM, 1.24; 95% CI, 1.15-1.33). Older age, higher comorbidity level, higher number of secondary diagnoses, higher use of hospital resources (as measured by RIWs), longer than expected hospital stay (as measured by conservable days), and male gender were also associated with higher mortality. Similarly, patients receiving palliative care and those who spent at least 1 day in a special care unit (critical care, observation, and monitored care units) also had higher odds of mortality. On the other hand, admission to nonteaching medium facilities and longer hospital stay were associated with lower mortality. Compared to the first year of this analysis, lower mortality rates were observed in subsequent fiscal years. Finally, there appear to be geographic variations in mortality within Fraser Health.

Our analysis of patients with COPD, CHF, and pneumonia showed mixed results (Table 4). Patients admitted to the FP provider group with CHF and pneumonia had higher mortality compared to hospitalists (OR for CHF, 1.77; 95% CI, 1.38-2.27; OR for pneumonia, 1.53; 95% CI, 1.25-1.88), with a similar but nonstatistically significant trend observed for patients with COPD (OR, 1.29; 95% CI, 0.99-1.70). On the other hand, the higher observed mortality associated with the IM provider group in the overall study population only persisted for patients with COPD (OR, 2.71; 95% CI, 1.94-3.80), with no statistically significant differences for patients with CHF (OR, 1.18; 95% CI, 0.84-1.65) and pneumonia (OR, 0.93; 95% CI, 0.69-1.25).

We also studied adjusted mortality as measured by HSMRs. Currently, our Health Information Management system calculates an HSMR value for each patient admitted to our acute care facilities using the methodology developed by CIHI. Prior internal audits demonstrated that our internal calculations closely approximate those reported nationally. Our analysis suggests that over time, HSMR rates for the 3 provider groups have diverged, with patients admitted to IM providers having a higher mortality rate than what would be expected based on the presenting clinical conditions and comorbidity levels (Figure, part D).

Readmission

The results of our multiple logistic regression for readmission are summarized in Table 5 (n = 166,042). The impact of provider group on 30-day readmission is mixed, with higher odds associated with FPs compared to hospitalists (OR, 1.27; 95% CI, 1.22-1.34) and lower odds associated with IM physicians (OR, 0.83; 95% CI, 0.79-0.87). Gender and RIW did not show any significant associations, but increasing age, higher number of secondary diagnoses, higher comorbidity levels, and longer than expected LOS (as measure by conservable days) were associated with higher odds of readmission. Conversely, longer hospitalization, admission to a large community hospital, palliative status, admission to a special care unit, geography, and fiscal year were associated with lower odds of readmission.

The above differences between provider groups were no longer consistently present when we analyzed patients presenting with COPD, CHF, and pneumonias (Table 6). Only patients admitted to the FP provider group with pneumonia had higher odds of readmission compared to hospitalists (OR, 1.27; 95% CI, 1.05-1.54). Conversely, only patients admitted to the IM provider group with CHF showed lower readmission (OR, 0.75; 95% CI, 0.62-0.92).

 

 

Total LOS

Results using generalized linear regressions for total LOS are presented in Table 7 (n = 183,779). Patients admitted to the IM provider group had significantly lower total LOS (mean, 5.13 days; 95% CI, 5.04-5.21) compared to the hospitalist (mean, 7.37 days; 95% CI, 7.26-7.49) and FP (mean, 7.30 days; 95% CI, 7.19-7.41) groups, with no significant differences between the latter 2 groups. Older patients, females, patients with higher comorbidity levels or number of secondary diagnoses, higher RIW, palliative patients, and discharge to a facility other than the patient’s home were associated with a significantly longer LOS. On the other hand, admission to nonteaching hospitals and admission to a special care unit was associated with lower LOS.

When we compared total LOS for patients admitted with COPD, CHF, and pneumonias, the same differences observed for the broader comparisons persisted: IM patients consistently showed shorter LOS compared to hospitalist patients, while LOS associated with FP patients was similar (Table 8).

Discussion

To our knowledge, our evaluation is the largest study to date designed to understand outcomes associated with hospitalist care in Canada. Our analyses suggest that patients admitted to our large network of hospitalist services present with clinical conditions that are very similar to those of general medicine patients in other Canadian provinces.^28,29 They also show that patients cared for by hospitalists experience lower mortality rates compared to those cared for by FPs. Our findings are similar to previous studies, which have suggested a 12% to 75% reduction in odds of mortality associated with hospitalist care.^18,19 These differences persisted even when we focused on patients presenting with specific clinical conditions (CHF, COPD, and pneumonias).

 

White and colleagues have previously demonstrated that generalist physicians who had higher volumes of inpatient care activity also had lower mortality rates compared to those who cared for hospitalized patients less frequently.¹⁹ An association between higher physician caseloads and better outcomes has been established for many surgical and medical conditions.^30-32 Given that 85% of hospitalists in our program have post-graduate medical training in family medicine (internal department surveys, data not shown), it is less likely that training background can explain differences in outcomes. Instead, differences in patient volumes and the dedicated focus of hospitalists on acute care are likely more important contributors to lower mortality. In our program, a full-time hospitalist spends an average of 2000 hours annually providing services in the hospital setting. The continuous on-site presence of hospitalists enhances their clinical experience with regards to the management of common medical conditions, and increases their exposure to less common presentations of illnesses. The ability to respond to deteriorating patients in a timely manner may be another factor in explaining the differences in mortality rates between dedicated hospital-based generalist providers and similarly trained physicians with a primarily community-based focus.

In our study, hospitalist care was also broadly associated with lower mortality compared to the IM providers, although these differences were not consistently present when patients with specific diagnoses were compared. This may be partly explained by the relationship between caseload and outcomes, but other factors may also be important. For example, patients admitted by IM providers spend significantly more time in specialized units. They also predominantly present with cardiac conditions, and as such may have higher acuity levels and require more invasive interventions. While this may explain the higher observed mortality, a within-group comparison still suggests higher than expected mortality for IM patients. The HSMR methodology measures actual mortality rates compared to what would be expected based on clinical presentation and baseline population characteristics. Calculating HSMR is highly dependent on proper documentation and chart abstraction,^33,34 and it is possible that some of the differences observed are due to incomplete physician documentation. However, a more in-depth analysis of care processes will be required to clarify the observed trends.

Compared to hospitalists, patients cared for by FPs also had higher odds of readmission within 30 days, which is consistent with prior studies.^18,19 One of the criticisms of the hospitalist model has been the inherent discontinuity of care that is built into the model, which can contribute to suboptimal transitions of care between the acute and community settings.³⁵ The expectation is that FPs who admit their own patients do not face this challenge, and as a result their patients should be readmitted less frequently after discharge. Our data and those from previous studies do not support this hypothesis. At the same time, when we studied patients with specific clinical diagnoses, only those hospitalized for pneumonias continued to demonstrate higher readmission odds. This suggests that hospital readmission rate is a complex measure that may be influenced by a multitude of hospital and community factors, and may be different for patients who present with different clinical diagnoses. Further research is required to better understand the relationship between provider type and experience with hospital readmission for patients with various clinical presentations.

Unlike the United States, where hospitalist care has been associated with reductions in LOS,^26,36 studies in the Canadian health care setting have shown mixed results.^17-21 In our evaluation, hospitalist care is not associated with reductions in total LOS compared to care provided by FPs or IM physicians. This could be due to a number of factors. First, unlike FPs, who know their patients, hospitalists may have a more conservative risk tolerance in discharging patients with whom they are not familiar. Similarly, physicians who have trained in IM may have a lower threshold for discharging patients than hospitalists, whose training background is mainly rooted in family medicine.³ Second, discontinuity of care has been associated with longer LOS for hospitalized patients.^37,38 Hospitalists generally work for 7- to 10-day rotations. As a result, a patient may see a number of different hospitalists during the same hospital stay, which could nullify any gains in LOS that may be expected from better familiarity with hospital processes. Third, whereas a FP or an internist may only have a few inpatients under their care at any given time, each hospitalist typically cares for 17 to 22 patients every day. Increasing hospitalist workload has been shown to negatively impact LOS and may result in lower efficiency.³⁹ Finally, many patients in our health system who require more time to recuperate or need complex discharge planning are usually transferred to the care of the hospitalist service from other services, or are preferentially admitted to hospitalists from the emergency department. As a result, hospitalists may look after a disproportionately higher number of long-stay patients. Despite all this, hospitalists in our population perform similarly to FPs, regardless of the clinical diagnoses of hospitalized patients.

 

Our study has a number of notable limitations. First, we used administrative data to conduct our evaluation and could only control for factors that are available in our data systems. As a result, some potential confounders may not have been taken into consideration. For example, our databases do not contain provider characteristics (eg, age, years of clinical experience) that have been deemed to be relevant by White and Glazier.²⁶ Similarly, we did not have all the necessary information about the characteristics of the various MRP programs (eg, number of physicians involved in group practices, the schedule model of community FP call groups) and were not able to account for the potential impact of these on observed outcomes. Second, although our findings mirror prior studies from other parts of Canada, they may not be applicable to hospitalist programs in other jurisdictions or in health systems that are not regionalized or integrated. Third, our IM provider group is heterogeneous, with a number of different IM subspecialties (cardiologists, gastroenterologists, general internists) grouped under the IM category in our database. As a result, comparisons between the IM provider group and the other 2 provider groups, which are more homogenous, should be interpreted with caution.

Finally, we included only patients admitted to facilities in which a hospitalist service existed during the study period. As a result, a medium-size community hospital without a hospitalist service where patients are cared for exclusively by FPs and IM physicians was not included in the comparisons, and in 4 of the 10 facilities included, the number of FP patients was less than 10% of total hospitalized patients at the site (Appendix A). This may have resulted in an under-representation of FP patients.

Conclusion

Debates about the merits of the hospitalist model in Canada continue, and are in part fueled by a paucity of robust evidence about its impact on care outcomes compared to more traditional ways of providing inpatient care. In our evaluation, care provided by hospitalists is associated with lower mortality and readmission rates, despite similar LOS compared with FPs. Hospitalist care is also associated with lower mortality compared to IM providers. Hospitalists also demonstrated progressive improvement over time, with decreasing LOS and mortality rates and a stable readmission rate. Our results suggest that physicians with a focus on inpatient care can have positive contributions to quality and efficiency of care in Canada.

Corresponding author: Vandad Yousefi MD, CCFP, FHM, Fraser Health Authority, 400, 13450–102 Avenue, Surrey BC V3T 0H1, Canada.

Financial disclosures: None.

From the Fraser Health Authority, Surrey, British Columbia, Canada.

Abstract

• Objective: To study care outcomes associated with a network of hospitalist services compared to traditional providers.
• Design: Retrospective review of administrative data.
• Setting and participants: Patients from a large integrated health care system in British Columbia in western Canada admitted and cared for by 3 provider groups between April 1, 2012, and March 31, 2018: hospitalists, family physicians (FP), and internal medicine (IM) physicians:
• Measurements: Average total length of stay (LOS), 30-day readmission, in-hospital mortality, and hospital standardized mortality ratio (HSMR) were the study outcome measures. Multiple logistic regression or generalized regression were completed to determine the relationship between provider groups and outcomes.
• Results: A total of 248,412 hospitalizations were included. Compared to patients admitted to hospitalists, patients admitted to other providers had higher odds of mortality (odds ratio [OR] for FP, 1.29; 95% confidence interval [CI], 1.21-1.37; OR for IM, 1.24; 95% CI, 1.15-1.33). Compared to hospitalist care, FP care was associated with higher readmission (OR, 1.27; 95% CI, 1.22-1.33), while IM care showed lower odds of readmission (OR, 0.83; 95% CI, 0.79-0.87). Patients admitted to the IM group had significantly lower total LOS (mean, 5.13 days; 95% CI, 5.04-5.21) compared to patients admitted to hospitalists (mean, 7.37 days; CI, 7.26-7.49) and FPs (mean, 7.30 days; 95% CI, 7.19-7.41). In a subgroup analysis of patients presenting with congestive heart failure, chronic obstructive pulmonary disease, and pneumonia, these general tendencies broadly persisted for mortality and LOS comparisons between FPs and hospitalists, but results were mixed for hospital readmissions.
• Conclusion: Care provided by hospitalists was associated with lower mortality and readmission rates compared with care provided by FPs, despite similar LOS. These findings may reflect differences in volume of services delivered by individual physicians, on-site availability to address urgent medical issues, and evolving specialization of clinical and nonclinical care processes in the acute care setting.

Keywords: hospital medicine; length of stay; readmission; mortality.

The hospitalist model of care has undergone rapid growth globally in recent years.¹ The first hospitalist programs in Canada began around the same time as those in the United States and share many similarities in design and operations with their counterparts.^2-4 However, unlike in the United States, where the hospitalist model has successfully established itself as an emerging specialty, debates about the merits of the model and its value proposition continue among Canadian observers.^5-9

Historically, the type of physicians who acted as the most responsible provider (MRP) in Canadian hospitals depended on setting and geography.¹⁰ In large urban areas, groups of general internists or specialists have historically looked after general medicine patients as part of university-affiliated teaching services.^11,12 Patients admitted to community hospitals have traditionally been cared for by their own primary care providers, typically general practitioners or family physicians (FPs). In the mid-1990s, many primary care providers in urban centers began to withdraw from inpatient care and primarily focused their practices in the outpatient setting.^13-15 Hospitalist programs emerged as health care administrators sought to fill the resulting gap in MRP coverage.^2,10

To date, attempts to understand the impact of hospitalist programs in Canada have been limited. A number of early studies aimed to describe¹⁶ the role of hospitalists in Canada and suggested improvements in length of stay (LOS) and staff satisfaction.¹⁷ However, these studies relied on unadjusted before-after comparisons and lacked methodological rigor to draw robust conclusions. More recently, a few studies have evaluated care outcomes associated with hospitalists using administrative databases, which attempted to control for potential confounding factors.^18-21

While these studies are beginning to shed some light on the impact of hospital medicine programs in Canada, there are a number of issues that limit their generalizability. For example, the majority of studies to date focus on hospital medicine programs in Canada’s largest province (Ontario), and most describe experiences from single institutions. Since each of the 13 provincial and territorial governments organizes its health care system differently,²² results from 1 province may not be generalizable to other parts of the country. Moreover, hospitalists in Ontario are more diverse in their training backgrounds, with a larger percentage having trained in general internal medicine (IM), as compared to other parts of Canada, where the majority of hospitalists are overwhelmingly trained as FPs.³

We aimed to study care outcomes associated with a network of hospitalist services compared to “traditional” providers (community-based FPs and IM specialists) in a large integrated health care system in the province of British Columbia in western Canada. The hospital medicine services in this network span a range of community and academic hospitals, and collectively constitute 1 of the largest regional programs in the country. This provides a unique opportunity to understand the impact of hospitalists on outcome measures across a range of acute care institutions.

 

 

Methods

Setting and Population

Fraser Health Authority is 1 of 5 regional health authorities in British Columbia that emerged in 2001.^23,24 It operates a network of hospitalist programs in 10 of its 12 acute care hospitals. In addition to hospitalists, there are a variable number of “traditional” physician providers who continue to act as MRPs. These include community-based FPs who continue to see their own patients in the hospital, either as part of a solo-practice model or a clinic-based call group. There are also a number of general internists and other subspecialists who accept MRP roles for general medicine patients who may present with higher-acuity conditions. As a result, patients requiring hospitalization due to nonsurgical or noncritical care conditions at each Fraser Health hospital may be cared for by a physician belonging to 1 of 3 groups, depending on local circumstances: an FP, a hospitalist, or an internist.

Inclusion and Exclusion Criteria

In order to evaluate comparative outcomes associated with hospitalist care, we included all patients admitted to a physician in each of the 3 provider groups between April 1, 2012, and March 31, 2018. We chose this time period for 2 reasons: first, we wanted to ensure comparability over an extended period of time, given the methodological changes implemented in 2009 by the Canadian Institute for Health Information (CIHI), the federal organization in the country responsible for setting standards for health care measures.²⁵ Second, previous internal reviews had suggested that data quality prior to this year was inconsistent. We only considered hospitalizations where patients were admitted to and discharged by the same service, and excluded 2 acute care facilities and 1 free-standing rehabilitation facility without a hospitalist service during this period. We also excluded patients who resided in a location beyond the geographic catchment area of Fraser Health. Further details about data collection are outlined in the Appendix.

Measures

We used the framework developed by White and Glazier²⁶ to inform the selection of our outcome measures, as well as relevant variables that may impact them. This framework proposes that the design of the inpatient care model (structures and processes of care) directly affects care outcomes. The model also proposes that patient and provider attributes can modulate this relationship, and suggests that a comprehensive evaluation of hospitalist performance needs to take these factors into account. We identified average total LOS, 30-day readmission rate, in-hospital mortality, and hospital standardized mortality ratio (HSMR)²⁷ as primary outcome measures. HSMR is defined as actual over expected mortality and is measured by CIHI through a formula that takes into account patient illness attributes (eg, the most responsible diagnosis, comorbidity levels) and baseline population mortality rates.²⁷ We chose these measures because they are clinically relevant and easy to obtain and have been utilized in previous similar studies in Canada and the United States.^18-21,26

Statistical Analysis

Baseline demographic and clinical differences in patient outcomes were examined using independent t-tests or chi-square tests. Furthermore, baseline differences based on provider groups were explored using analysis of variance or chi-square tests. Multiple logistic regression analyses were completed to determine the relationship between provider groups and readmission and mortality, while the relationship between provider groups and hospital LOS was determined with generalized linear regression (using gamma distribution and a log link). Gamma distribution with a log link analysis is appropriate with outcome measures that are positively skewed (eg, hospital LOS). It assumes that data are sampled from an exponential family of distributions, thus mimicking a log-normal distribution, and minimizes estimation bias and standard errors. These analyses were completed while controlling for the effects of age, gender, and other potential confounding factors.

We initially attempted to control for case mix by incorporating case-mix groups (CMGs) in our multivariate analysis. However, we identified 475 CMGs with at least 1 patient in our study population. We then explored the inclusion of major clinical categories (MCCs) that broadly group CMGs into various higher order/organ-system level categories (eg, diseases of the respiratory system); however, we could not aggregate them into sufficiently homogenous groups to be entered into regression models. Instead, we conducted subgroup analyses on patients in our study population who were hospitalized with 1 of the following 3 CMGs: chronic obstructive pulmonary disease (COPD, n = 11,404 patients), congestive heart failure without coronary angiography (CHF, n = 7680), and pneumonia (itself an aggregate of 3 separate CMGs: aspiration pneumonia, bacterial pneumonia, viral/unspecified pneumonia, n = 11,155). We chose these CMGs as they are among the top 8 presentations for all 3 provider groups.

For all outcome measures, we excluded atypical patients (defined by CIHI as those with atypically long stays) and patients who had been transferred between facilities. For the readmission analysis, we also excluded patients who died in the hospital (Appendix A). Data analyses were completed in IBM SPSS, version 21. For all analyses, significance was determined using 2-tailed test and alpha < 0.05.

Ethics

The Fraser Health Department of Research and Evaluation reviewed this project to determine need for formal Ethics Review Board review, and granted an exemption based on institutional guidelines for program evaluations.

 

Results

A total of 132,178 patients were admitted to and discharged by 1 of the 3 study provider groups during the study period, accounting for a total of 248,412 hospitalizations. After excluding patients cared for in Fraser Health facilities without a hospitalist service and those who resided in a geographic area beyond Fraser Health, a total of 224,214 admissions were included in the final analysis.

Patient Characteristics

The demographic and clinical characteristics of patients by provider group are summarized in Table 1. Patients admitted to IM providers were substantially younger than those admitted to either FPs or hospitalists (61.00 vs 70.86 and 71.22 years, respectively; P < 0.005). However, patients admitted to hospitalists had higher degrees of complexity (as measured by higher comorbidity levels, number of secondary diagnoses, and higher resource intensity weights [RIWs]; P < 000.1 for all comparisons). Overall, the most common CMGs seen by FPs and hospitalists were similar, while IM providers primarily saw patients with cardiac conditions (Table 2).

Trends Over Time

During the study period, the number of patients admitted to the hospitalist services increased by 24%, while admissions to FPs and IM providers declined steadily (Figure). During this time, LOS for hospitalists progressively declined, while LOS for FPs and IM providers increased. Similar trends were observed for measures of mortality, while readmission rates remained constant for FPs, despite a decline observed for other providers.

 

 

Mortality

Table 3 summarizes the relationship between provider groups and in-hospital mortality (n = 183,779). Controlling for other variables, patients admitted to FP and IM providers had higher odds of mortality when compared to hospitalists (odds ratio [OR] for FPs, 1.29; 95% confidence interval [CI], 1.21-1.37; OR for IM, 1.24; 95% CI, 1.15-1.33). Older age, higher comorbidity level, higher number of secondary diagnoses, higher use of hospital resources (as measured by RIWs), longer than expected hospital stay (as measured by conservable days), and male gender were also associated with higher mortality. Similarly, patients receiving palliative care and those who spent at least 1 day in a special care unit (critical care, observation, and monitored care units) also had higher odds of mortality. On the other hand, admission to nonteaching medium facilities and longer hospital stay were associated with lower mortality. Compared to the first year of this analysis, lower mortality rates were observed in subsequent fiscal years. Finally, there appear to be geographic variations in mortality within Fraser Health.

Our analysis of patients with COPD, CHF, and pneumonia showed mixed results (Table 4). Patients admitted to the FP provider group with CHF and pneumonia had higher mortality compared to hospitalists (OR for CHF, 1.77; 95% CI, 1.38-2.27; OR for pneumonia, 1.53; 95% CI, 1.25-1.88), with a similar but nonstatistically significant trend observed for patients with COPD (OR, 1.29; 95% CI, 0.99-1.70). On the other hand, the higher observed mortality associated with the IM provider group in the overall study population only persisted for patients with COPD (OR, 2.71; 95% CI, 1.94-3.80), with no statistically significant differences for patients with CHF (OR, 1.18; 95% CI, 0.84-1.65) and pneumonia (OR, 0.93; 95% CI, 0.69-1.25).

We also studied adjusted mortality as measured by HSMRs. Currently, our Health Information Management system calculates an HSMR value for each patient admitted to our acute care facilities using the methodology developed by CIHI. Prior internal audits demonstrated that our internal calculations closely approximate those reported nationally. Our analysis suggests that over time, HSMR rates for the 3 provider groups have diverged, with patients admitted to IM providers having a higher mortality rate than what would be expected based on the presenting clinical conditions and comorbidity levels (Figure, part D).

Readmission

The results of our multiple logistic regression for readmission are summarized in Table 5 (n = 166,042). The impact of provider group on 30-day readmission is mixed, with higher odds associated with FPs compared to hospitalists (OR, 1.27; 95% CI, 1.22-1.34) and lower odds associated with IM physicians (OR, 0.83; 95% CI, 0.79-0.87). Gender and RIW did not show any significant associations, but increasing age, higher number of secondary diagnoses, higher comorbidity levels, and longer than expected LOS (as measure by conservable days) were associated with higher odds of readmission. Conversely, longer hospitalization, admission to a large community hospital, palliative status, admission to a special care unit, geography, and fiscal year were associated with lower odds of readmission.

The above differences between provider groups were no longer consistently present when we analyzed patients presenting with COPD, CHF, and pneumonias (Table 6). Only patients admitted to the FP provider group with pneumonia had higher odds of readmission compared to hospitalists (OR, 1.27; 95% CI, 1.05-1.54). Conversely, only patients admitted to the IM provider group with CHF showed lower readmission (OR, 0.75; 95% CI, 0.62-0.92).

 

 

Total LOS

Results using generalized linear regressions for total LOS are presented in Table 7 (n = 183,779). Patients admitted to the IM provider group had significantly lower total LOS (mean, 5.13 days; 95% CI, 5.04-5.21) compared to the hospitalist (mean, 7.37 days; 95% CI, 7.26-7.49) and FP (mean, 7.30 days; 95% CI, 7.19-7.41) groups, with no significant differences between the latter 2 groups. Older patients, females, patients with higher comorbidity levels or number of secondary diagnoses, higher RIW, palliative patients, and discharge to a facility other than the patient’s home were associated with a significantly longer LOS. On the other hand, admission to nonteaching hospitals and admission to a special care unit was associated with lower LOS.

When we compared total LOS for patients admitted with COPD, CHF, and pneumonias, the same differences observed for the broader comparisons persisted: IM patients consistently showed shorter LOS compared to hospitalist patients, while LOS associated with FP patients was similar (Table 8).

Discussion

To our knowledge, our evaluation is the largest study to date designed to understand outcomes associated with hospitalist care in Canada. Our analyses suggest that patients admitted to our large network of hospitalist services present with clinical conditions that are very similar to those of general medicine patients in other Canadian provinces.^28,29 They also show that patients cared for by hospitalists experience lower mortality rates compared to those cared for by FPs. Our findings are similar to previous studies, which have suggested a 12% to 75% reduction in odds of mortality associated with hospitalist care.^18,19 These differences persisted even when we focused on patients presenting with specific clinical conditions (CHF, COPD, and pneumonias).

 

White and colleagues have previously demonstrated that generalist physicians who had higher volumes of inpatient care activity also had lower mortality rates compared to those who cared for hospitalized patients less frequently.¹⁹ An association between higher physician caseloads and better outcomes has been established for many surgical and medical conditions.^30-32 Given that 85% of hospitalists in our program have post-graduate medical training in family medicine (internal department surveys, data not shown), it is less likely that training background can explain differences in outcomes. Instead, differences in patient volumes and the dedicated focus of hospitalists on acute care are likely more important contributors to lower mortality. In our program, a full-time hospitalist spends an average of 2000 hours annually providing services in the hospital setting. The continuous on-site presence of hospitalists enhances their clinical experience with regards to the management of common medical conditions, and increases their exposure to less common presentations of illnesses. The ability to respond to deteriorating patients in a timely manner may be another factor in explaining the differences in mortality rates between dedicated hospital-based generalist providers and similarly trained physicians with a primarily community-based focus.

In our study, hospitalist care was also broadly associated with lower mortality compared to the IM providers, although these differences were not consistently present when patients with specific diagnoses were compared. This may be partly explained by the relationship between caseload and outcomes, but other factors may also be important. For example, patients admitted by IM providers spend significantly more time in specialized units. They also predominantly present with cardiac conditions, and as such may have higher acuity levels and require more invasive interventions. While this may explain the higher observed mortality, a within-group comparison still suggests higher than expected mortality for IM patients. The HSMR methodology measures actual mortality rates compared to what would be expected based on clinical presentation and baseline population characteristics. Calculating HSMR is highly dependent on proper documentation and chart abstraction,^33,34 and it is possible that some of the differences observed are due to incomplete physician documentation. However, a more in-depth analysis of care processes will be required to clarify the observed trends.

Compared to hospitalists, patients cared for by FPs also had higher odds of readmission within 30 days, which is consistent with prior studies.^18,19 One of the criticisms of the hospitalist model has been the inherent discontinuity of care that is built into the model, which can contribute to suboptimal transitions of care between the acute and community settings.³⁵ The expectation is that FPs who admit their own patients do not face this challenge, and as a result their patients should be readmitted less frequently after discharge. Our data and those from previous studies do not support this hypothesis. At the same time, when we studied patients with specific clinical diagnoses, only those hospitalized for pneumonias continued to demonstrate higher readmission odds. This suggests that hospital readmission rate is a complex measure that may be influenced by a multitude of hospital and community factors, and may be different for patients who present with different clinical diagnoses. Further research is required to better understand the relationship between provider type and experience with hospital readmission for patients with various clinical presentations.

Unlike the United States, where hospitalist care has been associated with reductions in LOS,^26,36 studies in the Canadian health care setting have shown mixed results.^17-21 In our evaluation, hospitalist care is not associated with reductions in total LOS compared to care provided by FPs or IM physicians. This could be due to a number of factors. First, unlike FPs, who know their patients, hospitalists may have a more conservative risk tolerance in discharging patients with whom they are not familiar. Similarly, physicians who have trained in IM may have a lower threshold for discharging patients than hospitalists, whose training background is mainly rooted in family medicine.³ Second, discontinuity of care has been associated with longer LOS for hospitalized patients.^37,38 Hospitalists generally work for 7- to 10-day rotations. As a result, a patient may see a number of different hospitalists during the same hospital stay, which could nullify any gains in LOS that may be expected from better familiarity with hospital processes. Third, whereas a FP or an internist may only have a few inpatients under their care at any given time, each hospitalist typically cares for 17 to 22 patients every day. Increasing hospitalist workload has been shown to negatively impact LOS and may result in lower efficiency.³⁹ Finally, many patients in our health system who require more time to recuperate or need complex discharge planning are usually transferred to the care of the hospitalist service from other services, or are preferentially admitted to hospitalists from the emergency department. As a result, hospitalists may look after a disproportionately higher number of long-stay patients. Despite all this, hospitalists in our population perform similarly to FPs, regardless of the clinical diagnoses of hospitalized patients.

 

Our study has a number of notable limitations. First, we used administrative data to conduct our evaluation and could only control for factors that are available in our data systems. As a result, some potential confounders may not have been taken into consideration. For example, our databases do not contain provider characteristics (eg, age, years of clinical experience) that have been deemed to be relevant by White and Glazier.²⁶ Similarly, we did not have all the necessary information about the characteristics of the various MRP programs (eg, number of physicians involved in group practices, the schedule model of community FP call groups) and were not able to account for the potential impact of these on observed outcomes. Second, although our findings mirror prior studies from other parts of Canada, they may not be applicable to hospitalist programs in other jurisdictions or in health systems that are not regionalized or integrated. Third, our IM provider group is heterogeneous, with a number of different IM subspecialties (cardiologists, gastroenterologists, general internists) grouped under the IM category in our database. As a result, comparisons between the IM provider group and the other 2 provider groups, which are more homogenous, should be interpreted with caution.

Finally, we included only patients admitted to facilities in which a hospitalist service existed during the study period. As a result, a medium-size community hospital without a hospitalist service where patients are cared for exclusively by FPs and IM physicians was not included in the comparisons, and in 4 of the 10 facilities included, the number of FP patients was less than 10% of total hospitalized patients at the site (Appendix A). This may have resulted in an under-representation of FP patients.

Conclusion

Debates about the merits of the hospitalist model in Canada continue, and are in part fueled by a paucity of robust evidence about its impact on care outcomes compared to more traditional ways of providing inpatient care. In our evaluation, care provided by hospitalists is associated with lower mortality and readmission rates, despite similar LOS compared with FPs. Hospitalist care is also associated with lower mortality compared to IM providers. Hospitalists also demonstrated progressive improvement over time, with decreasing LOS and mortality rates and a stable readmission rate. Our results suggest that physicians with a focus on inpatient care can have positive contributions to quality and efficiency of care in Canada.

Corresponding author: Vandad Yousefi MD, CCFP, FHM, Fraser Health Authority, 400, 13450–102 Avenue, Surrey BC V3T 0H1, Canada.

Financial disclosures: None.

From the Fraser Health Authority, Surrey, British Columbia, Canada.

Abstract

• Objective: To study care outcomes associated with a network of hospitalist services compared to traditional providers.
• Design: Retrospective review of administrative data.
• Setting and participants: Patients from a large integrated health care system in British Columbia in western Canada admitted and cared for by 3 provider groups between April 1, 2012, and March 31, 2018: hospitalists, family physicians (FP), and internal medicine (IM) physicians:
• Measurements: Average total length of stay (LOS), 30-day readmission, in-hospital mortality, and hospital standardized mortality ratio (HSMR) were the study outcome measures. Multiple logistic regression or generalized regression were completed to determine the relationship between provider groups and outcomes.
• Results: A total of 248,412 hospitalizations were included. Compared to patients admitted to hospitalists, patients admitted to other providers had higher odds of mortality (odds ratio [OR] for FP, 1.29; 95% confidence interval [CI], 1.21-1.37; OR for IM, 1.24; 95% CI, 1.15-1.33). Compared to hospitalist care, FP care was associated with higher readmission (OR, 1.27; 95% CI, 1.22-1.33), while IM care showed lower odds of readmission (OR, 0.83; 95% CI, 0.79-0.87). Patients admitted to the IM group had significantly lower total LOS (mean, 5.13 days; 95% CI, 5.04-5.21) compared to patients admitted to hospitalists (mean, 7.37 days; CI, 7.26-7.49) and FPs (mean, 7.30 days; 95% CI, 7.19-7.41). In a subgroup analysis of patients presenting with congestive heart failure, chronic obstructive pulmonary disease, and pneumonia, these general tendencies broadly persisted for mortality and LOS comparisons between FPs and hospitalists, but results were mixed for hospital readmissions.
• Conclusion: Care provided by hospitalists was associated with lower mortality and readmission rates compared with care provided by FPs, despite similar LOS. These findings may reflect differences in volume of services delivered by individual physicians, on-site availability to address urgent medical issues, and evolving specialization of clinical and nonclinical care processes in the acute care setting.

Keywords: hospital medicine; length of stay; readmission; mortality.

The hospitalist model of care has undergone rapid growth globally in recent years.¹ The first hospitalist programs in Canada began around the same time as those in the United States and share many similarities in design and operations with their counterparts.^2-4 However, unlike in the United States, where the hospitalist model has successfully established itself as an emerging specialty, debates about the merits of the model and its value proposition continue among Canadian observers.^5-9

Historically, the type of physicians who acted as the most responsible provider (MRP) in Canadian hospitals depended on setting and geography.¹⁰ In large urban areas, groups of general internists or specialists have historically looked after general medicine patients as part of university-affiliated teaching services.^11,12 Patients admitted to community hospitals have traditionally been cared for by their own primary care providers, typically general practitioners or family physicians (FPs). In the mid-1990s, many primary care providers in urban centers began to withdraw from inpatient care and primarily focused their practices in the outpatient setting.^13-15 Hospitalist programs emerged as health care administrators sought to fill the resulting gap in MRP coverage.^2,10

To date, attempts to understand the impact of hospitalist programs in Canada have been limited. A number of early studies aimed to describe¹⁶ the role of hospitalists in Canada and suggested improvements in length of stay (LOS) and staff satisfaction.¹⁷ However, these studies relied on unadjusted before-after comparisons and lacked methodological rigor to draw robust conclusions. More recently, a few studies have evaluated care outcomes associated with hospitalists using administrative databases, which attempted to control for potential confounding factors.^18-21

While these studies are beginning to shed some light on the impact of hospital medicine programs in Canada, there are a number of issues that limit their generalizability. For example, the majority of studies to date focus on hospital medicine programs in Canada’s largest province (Ontario), and most describe experiences from single institutions. Since each of the 13 provincial and territorial governments organizes its health care system differently,²² results from 1 province may not be generalizable to other parts of the country. Moreover, hospitalists in Ontario are more diverse in their training backgrounds, with a larger percentage having trained in general internal medicine (IM), as compared to other parts of Canada, where the majority of hospitalists are overwhelmingly trained as FPs.³

We aimed to study care outcomes associated with a network of hospitalist services compared to “traditional” providers (community-based FPs and IM specialists) in a large integrated health care system in the province of British Columbia in western Canada. The hospital medicine services in this network span a range of community and academic hospitals, and collectively constitute 1 of the largest regional programs in the country. This provides a unique opportunity to understand the impact of hospitalists on outcome measures across a range of acute care institutions.

 

 

Methods

Setting and Population

Fraser Health Authority is 1 of 5 regional health authorities in British Columbia that emerged in 2001.^23,24 It operates a network of hospitalist programs in 10 of its 12 acute care hospitals. In addition to hospitalists, there are a variable number of “traditional” physician providers who continue to act as MRPs. These include community-based FPs who continue to see their own patients in the hospital, either as part of a solo-practice model or a clinic-based call group. There are also a number of general internists and other subspecialists who accept MRP roles for general medicine patients who may present with higher-acuity conditions. As a result, patients requiring hospitalization due to nonsurgical or noncritical care conditions at each Fraser Health hospital may be cared for by a physician belonging to 1 of 3 groups, depending on local circumstances: an FP, a hospitalist, or an internist.

Inclusion and Exclusion Criteria

In order to evaluate comparative outcomes associated with hospitalist care, we included all patients admitted to a physician in each of the 3 provider groups between April 1, 2012, and March 31, 2018. We chose this time period for 2 reasons: first, we wanted to ensure comparability over an extended period of time, given the methodological changes implemented in 2009 by the Canadian Institute for Health Information (CIHI), the federal organization in the country responsible for setting standards for health care measures.²⁵ Second, previous internal reviews had suggested that data quality prior to this year was inconsistent. We only considered hospitalizations where patients were admitted to and discharged by the same service, and excluded 2 acute care facilities and 1 free-standing rehabilitation facility without a hospitalist service during this period. We also excluded patients who resided in a location beyond the geographic catchment area of Fraser Health. Further details about data collection are outlined in the Appendix.

Measures

We used the framework developed by White and Glazier²⁶ to inform the selection of our outcome measures, as well as relevant variables that may impact them. This framework proposes that the design of the inpatient care model (structures and processes of care) directly affects care outcomes. The model also proposes that patient and provider attributes can modulate this relationship, and suggests that a comprehensive evaluation of hospitalist performance needs to take these factors into account. We identified average total LOS, 30-day readmission rate, in-hospital mortality, and hospital standardized mortality ratio (HSMR)²⁷ as primary outcome measures. HSMR is defined as actual over expected mortality and is measured by CIHI through a formula that takes into account patient illness attributes (eg, the most responsible diagnosis, comorbidity levels) and baseline population mortality rates.²⁷ We chose these measures because they are clinically relevant and easy to obtain and have been utilized in previous similar studies in Canada and the United States.^18-21,26

Statistical Analysis

Baseline demographic and clinical differences in patient outcomes were examined using independent t-tests or chi-square tests. Furthermore, baseline differences based on provider groups were explored using analysis of variance or chi-square tests. Multiple logistic regression analyses were completed to determine the relationship between provider groups and readmission and mortality, while the relationship between provider groups and hospital LOS was determined with generalized linear regression (using gamma distribution and a log link). Gamma distribution with a log link analysis is appropriate with outcome measures that are positively skewed (eg, hospital LOS). It assumes that data are sampled from an exponential family of distributions, thus mimicking a log-normal distribution, and minimizes estimation bias and standard errors. These analyses were completed while controlling for the effects of age, gender, and other potential confounding factors.

We initially attempted to control for case mix by incorporating case-mix groups (CMGs) in our multivariate analysis. However, we identified 475 CMGs with at least 1 patient in our study population. We then explored the inclusion of major clinical categories (MCCs) that broadly group CMGs into various higher order/organ-system level categories (eg, diseases of the respiratory system); however, we could not aggregate them into sufficiently homogenous groups to be entered into regression models. Instead, we conducted subgroup analyses on patients in our study population who were hospitalized with 1 of the following 3 CMGs: chronic obstructive pulmonary disease (COPD, n = 11,404 patients), congestive heart failure without coronary angiography (CHF, n = 7680), and pneumonia (itself an aggregate of 3 separate CMGs: aspiration pneumonia, bacterial pneumonia, viral/unspecified pneumonia, n = 11,155). We chose these CMGs as they are among the top 8 presentations for all 3 provider groups.

For all outcome measures, we excluded atypical patients (defined by CIHI as those with atypically long stays) and patients who had been transferred between facilities. For the readmission analysis, we also excluded patients who died in the hospital (Appendix A). Data analyses were completed in IBM SPSS, version 21. For all analyses, significance was determined using 2-tailed test and alpha < 0.05.

Ethics

The Fraser Health Department of Research and Evaluation reviewed this project to determine need for formal Ethics Review Board review, and granted an exemption based on institutional guidelines for program evaluations.

 

Results

A total of 132,178 patients were admitted to and discharged by 1 of the 3 study provider groups during the study period, accounting for a total of 248,412 hospitalizations. After excluding patients cared for in Fraser Health facilities without a hospitalist service and those who resided in a geographic area beyond Fraser Health, a total of 224,214 admissions were included in the final analysis.

Patient Characteristics

The demographic and clinical characteristics of patients by provider group are summarized in Table 1. Patients admitted to IM providers were substantially younger than those admitted to either FPs or hospitalists (61.00 vs 70.86 and 71.22 years, respectively; P < 0.005). However, patients admitted to hospitalists had higher degrees of complexity (as measured by higher comorbidity levels, number of secondary diagnoses, and higher resource intensity weights [RIWs]; P < 000.1 for all comparisons). Overall, the most common CMGs seen by FPs and hospitalists were similar, while IM providers primarily saw patients with cardiac conditions (Table 2).

Trends Over Time

During the study period, the number of patients admitted to the hospitalist services increased by 24%, while admissions to FPs and IM providers declined steadily (Figure). During this time, LOS for hospitalists progressively declined, while LOS for FPs and IM providers increased. Similar trends were observed for measures of mortality, while readmission rates remained constant for FPs, despite a decline observed for other providers.

 

 

Mortality

Table 3 summarizes the relationship between provider groups and in-hospital mortality (n = 183,779). Controlling for other variables, patients admitted to FP and IM providers had higher odds of mortality when compared to hospitalists (odds ratio [OR] for FPs, 1.29; 95% confidence interval [CI], 1.21-1.37; OR for IM, 1.24; 95% CI, 1.15-1.33). Older age, higher comorbidity level, higher number of secondary diagnoses, higher use of hospital resources (as measured by RIWs), longer than expected hospital stay (as measured by conservable days), and male gender were also associated with higher mortality. Similarly, patients receiving palliative care and those who spent at least 1 day in a special care unit (critical care, observation, and monitored care units) also had higher odds of mortality. On the other hand, admission to nonteaching medium facilities and longer hospital stay were associated with lower mortality. Compared to the first year of this analysis, lower mortality rates were observed in subsequent fiscal years. Finally, there appear to be geographic variations in mortality within Fraser Health.

Our analysis of patients with COPD, CHF, and pneumonia showed mixed results (Table 4). Patients admitted to the FP provider group with CHF and pneumonia had higher mortality compared to hospitalists (OR for CHF, 1.77; 95% CI, 1.38-2.27; OR for pneumonia, 1.53; 95% CI, 1.25-1.88), with a similar but nonstatistically significant trend observed for patients with COPD (OR, 1.29; 95% CI, 0.99-1.70). On the other hand, the higher observed mortality associated with the IM provider group in the overall study population only persisted for patients with COPD (OR, 2.71; 95% CI, 1.94-3.80), with no statistically significant differences for patients with CHF (OR, 1.18; 95% CI, 0.84-1.65) and pneumonia (OR, 0.93; 95% CI, 0.69-1.25).

We also studied adjusted mortality as measured by HSMRs. Currently, our Health Information Management system calculates an HSMR value for each patient admitted to our acute care facilities using the methodology developed by CIHI. Prior internal audits demonstrated that our internal calculations closely approximate those reported nationally. Our analysis suggests that over time, HSMR rates for the 3 provider groups have diverged, with patients admitted to IM providers having a higher mortality rate than what would be expected based on the presenting clinical conditions and comorbidity levels (Figure, part D).

Readmission

The results of our multiple logistic regression for readmission are summarized in Table 5 (n = 166,042). The impact of provider group on 30-day readmission is mixed, with higher odds associated with FPs compared to hospitalists (OR, 1.27; 95% CI, 1.22-1.34) and lower odds associated with IM physicians (OR, 0.83; 95% CI, 0.79-0.87). Gender and RIW did not show any significant associations, but increasing age, higher number of secondary diagnoses, higher comorbidity levels, and longer than expected LOS (as measure by conservable days) were associated with higher odds of readmission. Conversely, longer hospitalization, admission to a large community hospital, palliative status, admission to a special care unit, geography, and fiscal year were associated with lower odds of readmission.

The above differences between provider groups were no longer consistently present when we analyzed patients presenting with COPD, CHF, and pneumonias (Table 6). Only patients admitted to the FP provider group with pneumonia had higher odds of readmission compared to hospitalists (OR, 1.27; 95% CI, 1.05-1.54). Conversely, only patients admitted to the IM provider group with CHF showed lower readmission (OR, 0.75; 95% CI, 0.62-0.92).

 

 

Total LOS

Results using generalized linear regressions for total LOS are presented in Table 7 (n = 183,779). Patients admitted to the IM provider group had significantly lower total LOS (mean, 5.13 days; 95% CI, 5.04-5.21) compared to the hospitalist (mean, 7.37 days; 95% CI, 7.26-7.49) and FP (mean, 7.30 days; 95% CI, 7.19-7.41) groups, with no significant differences between the latter 2 groups. Older patients, females, patients with higher comorbidity levels or number of secondary diagnoses, higher RIW, palliative patients, and discharge to a facility other than the patient’s home were associated with a significantly longer LOS. On the other hand, admission to nonteaching hospitals and admission to a special care unit was associated with lower LOS.

When we compared total LOS for patients admitted with COPD, CHF, and pneumonias, the same differences observed for the broader comparisons persisted: IM patients consistently showed shorter LOS compared to hospitalist patients, while LOS associated with FP patients was similar (Table 8).

Discussion

To our knowledge, our evaluation is the largest study to date designed to understand outcomes associated with hospitalist care in Canada. Our analyses suggest that patients admitted to our large network of hospitalist services present with clinical conditions that are very similar to those of general medicine patients in other Canadian provinces.^28,29 They also show that patients cared for by hospitalists experience lower mortality rates compared to those cared for by FPs. Our findings are similar to previous studies, which have suggested a 12% to 75% reduction in odds of mortality associated with hospitalist care.^18,19 These differences persisted even when we focused on patients presenting with specific clinical conditions (CHF, COPD, and pneumonias).

 

White and colleagues have previously demonstrated that generalist physicians who had higher volumes of inpatient care activity also had lower mortality rates compared to those who cared for hospitalized patients less frequently.¹⁹ An association between higher physician caseloads and better outcomes has been established for many surgical and medical conditions.^30-32 Given that 85% of hospitalists in our program have post-graduate medical training in family medicine (internal department surveys, data not shown), it is less likely that training background can explain differences in outcomes. Instead, differences in patient volumes and the dedicated focus of hospitalists on acute care are likely more important contributors to lower mortality. In our program, a full-time hospitalist spends an average of 2000 hours annually providing services in the hospital setting. The continuous on-site presence of hospitalists enhances their clinical experience with regards to the management of common medical conditions, and increases their exposure to less common presentations of illnesses. The ability to respond to deteriorating patients in a timely manner may be another factor in explaining the differences in mortality rates between dedicated hospital-based generalist providers and similarly trained physicians with a primarily community-based focus.

In our study, hospitalist care was also broadly associated with lower mortality compared to the IM providers, although these differences were not consistently present when patients with specific diagnoses were compared. This may be partly explained by the relationship between caseload and outcomes, but other factors may also be important. For example, patients admitted by IM providers spend significantly more time in specialized units. They also predominantly present with cardiac conditions, and as such may have higher acuity levels and require more invasive interventions. While this may explain the higher observed mortality, a within-group comparison still suggests higher than expected mortality for IM patients. The HSMR methodology measures actual mortality rates compared to what would be expected based on clinical presentation and baseline population characteristics. Calculating HSMR is highly dependent on proper documentation and chart abstraction,^33,34 and it is possible that some of the differences observed are due to incomplete physician documentation. However, a more in-depth analysis of care processes will be required to clarify the observed trends.

Compared to hospitalists, patients cared for by FPs also had higher odds of readmission within 30 days, which is consistent with prior studies.^18,19 One of the criticisms of the hospitalist model has been the inherent discontinuity of care that is built into the model, which can contribute to suboptimal transitions of care between the acute and community settings.³⁵ The expectation is that FPs who admit their own patients do not face this challenge, and as a result their patients should be readmitted less frequently after discharge. Our data and those from previous studies do not support this hypothesis. At the same time, when we studied patients with specific clinical diagnoses, only those hospitalized for pneumonias continued to demonstrate higher readmission odds. This suggests that hospital readmission rate is a complex measure that may be influenced by a multitude of hospital and community factors, and may be different for patients who present with different clinical diagnoses. Further research is required to better understand the relationship between provider type and experience with hospital readmission for patients with various clinical presentations.

Unlike the United States, where hospitalist care has been associated with reductions in LOS,^26,36 studies in the Canadian health care setting have shown mixed results.^17-21 In our evaluation, hospitalist care is not associated with reductions in total LOS compared to care provided by FPs or IM physicians. This could be due to a number of factors. First, unlike FPs, who know their patients, hospitalists may have a more conservative risk tolerance in discharging patients with whom they are not familiar. Similarly, physicians who have trained in IM may have a lower threshold for discharging patients than hospitalists, whose training background is mainly rooted in family medicine.³ Second, discontinuity of care has been associated with longer LOS for hospitalized patients.^37,38 Hospitalists generally work for 7- to 10-day rotations. As a result, a patient may see a number of different hospitalists during the same hospital stay, which could nullify any gains in LOS that may be expected from better familiarity with hospital processes. Third, whereas a FP or an internist may only have a few inpatients under their care at any given time, each hospitalist typically cares for 17 to 22 patients every day. Increasing hospitalist workload has been shown to negatively impact LOS and may result in lower efficiency.³⁹ Finally, many patients in our health system who require more time to recuperate or need complex discharge planning are usually transferred to the care of the hospitalist service from other services, or are preferentially admitted to hospitalists from the emergency department. As a result, hospitalists may look after a disproportionately higher number of long-stay patients. Despite all this, hospitalists in our population perform similarly to FPs, regardless of the clinical diagnoses of hospitalized patients.

 

Our study has a number of notable limitations. First, we used administrative data to conduct our evaluation and could only control for factors that are available in our data systems. As a result, some potential confounders may not have been taken into consideration. For example, our databases do not contain provider characteristics (eg, age, years of clinical experience) that have been deemed to be relevant by White and Glazier.²⁶ Similarly, we did not have all the necessary information about the characteristics of the various MRP programs (eg, number of physicians involved in group practices, the schedule model of community FP call groups) and were not able to account for the potential impact of these on observed outcomes. Second, although our findings mirror prior studies from other parts of Canada, they may not be applicable to hospitalist programs in other jurisdictions or in health systems that are not regionalized or integrated. Third, our IM provider group is heterogeneous, with a number of different IM subspecialties (cardiologists, gastroenterologists, general internists) grouped under the IM category in our database. As a result, comparisons between the IM provider group and the other 2 provider groups, which are more homogenous, should be interpreted with caution.

Finally, we included only patients admitted to facilities in which a hospitalist service existed during the study period. As a result, a medium-size community hospital without a hospitalist service where patients are cared for exclusively by FPs and IM physicians was not included in the comparisons, and in 4 of the 10 facilities included, the number of FP patients was less than 10% of total hospitalized patients at the site (Appendix A). This may have resulted in an under-representation of FP patients.

Conclusion

Debates about the merits of the hospitalist model in Canada continue, and are in part fueled by a paucity of robust evidence about its impact on care outcomes compared to more traditional ways of providing inpatient care. In our evaluation, care provided by hospitalists is associated with lower mortality and readmission rates, despite similar LOS compared with FPs. Hospitalist care is also associated with lower mortality compared to IM providers. Hospitalists also demonstrated progressive improvement over time, with decreasing LOS and mortality rates and a stable readmission rate. Our results suggest that physicians with a focus on inpatient care can have positive contributions to quality and efficiency of care in Canada.

Corresponding author: Vandad Yousefi MD, CCFP, FHM, Fraser Health Authority, 400, 13450–102 Avenue, Surrey BC V3T 0H1, Canada.

Financial disclosures: None.

Persistent gender disparities exist in pay,^1,2 leadership opportunities,^3,4 promotion,⁵ and speaking opportunities.⁶ While the gender distribution of the hospitalist workforce may be approaching parity,^3,7,8 gender differences in leadership, speakership, and authorship have already been noted in hospital medicine.³ Between 2006 and 2012, women constituted less than a third (26%) of the presenters at the national conferences of the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM).³

The SHM Annual Meeting has historically had an “open call” peer review process for workshop presenters with the goal of increasing the diversity of presenters. In 2019, this process was expanded to include didactic speakers. Our aim in this study was to assess whether these open call procedures resulted in improved representation of women speakers and how the proportion of women speakers affects the overall evaluation scores of the conference. Our hypothesis was that the introduction of an open call process for the SHM conference didactic speakers would be associated with an increased proportion of women speakers, compared with the closed call processes, without a negative impact on conference scores.

METHODS

The study is a retrospective evaluation of data collected regarding speakers at the annual SHM conference from 2015 to 2019. The SHM national conference typically has two main types of offerings: workshops and didactics. Workshop presenters from 2015 to 2019 were selected via an open call process as defined below. Didactic speakers (except for plenary speakers) were selected using the open call process for 2019 only.

We aimed to compare (1) the number and proportion of women speakers, compared with men speakers, over time and (2) the proportion of women speakers when open call processes were utilized versus that seen with closed call processes. Open call included workshops for all years and didactics for 2019; closed call included didactics for 2015 to 2018 and plenary sessions 2015 to 2019 (Table). The speaker list for the conferences was obtained from conference pamphlets or agendas available via Internet searches or obtained through attendance at the conference.

Speaker Categories and Identification Process

We determined whether each individual was a featured speaker (one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), whether they spoke in a group format, and whether the speaking opportunity type was a workshop or a didactic session. Numbers of featured and plenary speakers were combined because of low numbers. SHM provided deidentified conference evaluation data for each year studied. For the purposes of this study, we analyzed all speakers which included physicians, advanced practice providers, and professionals such as nurses and other interdisciplinary team members. The same speaker could be included multiple times if they had multiple speaking opportunities.

 

Open Call Process

We defined the “open call process” (referred to as “open call” here forward) as the process utilized by SHM that includes the following two components: (1) advertisements to members of SHM and to the medical community at large through a variety of mechanisms including emails, websites, and social media outlets and (2) an online submission process that includes names of proposed speakers and their topic and, in the case of workshops, session objectives as well as an outline of the proposed workshop. SHM committees may also submit suggestions for topics and speakers. Annual Conference Committee members then review and rate submissions on the categories of topic, organization and clarity, objectives, and speaker qualifications (with a focus on institutional, geographic, and gender diversity). Scores are assigned from 1 to 5 (with 5 being the best score) for each category and a section for comments is available. All submissions are also evaluated by the course director.

After initial committee reviews, scores with marked reviewer discrepancies are rereviewed and discussed by the committee and course director. A cutoff score is then calculated with proposals falling below the cutoff threshold omitted from further consideration. Weekly calls are then focused on subcategories (ie tracks) with emphasis on clinical and educational content. Each of the tracks have a subcommittee with track leads to curate the best content first and then focus on final speaker selection. More recently, templates are shared with the track leads that include a location to call out gender and institutional diversity. Weekly calls are held to hone the content and determine the speakers.

For the purposes of this study, when the above process was not used, the authors refer to it as “closed call.” Closed call processes do not typically involve open invitations or a peer review process. (Table)

Gender

Gender was assigned based on the speaker’s self-identification by the pronouns used in their biography submitted to the conference or on their institutional website or other websites where the speaker was referenced. Persons using she/her/hers pronouns were noted as women and persons using he/him/his were noted as men. For the purposes of this study, we conceptualized gender as binary (ie woman/man) given the limited information we had from online sources.

ANALYSIS

REDCap, a secure, Web-based application for building and managing online survey and databases, was used to collect and manage all study data.⁹

All analyses were performed using SAS Enterprise Guide 8.1 (SAS Institute, Inc., Cary, North Carolina) using retrospectively collected data. A Cochran-Armitage test for trend was used to evaluate the proportion of women speakers from 2015 to 2019. A chi-square test was used to assess the proportion of women speakers for open call processes versus that seen with closed call. One-way analysis of variance (ANOVA) was used to evaluate annual conference evaluation scores from 2015 to 2019. Either numbers with proportions or means with standard deviations have been reported. Bonferroni’s correction for multiple comparisons was applied, with a P < .008 considered statistically significant.

 

RESULTS

Between 2015 and 2019, a total of 709 workshop and didactic presentations were given by 1,261 speakers at the annual Society of Hospital Medicine Conference. Of these, 505 (40%) were women; 756 (60%) were men. There were no missing data.

From 2015 to 2019, representation of women speakers increased from 35% of all speakers to 47% of all speakers (P = .0068). Women plenary speakers increased from 23% in 2015 to 45% in 2019 (P = .0396).

The proportion of women presenters for workshops (which have utilized an open call process throughout the study period), ranged from 43% to 53% from 2015 to 2019 with no statistically significant difference in gender distribution across years (Figure).

A greater proportion of speakers selected by an open call process were women compared to when speakers were selected by a closed call process (261 (47%) vs 244 (34%); P < .0001).

Of didactics or workshops given in a group format (N = 299), 82 (27%) were given by all-men groups and 38 (13%) were given by all-women groups. Women speakers participating in all-women group talks accounted for 21% of all women speakers; whereas men speakers participating in all-men group talks account for 26% of all men speakers (P = .02). We found that all-men group speaking opportunities did decrease from 41% of group talks in 2015 to 21% of group talks in 2019 (P = .0065).

We saw an average 3% annual increase in women speakers from 2015 to 2019, an 8% increase from 2018 to 2019 for all speakers, and an 11% increase in women speakers specific to didactic sessions. Overall conference ratings increased from a mean of 4.3 ± 0.24 in 2015 to a mean of 4.6 ± 0.14 in 2019 (n = 1,202; P < .0001; Figure).

DISCUSSION

The important findings of this study are that there has been an increase in women speakers over the last 5 years at the annual Society of Hospital Medicine Conference, that women had higher representation as speakers when open call processes were followed, and that conference scores continued to improve during the time frame studied. These findings suggest that a systematic open call process helps to support equitable speaking opportunities for men and women at a national hospital medicine conference without a negative impact on conference quality.

To recruit more diverse speakers, open call and peer review processes were used in addition to deliberate efforts at ensuring diversity in speakers. We found that over time, the proportion of women with speaking opportunities increased from 2015 to 2019. Interestingly, workshops, which had open call processes in place for the duration of the study period, had almost equal numbers of men and women presenting in all years. We also found that the number of all-men speaking groups decreased between 2015 and 2019.

A single process change can impact gender equity, but the target of true equity is expected to require additional measures such as assessment of committee structures and diversity, checklists, and reporting structures (data analysis and plans when goals not achieved).^10-13 For instance, the American Society for Microbiology General Meeting was able to achieve gender equity in speakers by a multifold approach including ensuring the program committee was aware of gender statistics, increasing female representation among session convener teams, and direct instruction to try to avoid all-male sessions.¹¹

It is important to acknowledge that these processes do require valuable resources including time. SHM has historically used committee volunteers to conduct the peer review process with each committee member reviewing 20 to 30 workshop submissions and 30 to 50 didactic sessions. While open processes with peer review seem to generate improved gender equity, ensuring processes are in place during the selection process is also key.

Several recent notable efforts to enhance gender equity and to increase diversity have been proposed. One such example of a process that may further improve gender equity was proposed by editors at the Journal of Hospital Medicine to assess current representation via demographics including gender, race, and ethnicity of authors with plans to assess patterns in the coming years.¹⁴ The American College of Physicians also published a position paper on achieving gender equity with a recommendation that organizational policies and procedures should be implemented that address implicit bias.¹⁵

Our study showed that, from 2015 to 2019, conference evaluations saw a significant increase in the score concurrently with the rise in proportion of women speakers. This finding suggests that quality does not seem to be affected by this new methodology for speaker selection and in fact this methodology may actually help improve the overall quality of the conference. To our knowledge, this is one of the first studies to concurrently evaluate speaker gender equity with conference quality.

Our study offers several strengths. This study took a pragmatic approach to understanding how processes can impact gender equity, and we were able to take advantage of the evolution of the open call system (ie workshops which have been an open call process for the duration of the study versus speaking opportunities that were not).

Our study also has several limitations. First, this study is retrospective in nature and thus other processes could have contributed to the improved gender equity, such as an organization’s priorities over time. During this study period, the SHM conference saw an average 3% increase annually in women speakers and an increase of 8% from 2018 to 2019 for all speakers compared to national trends of approximately 1%,⁶ which suggests that the open call processes in place could be contributing to the overall increases seen. Similarly, because of the retrospective nature of the study, we cannot be certain that the improvements in conference scores were directly the result of improved gender equity, although it does suggest that the improvements in gender equity did not have an adverse impact on the scores. We also did not assess how the composition of selection committee members for the meeting could have impacted the overall composition of the speakers. Our study looked at diversity only from the perspective of gender in a binary fashion, and thus additional studies are needed to assess how to improve diversity overall. It is unclear how this new open call for speakers affects race and ethnic diversity specifically. Identifying gender for the purposes of this study was facilitated by speakers providing their own biographies and the respective pronouns used in those biographies, and thus gender was easier to ascertain than race and ethnicity, which are not as readily available. For organizations to understand their diversity, equity, and inclusion efforts, enhancing the ability to fairly track and measure diversity will be key. Lastly, understanding of the exact composition of hospitalists from both a gender and race/ethnicity perspective is lacking. Studies have suggested that, based upon those surveyed or studied, there is a fairly equal balance of men and women albeit in academic groups.³

 

CONCLUSIONS

An open call approach to speakers at a national hospitalist conference seems to have contributed to improvements regarding gender equity in speaking opportunities with a concurrent improvement in overall rating of the conference. The open call system is a potential mechanism that other institutions and organizations could employ to enhance their diversity efforts.

Acknowledgments

Society of Hospital Medicine Diversity, Equity, Inclusion Special Interest Group

Work Group for SPEAK UP: Marisha Burden, MD, Daniel Cabrera, MD, Amira del Pino-Jones, MD, Areeba Kara, MD, Angela Keniston, MSPH, Keshav Khanijow, MD, Flora Kisuule, MD, Chiara Mandel, Benji Mathews, MD, David Paje, MD, Stephan Papp, MD, Snehal Patel, MD, Suchita Shah Sata, MD, Dustin Smith, MD, Kevin Vuernick

Persistent gender disparities exist in pay,^1,2 leadership opportunities,^3,4 promotion,⁵ and speaking opportunities.⁶ While the gender distribution of the hospitalist workforce may be approaching parity,^3,7,8 gender differences in leadership, speakership, and authorship have already been noted in hospital medicine.³ Between 2006 and 2012, women constituted less than a third (26%) of the presenters at the national conferences of the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM).³

The SHM Annual Meeting has historically had an “open call” peer review process for workshop presenters with the goal of increasing the diversity of presenters. In 2019, this process was expanded to include didactic speakers. Our aim in this study was to assess whether these open call procedures resulted in improved representation of women speakers and how the proportion of women speakers affects the overall evaluation scores of the conference. Our hypothesis was that the introduction of an open call process for the SHM conference didactic speakers would be associated with an increased proportion of women speakers, compared with the closed call processes, without a negative impact on conference scores.

METHODS

The study is a retrospective evaluation of data collected regarding speakers at the annual SHM conference from 2015 to 2019. The SHM national conference typically has two main types of offerings: workshops and didactics. Workshop presenters from 2015 to 2019 were selected via an open call process as defined below. Didactic speakers (except for plenary speakers) were selected using the open call process for 2019 only.

We aimed to compare (1) the number and proportion of women speakers, compared with men speakers, over time and (2) the proportion of women speakers when open call processes were utilized versus that seen with closed call processes. Open call included workshops for all years and didactics for 2019; closed call included didactics for 2015 to 2018 and plenary sessions 2015 to 2019 (Table). The speaker list for the conferences was obtained from conference pamphlets or agendas available via Internet searches or obtained through attendance at the conference.

Speaker Categories and Identification Process

We determined whether each individual was a featured speaker (one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), whether they spoke in a group format, and whether the speaking opportunity type was a workshop or a didactic session. Numbers of featured and plenary speakers were combined because of low numbers. SHM provided deidentified conference evaluation data for each year studied. For the purposes of this study, we analyzed all speakers which included physicians, advanced practice providers, and professionals such as nurses and other interdisciplinary team members. The same speaker could be included multiple times if they had multiple speaking opportunities.

 

Open Call Process

We defined the “open call process” (referred to as “open call” here forward) as the process utilized by SHM that includes the following two components: (1) advertisements to members of SHM and to the medical community at large through a variety of mechanisms including emails, websites, and social media outlets and (2) an online submission process that includes names of proposed speakers and their topic and, in the case of workshops, session objectives as well as an outline of the proposed workshop. SHM committees may also submit suggestions for topics and speakers. Annual Conference Committee members then review and rate submissions on the categories of topic, organization and clarity, objectives, and speaker qualifications (with a focus on institutional, geographic, and gender diversity). Scores are assigned from 1 to 5 (with 5 being the best score) for each category and a section for comments is available. All submissions are also evaluated by the course director.

After initial committee reviews, scores with marked reviewer discrepancies are rereviewed and discussed by the committee and course director. A cutoff score is then calculated with proposals falling below the cutoff threshold omitted from further consideration. Weekly calls are then focused on subcategories (ie tracks) with emphasis on clinical and educational content. Each of the tracks have a subcommittee with track leads to curate the best content first and then focus on final speaker selection. More recently, templates are shared with the track leads that include a location to call out gender and institutional diversity. Weekly calls are held to hone the content and determine the speakers.

For the purposes of this study, when the above process was not used, the authors refer to it as “closed call.” Closed call processes do not typically involve open invitations or a peer review process. (Table)

Gender

Gender was assigned based on the speaker’s self-identification by the pronouns used in their biography submitted to the conference or on their institutional website or other websites where the speaker was referenced. Persons using she/her/hers pronouns were noted as women and persons using he/him/his were noted as men. For the purposes of this study, we conceptualized gender as binary (ie woman/man) given the limited information we had from online sources.

ANALYSIS

REDCap, a secure, Web-based application for building and managing online survey and databases, was used to collect and manage all study data.⁹

All analyses were performed using SAS Enterprise Guide 8.1 (SAS Institute, Inc., Cary, North Carolina) using retrospectively collected data. A Cochran-Armitage test for trend was used to evaluate the proportion of women speakers from 2015 to 2019. A chi-square test was used to assess the proportion of women speakers for open call processes versus that seen with closed call. One-way analysis of variance (ANOVA) was used to evaluate annual conference evaluation scores from 2015 to 2019. Either numbers with proportions or means with standard deviations have been reported. Bonferroni’s correction for multiple comparisons was applied, with a P < .008 considered statistically significant.

 

RESULTS

Between 2015 and 2019, a total of 709 workshop and didactic presentations were given by 1,261 speakers at the annual Society of Hospital Medicine Conference. Of these, 505 (40%) were women; 756 (60%) were men. There were no missing data.

From 2015 to 2019, representation of women speakers increased from 35% of all speakers to 47% of all speakers (P = .0068). Women plenary speakers increased from 23% in 2015 to 45% in 2019 (P = .0396).

The proportion of women presenters for workshops (which have utilized an open call process throughout the study period), ranged from 43% to 53% from 2015 to 2019 with no statistically significant difference in gender distribution across years (Figure).

A greater proportion of speakers selected by an open call process were women compared to when speakers were selected by a closed call process (261 (47%) vs 244 (34%); P < .0001).

Of didactics or workshops given in a group format (N = 299), 82 (27%) were given by all-men groups and 38 (13%) were given by all-women groups. Women speakers participating in all-women group talks accounted for 21% of all women speakers; whereas men speakers participating in all-men group talks account for 26% of all men speakers (P = .02). We found that all-men group speaking opportunities did decrease from 41% of group talks in 2015 to 21% of group talks in 2019 (P = .0065).

We saw an average 3% annual increase in women speakers from 2015 to 2019, an 8% increase from 2018 to 2019 for all speakers, and an 11% increase in women speakers specific to didactic sessions. Overall conference ratings increased from a mean of 4.3 ± 0.24 in 2015 to a mean of 4.6 ± 0.14 in 2019 (n = 1,202; P < .0001; Figure).

DISCUSSION

The important findings of this study are that there has been an increase in women speakers over the last 5 years at the annual Society of Hospital Medicine Conference, that women had higher representation as speakers when open call processes were followed, and that conference scores continued to improve during the time frame studied. These findings suggest that a systematic open call process helps to support equitable speaking opportunities for men and women at a national hospital medicine conference without a negative impact on conference quality.

To recruit more diverse speakers, open call and peer review processes were used in addition to deliberate efforts at ensuring diversity in speakers. We found that over time, the proportion of women with speaking opportunities increased from 2015 to 2019. Interestingly, workshops, which had open call processes in place for the duration of the study period, had almost equal numbers of men and women presenting in all years. We also found that the number of all-men speaking groups decreased between 2015 and 2019.

A single process change can impact gender equity, but the target of true equity is expected to require additional measures such as assessment of committee structures and diversity, checklists, and reporting structures (data analysis and plans when goals not achieved).^10-13 For instance, the American Society for Microbiology General Meeting was able to achieve gender equity in speakers by a multifold approach including ensuring the program committee was aware of gender statistics, increasing female representation among session convener teams, and direct instruction to try to avoid all-male sessions.¹¹

It is important to acknowledge that these processes do require valuable resources including time. SHM has historically used committee volunteers to conduct the peer review process with each committee member reviewing 20 to 30 workshop submissions and 30 to 50 didactic sessions. While open processes with peer review seem to generate improved gender equity, ensuring processes are in place during the selection process is also key.

Several recent notable efforts to enhance gender equity and to increase diversity have been proposed. One such example of a process that may further improve gender equity was proposed by editors at the Journal of Hospital Medicine to assess current representation via demographics including gender, race, and ethnicity of authors with plans to assess patterns in the coming years.¹⁴ The American College of Physicians also published a position paper on achieving gender equity with a recommendation that organizational policies and procedures should be implemented that address implicit bias.¹⁵

Our study showed that, from 2015 to 2019, conference evaluations saw a significant increase in the score concurrently with the rise in proportion of women speakers. This finding suggests that quality does not seem to be affected by this new methodology for speaker selection and in fact this methodology may actually help improve the overall quality of the conference. To our knowledge, this is one of the first studies to concurrently evaluate speaker gender equity with conference quality.

Our study offers several strengths. This study took a pragmatic approach to understanding how processes can impact gender equity, and we were able to take advantage of the evolution of the open call system (ie workshops which have been an open call process for the duration of the study versus speaking opportunities that were not).

Our study also has several limitations. First, this study is retrospective in nature and thus other processes could have contributed to the improved gender equity, such as an organization’s priorities over time. During this study period, the SHM conference saw an average 3% increase annually in women speakers and an increase of 8% from 2018 to 2019 for all speakers compared to national trends of approximately 1%,⁶ which suggests that the open call processes in place could be contributing to the overall increases seen. Similarly, because of the retrospective nature of the study, we cannot be certain that the improvements in conference scores were directly the result of improved gender equity, although it does suggest that the improvements in gender equity did not have an adverse impact on the scores. We also did not assess how the composition of selection committee members for the meeting could have impacted the overall composition of the speakers. Our study looked at diversity only from the perspective of gender in a binary fashion, and thus additional studies are needed to assess how to improve diversity overall. It is unclear how this new open call for speakers affects race and ethnic diversity specifically. Identifying gender for the purposes of this study was facilitated by speakers providing their own biographies and the respective pronouns used in those biographies, and thus gender was easier to ascertain than race and ethnicity, which are not as readily available. For organizations to understand their diversity, equity, and inclusion efforts, enhancing the ability to fairly track and measure diversity will be key. Lastly, understanding of the exact composition of hospitalists from both a gender and race/ethnicity perspective is lacking. Studies have suggested that, based upon those surveyed or studied, there is a fairly equal balance of men and women albeit in academic groups.³

 

CONCLUSIONS

An open call approach to speakers at a national hospitalist conference seems to have contributed to improvements regarding gender equity in speaking opportunities with a concurrent improvement in overall rating of the conference. The open call system is a potential mechanism that other institutions and organizations could employ to enhance their diversity efforts.

Acknowledgments

Society of Hospital Medicine Diversity, Equity, Inclusion Special Interest Group

Work Group for SPEAK UP: Marisha Burden, MD, Daniel Cabrera, MD, Amira del Pino-Jones, MD, Areeba Kara, MD, Angela Keniston, MSPH, Keshav Khanijow, MD, Flora Kisuule, MD, Chiara Mandel, Benji Mathews, MD, David Paje, MD, Stephan Papp, MD, Snehal Patel, MD, Suchita Shah Sata, MD, Dustin Smith, MD, Kevin Vuernick

Persistent gender disparities exist in pay,^1,2 leadership opportunities,^3,4 promotion,⁵ and speaking opportunities.⁶ While the gender distribution of the hospitalist workforce may be approaching parity,^3,7,8 gender differences in leadership, speakership, and authorship have already been noted in hospital medicine.³ Between 2006 and 2012, women constituted less than a third (26%) of the presenters at the national conferences of the Society of Hospital Medicine (SHM) and the Society of General Internal Medicine (SGIM).³

The SHM Annual Meeting has historically had an “open call” peer review process for workshop presenters with the goal of increasing the diversity of presenters. In 2019, this process was expanded to include didactic speakers. Our aim in this study was to assess whether these open call procedures resulted in improved representation of women speakers and how the proportion of women speakers affects the overall evaluation scores of the conference. Our hypothesis was that the introduction of an open call process for the SHM conference didactic speakers would be associated with an increased proportion of women speakers, compared with the closed call processes, without a negative impact on conference scores.

METHODS

The study is a retrospective evaluation of data collected regarding speakers at the annual SHM conference from 2015 to 2019. The SHM national conference typically has two main types of offerings: workshops and didactics. Workshop presenters from 2015 to 2019 were selected via an open call process as defined below. Didactic speakers (except for plenary speakers) were selected using the open call process for 2019 only.

We aimed to compare (1) the number and proportion of women speakers, compared with men speakers, over time and (2) the proportion of women speakers when open call processes were utilized versus that seen with closed call processes. Open call included workshops for all years and didactics for 2019; closed call included didactics for 2015 to 2018 and plenary sessions 2015 to 2019 (Table). The speaker list for the conferences was obtained from conference pamphlets or agendas available via Internet searches or obtained through attendance at the conference.

Speaker Categories and Identification Process

We determined whether each individual was a featured speaker (one whose talk was unopposed by other sessions), plenary speaker (defined as such in the conference pamphlets), whether they spoke in a group format, and whether the speaking opportunity type was a workshop or a didactic session. Numbers of featured and plenary speakers were combined because of low numbers. SHM provided deidentified conference evaluation data for each year studied. For the purposes of this study, we analyzed all speakers which included physicians, advanced practice providers, and professionals such as nurses and other interdisciplinary team members. The same speaker could be included multiple times if they had multiple speaking opportunities.

 

Open Call Process

We defined the “open call process” (referred to as “open call” here forward) as the process utilized by SHM that includes the following two components: (1) advertisements to members of SHM and to the medical community at large through a variety of mechanisms including emails, websites, and social media outlets and (2) an online submission process that includes names of proposed speakers and their topic and, in the case of workshops, session objectives as well as an outline of the proposed workshop. SHM committees may also submit suggestions for topics and speakers. Annual Conference Committee members then review and rate submissions on the categories of topic, organization and clarity, objectives, and speaker qualifications (with a focus on institutional, geographic, and gender diversity). Scores are assigned from 1 to 5 (with 5 being the best score) for each category and a section for comments is available. All submissions are also evaluated by the course director.

After initial committee reviews, scores with marked reviewer discrepancies are rereviewed and discussed by the committee and course director. A cutoff score is then calculated with proposals falling below the cutoff threshold omitted from further consideration. Weekly calls are then focused on subcategories (ie tracks) with emphasis on clinical and educational content. Each of the tracks have a subcommittee with track leads to curate the best content first and then focus on final speaker selection. More recently, templates are shared with the track leads that include a location to call out gender and institutional diversity. Weekly calls are held to hone the content and determine the speakers.

For the purposes of this study, when the above process was not used, the authors refer to it as “closed call.” Closed call processes do not typically involve open invitations or a peer review process. (Table)

Gender

Gender was assigned based on the speaker’s self-identification by the pronouns used in their biography submitted to the conference or on their institutional website or other websites where the speaker was referenced. Persons using she/her/hers pronouns were noted as women and persons using he/him/his were noted as men. For the purposes of this study, we conceptualized gender as binary (ie woman/man) given the limited information we had from online sources.

ANALYSIS

REDCap, a secure, Web-based application for building and managing online survey and databases, was used to collect and manage all study data.⁹

All analyses were performed using SAS Enterprise Guide 8.1 (SAS Institute, Inc., Cary, North Carolina) using retrospectively collected data. A Cochran-Armitage test for trend was used to evaluate the proportion of women speakers from 2015 to 2019. A chi-square test was used to assess the proportion of women speakers for open call processes versus that seen with closed call. One-way analysis of variance (ANOVA) was used to evaluate annual conference evaluation scores from 2015 to 2019. Either numbers with proportions or means with standard deviations have been reported. Bonferroni’s correction for multiple comparisons was applied, with a P < .008 considered statistically significant.

 

RESULTS

Between 2015 and 2019, a total of 709 workshop and didactic presentations were given by 1,261 speakers at the annual Society of Hospital Medicine Conference. Of these, 505 (40%) were women; 756 (60%) were men. There were no missing data.

From 2015 to 2019, representation of women speakers increased from 35% of all speakers to 47% of all speakers (P = .0068). Women plenary speakers increased from 23% in 2015 to 45% in 2019 (P = .0396).

The proportion of women presenters for workshops (which have utilized an open call process throughout the study period), ranged from 43% to 53% from 2015 to 2019 with no statistically significant difference in gender distribution across years (Figure).

A greater proportion of speakers selected by an open call process were women compared to when speakers were selected by a closed call process (261 (47%) vs 244 (34%); P < .0001).

Of didactics or workshops given in a group format (N = 299), 82 (27%) were given by all-men groups and 38 (13%) were given by all-women groups. Women speakers participating in all-women group talks accounted for 21% of all women speakers; whereas men speakers participating in all-men group talks account for 26% of all men speakers (P = .02). We found that all-men group speaking opportunities did decrease from 41% of group talks in 2015 to 21% of group talks in 2019 (P = .0065).

We saw an average 3% annual increase in women speakers from 2015 to 2019, an 8% increase from 2018 to 2019 for all speakers, and an 11% increase in women speakers specific to didactic sessions. Overall conference ratings increased from a mean of 4.3 ± 0.24 in 2015 to a mean of 4.6 ± 0.14 in 2019 (n = 1,202; P < .0001; Figure).

DISCUSSION

The important findings of this study are that there has been an increase in women speakers over the last 5 years at the annual Society of Hospital Medicine Conference, that women had higher representation as speakers when open call processes were followed, and that conference scores continued to improve during the time frame studied. These findings suggest that a systematic open call process helps to support equitable speaking opportunities for men and women at a national hospital medicine conference without a negative impact on conference quality.

To recruit more diverse speakers, open call and peer review processes were used in addition to deliberate efforts at ensuring diversity in speakers. We found that over time, the proportion of women with speaking opportunities increased from 2015 to 2019. Interestingly, workshops, which had open call processes in place for the duration of the study period, had almost equal numbers of men and women presenting in all years. We also found that the number of all-men speaking groups decreased between 2015 and 2019.

A single process change can impact gender equity, but the target of true equity is expected to require additional measures such as assessment of committee structures and diversity, checklists, and reporting structures (data analysis and plans when goals not achieved).^10-13 For instance, the American Society for Microbiology General Meeting was able to achieve gender equity in speakers by a multifold approach including ensuring the program committee was aware of gender statistics, increasing female representation among session convener teams, and direct instruction to try to avoid all-male sessions.¹¹

It is important to acknowledge that these processes do require valuable resources including time. SHM has historically used committee volunteers to conduct the peer review process with each committee member reviewing 20 to 30 workshop submissions and 30 to 50 didactic sessions. While open processes with peer review seem to generate improved gender equity, ensuring processes are in place during the selection process is also key.

Several recent notable efforts to enhance gender equity and to increase diversity have been proposed. One such example of a process that may further improve gender equity was proposed by editors at the Journal of Hospital Medicine to assess current representation via demographics including gender, race, and ethnicity of authors with plans to assess patterns in the coming years.¹⁴ The American College of Physicians also published a position paper on achieving gender equity with a recommendation that organizational policies and procedures should be implemented that address implicit bias.¹⁵

Our study showed that, from 2015 to 2019, conference evaluations saw a significant increase in the score concurrently with the rise in proportion of women speakers. This finding suggests that quality does not seem to be affected by this new methodology for speaker selection and in fact this methodology may actually help improve the overall quality of the conference. To our knowledge, this is one of the first studies to concurrently evaluate speaker gender equity with conference quality.

Our study offers several strengths. This study took a pragmatic approach to understanding how processes can impact gender equity, and we were able to take advantage of the evolution of the open call system (ie workshops which have been an open call process for the duration of the study versus speaking opportunities that were not).

Our study also has several limitations. First, this study is retrospective in nature and thus other processes could have contributed to the improved gender equity, such as an organization’s priorities over time. During this study period, the SHM conference saw an average 3% increase annually in women speakers and an increase of 8% from 2018 to 2019 for all speakers compared to national trends of approximately 1%,⁶ which suggests that the open call processes in place could be contributing to the overall increases seen. Similarly, because of the retrospective nature of the study, we cannot be certain that the improvements in conference scores were directly the result of improved gender equity, although it does suggest that the improvements in gender equity did not have an adverse impact on the scores. We also did not assess how the composition of selection committee members for the meeting could have impacted the overall composition of the speakers. Our study looked at diversity only from the perspective of gender in a binary fashion, and thus additional studies are needed to assess how to improve diversity overall. It is unclear how this new open call for speakers affects race and ethnic diversity specifically. Identifying gender for the purposes of this study was facilitated by speakers providing their own biographies and the respective pronouns used in those biographies, and thus gender was easier to ascertain than race and ethnicity, which are not as readily available. For organizations to understand their diversity, equity, and inclusion efforts, enhancing the ability to fairly track and measure diversity will be key. Lastly, understanding of the exact composition of hospitalists from both a gender and race/ethnicity perspective is lacking. Studies have suggested that, based upon those surveyed or studied, there is a fairly equal balance of men and women albeit in academic groups.³

 

CONCLUSIONS

An open call approach to speakers at a national hospitalist conference seems to have contributed to improvements regarding gender equity in speaking opportunities with a concurrent improvement in overall rating of the conference. The open call system is a potential mechanism that other institutions and organizations could employ to enhance their diversity efforts.

Acknowledgments

Society of Hospital Medicine Diversity, Equity, Inclusion Special Interest Group

Work Group for SPEAK UP: Marisha Burden, MD, Daniel Cabrera, MD, Amira del Pino-Jones, MD, Areeba Kara, MD, Angela Keniston, MSPH, Keshav Khanijow, MD, Flora Kisuule, MD, Chiara Mandel, Benji Mathews, MD, David Paje, MD, Stephan Papp, MD, Snehal Patel, MD, Suchita Shah Sata, MD, Dustin Smith, MD, Kevin Vuernick

Amid the continued shift from fee-for-service toward value-based payment, policymakers such as the Centers for Medicare & Medicaid Services have initiated strategies to contain spending on episodes of care. This episode focus has led to nationwide implementation of payment models such as bundled payments, which hold hospitals accountable for quality and costs across procedure-­based (eg, coronary artery bypass surgery) and condition-­based (eg, congestive heart failure) episodes, which begin with hospitalization and encompass subsequent hospital and postdischarge care.

Simultaneously, Medicare has increased its emphasis on similarly designed episodes of care (eg, those spanning hospitalization and postdischarge care) using other strategies, such as public reporting and use of episode-based measures to evaluate hospital cost performance. In 2017, Medicare trialed the implementation of six Clinical Episode-Based Payment (CEBP) measures in the national Hospital Inpatient Quality Reporting Program in order to assess hospital and clinician spending on procedure and condition episodes.^1,2

CEBP measures reflect episode-specific spending, conveying “how expensive a hospital is” by capturing facility and professional payments for a given episode spanning between 3 days prior to hospitalization and 30 days following discharge. Given standard payment rates used in Medicare, the variation in episode spending reflects differences in quantity and type of services utilized within an episode. Medicare has specified episode-related services and designed CEBP measures via logic and definition rules informed by a combination of claims and procedures-based grouping, as well as by physician input. For example, the CEBP measure for cellulitis encompasses services related to diagnosing and treating the infection within the episode window, but not unrelated services such as eye exams for coexisting glaucoma. To increase clinical salience, CEBP measures are subdivided to reflect differing complexity when possible. For instance, cellulitis measures are divided into episodes with or without major complications or comorbidities and further subdivided into subtypes for episodes reflecting cellulitis in patients with diabetes, patients with decubitus ulcers, or neither.

CEBPs are similar to other spending measures used in payment programs, such as the Medicare Spending Per Beneficiary, but are more clinically relevant because their focus on episodes more closely reflects clinical practice. CEBPs and Medicare Spending Per Beneficiary have similar designs (eg, same episode windows) and purpose (eg, to capture the cost efficiency of hospital care).³ However, unlike CEBPs, Medicare Spending Per Beneficiary is a “global” measure that summarizes a hospital’s cost efficiency aggregated across all inpatient episodes rather than represent it based on specific conditions or procedures.⁴ The limitations of publicly reported global hospital measures—for instance, the poor correlation between hospital performance on distinct publicly reported quality measures⁵—highlight the potential utility of episode-specific spending measures such as CEBP.

Compared with episode-based payment models, initiatives such as CEBP measures have gone largely unstudied. However, they represent signals of Medicare’s growing commitment to addressing care episodes, tested without potentially tedious rulemaking required to change payment. In fact, publicly reported episode spending measures offer policymakers several interrelated benefits: the ability to rapidly evaluate performance at a large number of hospitals (eg, Medicare scaling up CEBP measures among all eligible hospitals nationwide), the option of leveraging publicly reported feedback to prompt clinical improvements (eg, by including CEBP measures in the Hospital Inpatient Quality Reporting Program), and the platform for developing and testing promising spending measures for subsequent use in formal payment models (eg, by using CEBP measures that possess large variation or cost-reduction opportunities in future bundled payment programs).

Despite these benefits, little is known about hospital performance on publicly reported episode-specific spending measures. We addressed this knowledge gap by providing what is, to our knowledge, the first nationwide description of hospital performance on such measures. We also evaluated which episode components accounted for spending variation in procedural vs condition episodes, examined whether CEBP measures can be used to effectively identify high- vs low-cost hospitals, and compared spending performance on CEBPs vs Medicare Spending Per Beneficiary.

 

METHODS

Data and Study Sample

We utilized publicly available data from Hospital Compare, which include information about hospital-level CEBP and Medicare Spending Per Beneficiary performance for Medicare-­certified acute care hospitals nationwide.⁵ Our analysis evaluated the six CEBP measures tested by Medicare in 2017: three conditions (cellulitis, kidney/urinary tract infection [UTI], gastrointestinal hemorrhage) and three procedures (spinal fusion, cholecystectomy and common duct exploration, and aortic aneurysm repair). Per Medicare rules, CEBP measures are calculated only for hospitals with requisite volume for targeted conditions (minimum of 40 episodes) and procedures (minimum of 25 episodes) and are reported on Hospital Compare in risk-adjusted (eg, for age, hierarchical condition categories in alignment with existing Medicare methodology) and payment-­standardized form (ie, accounts for wage index, medical education, disproportionate share hospital payments) . Each CEBP encompasses episodes with or without major complications/comorbidities.

For each hospital, CEBP spending is reported as average total episode spending, as well as average spending on specific components. We grouped components into three groups: hospitalization, skilled nursing facility (SNF) use, and other (encompassing postdischarge readmissions, emergency department visits, and home health agency use), with a focus on SNF given existing evidence from episode-based payment models about the opportunity for savings from reduced SNF care. Hospital Compare also provides information about the national CEBP measure performance (ie, average spending for a given episode type among all eligible hospitals nationwide).

Hospital Groups

To evaluate hospitals’ CEBP performance for specific episode types, we categorized hospitals as either “below average spending” if their average episode spending was below the national average or “above average spending” if spending was above the national average. According to this approach, a hospital could have below average spending for some episodes but above average spending for others.

To compare hospitals across episode types simultaneously, we categorized hospitals as “low cost” if episode spending was below the national average for all applicable measures, “high cost” if episode spending was above the national average for all applicable measures, or “mixed cost” if episode spending was above the national average for some measures and below for others.

We also conducted sensitivity analyses using alternative hospital group definitions. For comparisons of specific episode types, we categorized hospitals as “high spending” (top quartile of average episode spending among eligible hospitals) or “other spending” (all others). For comparisons across all episode types, we focused on SNF care and categorized hospitals as “high SNF cost” (top quartile of episode spending attributed to SNF care) and “other SNF cost” (all others). We applied a similar approach to Medicare Spending Per Beneficiary, categorizing hospitals as either “low MSPB cost” if their episode spending was below the national average for Medicare Spending Per Beneficiary or “high MSPB cost” if not.

Statistical Analysis

We assessed variation by describing the distribution of total episode spending across eligible hospitals for each individual episode type, as well as the proportion of spending attributed to SNF care across all episode types. We reported the difference between the 10th and 90th percentile for each distribution to quantify variation. To evaluate how individual episode components contributed to overall spending variation, we used linear regression and applied analysis of variance to each episode component. Specifically, we regressed episode spending on each episode component (hospital, SNF, other) separately and used these results to generate predicted episode spending values for each hospital based on its value for each spending component. We then calculated the differen-ces (ie, residuals) between predicted and actual total episode spending values. We plotted residuals for each component, with lower residual plot variation (ie, a flatter curve) representing larger contribution of a spending component to overall spending variation.

 

Pearson correlation coefficients were used to assess within-­hospital CEBP correlation (ie, the extent to which performance was hospital specific). We evaluated if and how components of spending varied across hospitals by comparing spending groups (for individual episode types) and cost groups (for all episode types). To test the robustness of these categories, we conducted sensitivity analyses using high spending vs other spending groups (for individual episode types) and high SNF cost vs low SNF cost groups (for all episode types).

To assess concordance between CEBP and Medicare Spending Per Beneficiary, we cross tabulated hospital CEBP performance (high vs low vs mixed cost) and Medicare Spending Per Beneficiary performance (high vs low MSPB cost). This approached allowed us to quantify the number of hospitals that have concordant performance for both types of spending measures (ie, high cost or low cost on both) and the number with discordant performance (eg, high cost on one spending measure but low cost on the other). We used Pearson correlation coefficients to assess correlation between CEBP and Medicare Spending Per Beneficiary, with evaluation of CEBP performance in aggregate form (ie, hospitals’ average CEBP performance across all eligible episode types) and by individual episode types.

Chi-square and Kruskal-Wallis tests were used to compare categorical and continuous variables, respectively. To compare spending amounts, we evaluated the distribution of total episode spending (Appendix Figure 1) and used ordinary least squares regression with spending as the dependent variable and hospital group, episode components, and their interaction as independent variables. Because CEBP dollar amounts are reported through Hospital Compare on a risk-adjusted and payment-standardized basis, no additional adjustments were applied. Analyses were performed using SAS version 9.4 (SAS Institute; Cary, NC) and all tests of significance were two-tailed at alpha=0.05.

RESULTS

Of 3,129 hospitals, 1,778 achieved minimum thresholds and had CEBPs calculated for at least one of the six CEBP episode types.

Variation in CEBP Performance

For each episode type, spending varied across eligible hospitals (Appendix Figure 2). In particular, the difference between the 10th and 90th percentile values for cellulitis, kidney/UTI, and gastrointestinal hemorrhage were $2,873, $3,514, and $2,982, respectively. Differences were greater for procedural episodes of aortic aneurysm ($17,860), spinal fusion ($11,893), and cholecystectomy ($3,689). Evaluated across all episode types, the proportion of episode spending attributed to SNF care also varied across hospitals (Appendix Figure 3), with a difference of 24.7% between the 10th (4.5%) and 90th (29.2%) percentile values.

Residual plots demonstrated differences in which episode components accounted for variation in overall spending. For aortic aneurysm episodes, variation in the SNF episode component best explained variation in episode spending and thus had the lowest residual plot variation, followed by other and hospital components (Figure). Similar patterns were observed for spinal fusion and cholecystectomy episodes. In contrast, for cellulitis episodes, all three components had comparable residual-plot variation, which indicates that the variation in the components explained episode spending variation similarly (Figure)—a pattern reflected in kidney/UTI and gastrointestinal hemorrhage episodes.

Correlation in Performance on CEBP Measures

 

Across hospitals in our sample, within-hospital correlations were generally low (Appendix Table 1). In particular, correlations ranged from –0.079 (between performance on aortic aneurysm and kidney/UTI episodes) to 0.42 (between performance on kidney/UTI and cellulitis episodes), with a median correlation coefficient of 0.13. Within-hospital correlations ranged from 0.037 to 0.28 when considered between procedural episodes and from 0.33 to 0.42 when considered between condition episodes. When assessed among the subset of 1,294 hospitals eligible for at least two CEBP measures, correlations were very similar (ranging from –0.080 to 0.42). Additional analyses among hospitals with more CEBPs (eg, all six measures) yielded correlations that were similar in magnitude.

CEBP Performance by Hospital Groups

Overall spending on specific episode types varied across hospital groups (Table). Spending for aortic aneurysm episodes was $42,633 at hospitals with above average spending and $37,730 at those with below average spending, while spending for spinal fusion episodes was $39,231 at those with above average spending and $34,832 at those with below average spending. In comparison, spending at hospitals deemed above and below average spending for cellulitis episodes was $10,763 and $9,064, respectively, and $11,223 and $9,161 at hospitals deemed above and below average spending for kidney/UTI episodes, respectively.

Spending on specific episode components also differed by hospital group (Table). Though the magnitude of absolute spending amounts and differences varied by specific episode, hospitals with above average spending tended to spend more on SNF than did those with below average spending. For example, hospitals with above average spending for cellulitis episodes spent an average of $2,564 on SNF (24% of overall episode spending) vs $1,293 (14% of episode spending) among those with below average spending. Similarly, hospitals with above and below average spending for kidney/UTI episodes spent $4,068 (36% of episode spending) and $2,232 (24% of episode spending) on SNF, respectively (P < .001 for both episode types). Findings were qualitatively similar in sensitivity analyses (Appendix Table 2).

Among hospitals in our sample, we categorized 481 as high cost (27%), 452 as low cost (25%), and 845 as mixed cost (48%), with hospital groups distributed broadly nationwide (Appendix Figure 4). Evaluated on performance across all six episode types, hospital groups also demonstrated differences in spending by cost components (Table). In particular, spending in SNF ranged from 18.1% of overall episode spending among high-cost hospitals to 10.7% among mixed-cost hospitals and 9.2% among low-cost hospitals. Additionally, spending on hospitalization accounted for 83.3% of overall episode spending among low-cost hospitals, compared with 81.2% and 73.4% among mixed-cost and high-cost hospitals, respectively (P < .001). Comparisons were qualitatively similar in sensitivity analyses (Appendix Table 3).

Comparison of CEBP and Medicare Spending Per Beneficiary Performance

Correlation between Medicare Spending Per Beneficiary and aggregated CEBPs was 0.42 and, for individual episode types, ranged between 0.14 and 0.36 (Appendix Table 2). There was low concordance between hospital performance on CEBP and Medicare Spending Per Beneficiary. Across all eligible hospitals, only 16.3% (290/1778) had positive concordance between performance on the two measure types (ie, low cost for both), while 16.5% (293/1778) had negative concordance (ie, high cost for both). There was discordant performance in most instances (67.2%; 1195/1778), which reflecting favorable performance on one measure type but not the other.

 

DISCUSSION

To our knowledge, this study is the first to describe hospitals’ episode-specific spending performance nationwide. It demonstrated significant variation across hospitals driven by different episode components for different episode types. It also showed low correlation between individual episode spending measures and poor concordance between episode-specific and global hospital spending measures. Two practice and policy implications are noteworthy.

First, our findings corroborate and build upon evidence from bundled payment programs about the opportunity for hospitals to improve their cost efficiency. Findings from bundled payment evaluations of surgical episodes suggest that the major area for cost savings is in the reduction of institutional post-acute care use such as that of SNFs.^7-9 We demonstrated similar opportunity in a national sample of hospitals, finding that, for the three evaluated procedural CEBPs, SNF care accounted for more variation in overall episode spending than did other components. While variation may imply opportunity for greater efficiency and standardization, it is important to note that variation itself is not inherently problematic. Additional studies are needed to distinguish between warranted and unwarranted variation in procedural episodes, as well as identify strategies for reducing the latter.

Though bundled payment evaluations have predominantly emphasized procedural episodes, existing evidence suggests that participation in medical condition bundles has not been associated with cost savings or utilization changes.^7-15 Findings from our analysis of variance—that there appear to be smaller variation-reduction opportunities for condition episodes than for procedural episodes—offer insight into this issue. Existing episodes are initiated by hospitalization and extend into the postacute period, a design that may not afford substantial post-acute care savings opportunities for condition episodes. This is an important insight as policymakers consider how to best design condition-based episodes in the future (eg, whether to use non–hospital based episode triggers). Future work should evaluate whether our findings reflect inherent differences between condition and procedural episodes¹⁶ or whether interventions can still optimize SNF care for these episodes despite smaller variation.

Second, our results highlight the potential limitations of global performance measures such as Medicare Spending Per Beneficiary. As a general measure of hospital spending, Medicare Spending Per Beneficiary is based on the premise that hospitals can be categorized as high or low cost with consideration of all inpatient episodic care. However, our analyses suggest that hospitals may be high cost for certain episodes and low cost for others—a fact highlighted by the low correlation and high discordance observed between hospital CEBP and Medicare Spending Per Beneficiary performance. Because overarching measures may miss spending differen-ces related to underlying clinical scenarios, episode-specific spending measures would provide important perspective and complements to global measures for assessing hospital cost performance, particularly in an era of value-based payments. Policymakers should consider prioritizing the development and implementation of such measures.

Our study has limitations. First, it is descriptive in nature, and future work should evaluate the association between episode-­specific spending measure performance and clinical and quality outcomes. Second, we evaluated all CEBP-eligible hospitals nationwide to provide a broad view of episode-specific spending. However, future studies should assess performance among hospital subtypes, such as vertically integrated or safety-­net organizations, because they may be more or less able to perform on these spending measures. Third, though findings may not be generalizable to other clinical episodes, our results were qualitatively consistent across episode types and broadly consistent with evidence from episode-based payment models. Fourth, we analyzed cost from the perspective of utilization and did not incorporate price considerations, which may be more relevant for commercial insurers than it is for Medicare.

Nonetheless, the emergence of CEBPs reflects the ongoing shift in policymaker attention toward episode-specific spending. In particular, though further scale or use of CEBP measures has been put on hold amid other payment reform changes, their nationwide implementation in 2017 signals Medicare’s broad interest in evaluating all hospitals on episode-specific spending efficiency, in addition to other facets of spending, quality, safety, and patient experience. Importantly, such efforts complement other ongoing nationwide initiatives for emphasizing episode spending, such as use of episode-based cost measures within the Merit-Based Incentive Payment System¹⁷ to score clinicians and groups in part based on their episode-specific spending efficiency. Insight about episode spending performance could help hospitals prepare for environments with increasing focus on episode spending and as policymakers incorporate this perspective into quality and value-­based payment policies.

 

Amid the continued shift from fee-for-service toward value-based payment, policymakers such as the Centers for Medicare & Medicaid Services have initiated strategies to contain spending on episodes of care. This episode focus has led to nationwide implementation of payment models such as bundled payments, which hold hospitals accountable for quality and costs across procedure-­based (eg, coronary artery bypass surgery) and condition-­based (eg, congestive heart failure) episodes, which begin with hospitalization and encompass subsequent hospital and postdischarge care.

Simultaneously, Medicare has increased its emphasis on similarly designed episodes of care (eg, those spanning hospitalization and postdischarge care) using other strategies, such as public reporting and use of episode-based measures to evaluate hospital cost performance. In 2017, Medicare trialed the implementation of six Clinical Episode-Based Payment (CEBP) measures in the national Hospital Inpatient Quality Reporting Program in order to assess hospital and clinician spending on procedure and condition episodes.^1,2

CEBP measures reflect episode-specific spending, conveying “how expensive a hospital is” by capturing facility and professional payments for a given episode spanning between 3 days prior to hospitalization and 30 days following discharge. Given standard payment rates used in Medicare, the variation in episode spending reflects differences in quantity and type of services utilized within an episode. Medicare has specified episode-related services and designed CEBP measures via logic and definition rules informed by a combination of claims and procedures-based grouping, as well as by physician input. For example, the CEBP measure for cellulitis encompasses services related to diagnosing and treating the infection within the episode window, but not unrelated services such as eye exams for coexisting glaucoma. To increase clinical salience, CEBP measures are subdivided to reflect differing complexity when possible. For instance, cellulitis measures are divided into episodes with or without major complications or comorbidities and further subdivided into subtypes for episodes reflecting cellulitis in patients with diabetes, patients with decubitus ulcers, or neither.

CEBPs are similar to other spending measures used in payment programs, such as the Medicare Spending Per Beneficiary, but are more clinically relevant because their focus on episodes more closely reflects clinical practice. CEBPs and Medicare Spending Per Beneficiary have similar designs (eg, same episode windows) and purpose (eg, to capture the cost efficiency of hospital care).³ However, unlike CEBPs, Medicare Spending Per Beneficiary is a “global” measure that summarizes a hospital’s cost efficiency aggregated across all inpatient episodes rather than represent it based on specific conditions or procedures.⁴ The limitations of publicly reported global hospital measures—for instance, the poor correlation between hospital performance on distinct publicly reported quality measures⁵—highlight the potential utility of episode-specific spending measures such as CEBP.

Compared with episode-based payment models, initiatives such as CEBP measures have gone largely unstudied. However, they represent signals of Medicare’s growing commitment to addressing care episodes, tested without potentially tedious rulemaking required to change payment. In fact, publicly reported episode spending measures offer policymakers several interrelated benefits: the ability to rapidly evaluate performance at a large number of hospitals (eg, Medicare scaling up CEBP measures among all eligible hospitals nationwide), the option of leveraging publicly reported feedback to prompt clinical improvements (eg, by including CEBP measures in the Hospital Inpatient Quality Reporting Program), and the platform for developing and testing promising spending measures for subsequent use in formal payment models (eg, by using CEBP measures that possess large variation or cost-reduction opportunities in future bundled payment programs).

Despite these benefits, little is known about hospital performance on publicly reported episode-specific spending measures. We addressed this knowledge gap by providing what is, to our knowledge, the first nationwide description of hospital performance on such measures. We also evaluated which episode components accounted for spending variation in procedural vs condition episodes, examined whether CEBP measures can be used to effectively identify high- vs low-cost hospitals, and compared spending performance on CEBPs vs Medicare Spending Per Beneficiary.

 

METHODS

Data and Study Sample

We utilized publicly available data from Hospital Compare, which include information about hospital-level CEBP and Medicare Spending Per Beneficiary performance for Medicare-­certified acute care hospitals nationwide.⁵ Our analysis evaluated the six CEBP measures tested by Medicare in 2017: three conditions (cellulitis, kidney/urinary tract infection [UTI], gastrointestinal hemorrhage) and three procedures (spinal fusion, cholecystectomy and common duct exploration, and aortic aneurysm repair). Per Medicare rules, CEBP measures are calculated only for hospitals with requisite volume for targeted conditions (minimum of 40 episodes) and procedures (minimum of 25 episodes) and are reported on Hospital Compare in risk-adjusted (eg, for age, hierarchical condition categories in alignment with existing Medicare methodology) and payment-­standardized form (ie, accounts for wage index, medical education, disproportionate share hospital payments) . Each CEBP encompasses episodes with or without major complications/comorbidities.

For each hospital, CEBP spending is reported as average total episode spending, as well as average spending on specific components. We grouped components into three groups: hospitalization, skilled nursing facility (SNF) use, and other (encompassing postdischarge readmissions, emergency department visits, and home health agency use), with a focus on SNF given existing evidence from episode-based payment models about the opportunity for savings from reduced SNF care. Hospital Compare also provides information about the national CEBP measure performance (ie, average spending for a given episode type among all eligible hospitals nationwide).

Hospital Groups

To evaluate hospitals’ CEBP performance for specific episode types, we categorized hospitals as either “below average spending” if their average episode spending was below the national average or “above average spending” if spending was above the national average. According to this approach, a hospital could have below average spending for some episodes but above average spending for others.

To compare hospitals across episode types simultaneously, we categorized hospitals as “low cost” if episode spending was below the national average for all applicable measures, “high cost” if episode spending was above the national average for all applicable measures, or “mixed cost” if episode spending was above the national average for some measures and below for others.

We also conducted sensitivity analyses using alternative hospital group definitions. For comparisons of specific episode types, we categorized hospitals as “high spending” (top quartile of average episode spending among eligible hospitals) or “other spending” (all others). For comparisons across all episode types, we focused on SNF care and categorized hospitals as “high SNF cost” (top quartile of episode spending attributed to SNF care) and “other SNF cost” (all others). We applied a similar approach to Medicare Spending Per Beneficiary, categorizing hospitals as either “low MSPB cost” if their episode spending was below the national average for Medicare Spending Per Beneficiary or “high MSPB cost” if not.

Statistical Analysis

We assessed variation by describing the distribution of total episode spending across eligible hospitals for each individual episode type, as well as the proportion of spending attributed to SNF care across all episode types. We reported the difference between the 10th and 90th percentile for each distribution to quantify variation. To evaluate how individual episode components contributed to overall spending variation, we used linear regression and applied analysis of variance to each episode component. Specifically, we regressed episode spending on each episode component (hospital, SNF, other) separately and used these results to generate predicted episode spending values for each hospital based on its value for each spending component. We then calculated the differen-ces (ie, residuals) between predicted and actual total episode spending values. We plotted residuals for each component, with lower residual plot variation (ie, a flatter curve) representing larger contribution of a spending component to overall spending variation.

 

Pearson correlation coefficients were used to assess within-­hospital CEBP correlation (ie, the extent to which performance was hospital specific). We evaluated if and how components of spending varied across hospitals by comparing spending groups (for individual episode types) and cost groups (for all episode types). To test the robustness of these categories, we conducted sensitivity analyses using high spending vs other spending groups (for individual episode types) and high SNF cost vs low SNF cost groups (for all episode types).

To assess concordance between CEBP and Medicare Spending Per Beneficiary, we cross tabulated hospital CEBP performance (high vs low vs mixed cost) and Medicare Spending Per Beneficiary performance (high vs low MSPB cost). This approached allowed us to quantify the number of hospitals that have concordant performance for both types of spending measures (ie, high cost or low cost on both) and the number with discordant performance (eg, high cost on one spending measure but low cost on the other). We used Pearson correlation coefficients to assess correlation between CEBP and Medicare Spending Per Beneficiary, with evaluation of CEBP performance in aggregate form (ie, hospitals’ average CEBP performance across all eligible episode types) and by individual episode types.

Chi-square and Kruskal-Wallis tests were used to compare categorical and continuous variables, respectively. To compare spending amounts, we evaluated the distribution of total episode spending (Appendix Figure 1) and used ordinary least squares regression with spending as the dependent variable and hospital group, episode components, and their interaction as independent variables. Because CEBP dollar amounts are reported through Hospital Compare on a risk-adjusted and payment-standardized basis, no additional adjustments were applied. Analyses were performed using SAS version 9.4 (SAS Institute; Cary, NC) and all tests of significance were two-tailed at alpha=0.05.

RESULTS

Of 3,129 hospitals, 1,778 achieved minimum thresholds and had CEBPs calculated for at least one of the six CEBP episode types.

Variation in CEBP Performance

For each episode type, spending varied across eligible hospitals (Appendix Figure 2). In particular, the difference between the 10th and 90th percentile values for cellulitis, kidney/UTI, and gastrointestinal hemorrhage were $2,873, $3,514, and $2,982, respectively. Differences were greater for procedural episodes of aortic aneurysm ($17,860), spinal fusion ($11,893), and cholecystectomy ($3,689). Evaluated across all episode types, the proportion of episode spending attributed to SNF care also varied across hospitals (Appendix Figure 3), with a difference of 24.7% between the 10th (4.5%) and 90th (29.2%) percentile values.

Residual plots demonstrated differences in which episode components accounted for variation in overall spending. For aortic aneurysm episodes, variation in the SNF episode component best explained variation in episode spending and thus had the lowest residual plot variation, followed by other and hospital components (Figure). Similar patterns were observed for spinal fusion and cholecystectomy episodes. In contrast, for cellulitis episodes, all three components had comparable residual-plot variation, which indicates that the variation in the components explained episode spending variation similarly (Figure)—a pattern reflected in kidney/UTI and gastrointestinal hemorrhage episodes.

Correlation in Performance on CEBP Measures

 

Across hospitals in our sample, within-hospital correlations were generally low (Appendix Table 1). In particular, correlations ranged from –0.079 (between performance on aortic aneurysm and kidney/UTI episodes) to 0.42 (between performance on kidney/UTI and cellulitis episodes), with a median correlation coefficient of 0.13. Within-hospital correlations ranged from 0.037 to 0.28 when considered between procedural episodes and from 0.33 to 0.42 when considered between condition episodes. When assessed among the subset of 1,294 hospitals eligible for at least two CEBP measures, correlations were very similar (ranging from –0.080 to 0.42). Additional analyses among hospitals with more CEBPs (eg, all six measures) yielded correlations that were similar in magnitude.

CEBP Performance by Hospital Groups

Overall spending on specific episode types varied across hospital groups (Table). Spending for aortic aneurysm episodes was $42,633 at hospitals with above average spending and $37,730 at those with below average spending, while spending for spinal fusion episodes was $39,231 at those with above average spending and $34,832 at those with below average spending. In comparison, spending at hospitals deemed above and below average spending for cellulitis episodes was $10,763 and $9,064, respectively, and $11,223 and $9,161 at hospitals deemed above and below average spending for kidney/UTI episodes, respectively.

Spending on specific episode components also differed by hospital group (Table). Though the magnitude of absolute spending amounts and differences varied by specific episode, hospitals with above average spending tended to spend more on SNF than did those with below average spending. For example, hospitals with above average spending for cellulitis episodes spent an average of $2,564 on SNF (24% of overall episode spending) vs $1,293 (14% of episode spending) among those with below average spending. Similarly, hospitals with above and below average spending for kidney/UTI episodes spent $4,068 (36% of episode spending) and $2,232 (24% of episode spending) on SNF, respectively (P < .001 for both episode types). Findings were qualitatively similar in sensitivity analyses (Appendix Table 2).

Among hospitals in our sample, we categorized 481 as high cost (27%), 452 as low cost (25%), and 845 as mixed cost (48%), with hospital groups distributed broadly nationwide (Appendix Figure 4). Evaluated on performance across all six episode types, hospital groups also demonstrated differences in spending by cost components (Table). In particular, spending in SNF ranged from 18.1% of overall episode spending among high-cost hospitals to 10.7% among mixed-cost hospitals and 9.2% among low-cost hospitals. Additionally, spending on hospitalization accounted for 83.3% of overall episode spending among low-cost hospitals, compared with 81.2% and 73.4% among mixed-cost and high-cost hospitals, respectively (P < .001). Comparisons were qualitatively similar in sensitivity analyses (Appendix Table 3).

Comparison of CEBP and Medicare Spending Per Beneficiary Performance

Correlation between Medicare Spending Per Beneficiary and aggregated CEBPs was 0.42 and, for individual episode types, ranged between 0.14 and 0.36 (Appendix Table 2). There was low concordance between hospital performance on CEBP and Medicare Spending Per Beneficiary. Across all eligible hospitals, only 16.3% (290/1778) had positive concordance between performance on the two measure types (ie, low cost for both), while 16.5% (293/1778) had negative concordance (ie, high cost for both). There was discordant performance in most instances (67.2%; 1195/1778), which reflecting favorable performance on one measure type but not the other.

 

DISCUSSION

To our knowledge, this study is the first to describe hospitals’ episode-specific spending performance nationwide. It demonstrated significant variation across hospitals driven by different episode components for different episode types. It also showed low correlation between individual episode spending measures and poor concordance between episode-specific and global hospital spending measures. Two practice and policy implications are noteworthy.

First, our findings corroborate and build upon evidence from bundled payment programs about the opportunity for hospitals to improve their cost efficiency. Findings from bundled payment evaluations of surgical episodes suggest that the major area for cost savings is in the reduction of institutional post-acute care use such as that of SNFs.^7-9 We demonstrated similar opportunity in a national sample of hospitals, finding that, for the three evaluated procedural CEBPs, SNF care accounted for more variation in overall episode spending than did other components. While variation may imply opportunity for greater efficiency and standardization, it is important to note that variation itself is not inherently problematic. Additional studies are needed to distinguish between warranted and unwarranted variation in procedural episodes, as well as identify strategies for reducing the latter.

Though bundled payment evaluations have predominantly emphasized procedural episodes, existing evidence suggests that participation in medical condition bundles has not been associated with cost savings or utilization changes.^7-15 Findings from our analysis of variance—that there appear to be smaller variation-reduction opportunities for condition episodes than for procedural episodes—offer insight into this issue. Existing episodes are initiated by hospitalization and extend into the postacute period, a design that may not afford substantial post-acute care savings opportunities for condition episodes. This is an important insight as policymakers consider how to best design condition-based episodes in the future (eg, whether to use non–hospital based episode triggers). Future work should evaluate whether our findings reflect inherent differences between condition and procedural episodes¹⁶ or whether interventions can still optimize SNF care for these episodes despite smaller variation.

Second, our results highlight the potential limitations of global performance measures such as Medicare Spending Per Beneficiary. As a general measure of hospital spending, Medicare Spending Per Beneficiary is based on the premise that hospitals can be categorized as high or low cost with consideration of all inpatient episodic care. However, our analyses suggest that hospitals may be high cost for certain episodes and low cost for others—a fact highlighted by the low correlation and high discordance observed between hospital CEBP and Medicare Spending Per Beneficiary performance. Because overarching measures may miss spending differen-ces related to underlying clinical scenarios, episode-specific spending measures would provide important perspective and complements to global measures for assessing hospital cost performance, particularly in an era of value-based payments. Policymakers should consider prioritizing the development and implementation of such measures.

Our study has limitations. First, it is descriptive in nature, and future work should evaluate the association between episode-­specific spending measure performance and clinical and quality outcomes. Second, we evaluated all CEBP-eligible hospitals nationwide to provide a broad view of episode-specific spending. However, future studies should assess performance among hospital subtypes, such as vertically integrated or safety-­net organizations, because they may be more or less able to perform on these spending measures. Third, though findings may not be generalizable to other clinical episodes, our results were qualitatively consistent across episode types and broadly consistent with evidence from episode-based payment models. Fourth, we analyzed cost from the perspective of utilization and did not incorporate price considerations, which may be more relevant for commercial insurers than it is for Medicare.

Nonetheless, the emergence of CEBPs reflects the ongoing shift in policymaker attention toward episode-specific spending. In particular, though further scale or use of CEBP measures has been put on hold amid other payment reform changes, their nationwide implementation in 2017 signals Medicare’s broad interest in evaluating all hospitals on episode-specific spending efficiency, in addition to other facets of spending, quality, safety, and patient experience. Importantly, such efforts complement other ongoing nationwide initiatives for emphasizing episode spending, such as use of episode-based cost measures within the Merit-Based Incentive Payment System¹⁷ to score clinicians and groups in part based on their episode-specific spending efficiency. Insight about episode spending performance could help hospitals prepare for environments with increasing focus on episode spending and as policymakers incorporate this perspective into quality and value-­based payment policies.

 

Amid the continued shift from fee-for-service toward value-based payment, policymakers such as the Centers for Medicare & Medicaid Services have initiated strategies to contain spending on episodes of care. This episode focus has led to nationwide implementation of payment models such as bundled payments, which hold hospitals accountable for quality and costs across procedure-­based (eg, coronary artery bypass surgery) and condition-­based (eg, congestive heart failure) episodes, which begin with hospitalization and encompass subsequent hospital and postdischarge care.

Simultaneously, Medicare has increased its emphasis on similarly designed episodes of care (eg, those spanning hospitalization and postdischarge care) using other strategies, such as public reporting and use of episode-based measures to evaluate hospital cost performance. In 2017, Medicare trialed the implementation of six Clinical Episode-Based Payment (CEBP) measures in the national Hospital Inpatient Quality Reporting Program in order to assess hospital and clinician spending on procedure and condition episodes.^1,2

CEBP measures reflect episode-specific spending, conveying “how expensive a hospital is” by capturing facility and professional payments for a given episode spanning between 3 days prior to hospitalization and 30 days following discharge. Given standard payment rates used in Medicare, the variation in episode spending reflects differences in quantity and type of services utilized within an episode. Medicare has specified episode-related services and designed CEBP measures via logic and definition rules informed by a combination of claims and procedures-based grouping, as well as by physician input. For example, the CEBP measure for cellulitis encompasses services related to diagnosing and treating the infection within the episode window, but not unrelated services such as eye exams for coexisting glaucoma. To increase clinical salience, CEBP measures are subdivided to reflect differing complexity when possible. For instance, cellulitis measures are divided into episodes with or without major complications or comorbidities and further subdivided into subtypes for episodes reflecting cellulitis in patients with diabetes, patients with decubitus ulcers, or neither.

CEBPs are similar to other spending measures used in payment programs, such as the Medicare Spending Per Beneficiary, but are more clinically relevant because their focus on episodes more closely reflects clinical practice. CEBPs and Medicare Spending Per Beneficiary have similar designs (eg, same episode windows) and purpose (eg, to capture the cost efficiency of hospital care).³ However, unlike CEBPs, Medicare Spending Per Beneficiary is a “global” measure that summarizes a hospital’s cost efficiency aggregated across all inpatient episodes rather than represent it based on specific conditions or procedures.⁴ The limitations of publicly reported global hospital measures—for instance, the poor correlation between hospital performance on distinct publicly reported quality measures⁵—highlight the potential utility of episode-specific spending measures such as CEBP.

Compared with episode-based payment models, initiatives such as CEBP measures have gone largely unstudied. However, they represent signals of Medicare’s growing commitment to addressing care episodes, tested without potentially tedious rulemaking required to change payment. In fact, publicly reported episode spending measures offer policymakers several interrelated benefits: the ability to rapidly evaluate performance at a large number of hospitals (eg, Medicare scaling up CEBP measures among all eligible hospitals nationwide), the option of leveraging publicly reported feedback to prompt clinical improvements (eg, by including CEBP measures in the Hospital Inpatient Quality Reporting Program), and the platform for developing and testing promising spending measures for subsequent use in formal payment models (eg, by using CEBP measures that possess large variation or cost-reduction opportunities in future bundled payment programs).

Despite these benefits, little is known about hospital performance on publicly reported episode-specific spending measures. We addressed this knowledge gap by providing what is, to our knowledge, the first nationwide description of hospital performance on such measures. We also evaluated which episode components accounted for spending variation in procedural vs condition episodes, examined whether CEBP measures can be used to effectively identify high- vs low-cost hospitals, and compared spending performance on CEBPs vs Medicare Spending Per Beneficiary.

 

METHODS

Data and Study Sample

We utilized publicly available data from Hospital Compare, which include information about hospital-level CEBP and Medicare Spending Per Beneficiary performance for Medicare-­certified acute care hospitals nationwide.⁵ Our analysis evaluated the six CEBP measures tested by Medicare in 2017: three conditions (cellulitis, kidney/urinary tract infection [UTI], gastrointestinal hemorrhage) and three procedures (spinal fusion, cholecystectomy and common duct exploration, and aortic aneurysm repair). Per Medicare rules, CEBP measures are calculated only for hospitals with requisite volume for targeted conditions (minimum of 40 episodes) and procedures (minimum of 25 episodes) and are reported on Hospital Compare in risk-adjusted (eg, for age, hierarchical condition categories in alignment with existing Medicare methodology) and payment-­standardized form (ie, accounts for wage index, medical education, disproportionate share hospital payments) . Each CEBP encompasses episodes with or without major complications/comorbidities.

For each hospital, CEBP spending is reported as average total episode spending, as well as average spending on specific components. We grouped components into three groups: hospitalization, skilled nursing facility (SNF) use, and other (encompassing postdischarge readmissions, emergency department visits, and home health agency use), with a focus on SNF given existing evidence from episode-based payment models about the opportunity for savings from reduced SNF care. Hospital Compare also provides information about the national CEBP measure performance (ie, average spending for a given episode type among all eligible hospitals nationwide).

Hospital Groups

To evaluate hospitals’ CEBP performance for specific episode types, we categorized hospitals as either “below average spending” if their average episode spending was below the national average or “above average spending” if spending was above the national average. According to this approach, a hospital could have below average spending for some episodes but above average spending for others.

To compare hospitals across episode types simultaneously, we categorized hospitals as “low cost” if episode spending was below the national average for all applicable measures, “high cost” if episode spending was above the national average for all applicable measures, or “mixed cost” if episode spending was above the national average for some measures and below for others.

We also conducted sensitivity analyses using alternative hospital group definitions. For comparisons of specific episode types, we categorized hospitals as “high spending” (top quartile of average episode spending among eligible hospitals) or “other spending” (all others). For comparisons across all episode types, we focused on SNF care and categorized hospitals as “high SNF cost” (top quartile of episode spending attributed to SNF care) and “other SNF cost” (all others). We applied a similar approach to Medicare Spending Per Beneficiary, categorizing hospitals as either “low MSPB cost” if their episode spending was below the national average for Medicare Spending Per Beneficiary or “high MSPB cost” if not.

Statistical Analysis

We assessed variation by describing the distribution of total episode spending across eligible hospitals for each individual episode type, as well as the proportion of spending attributed to SNF care across all episode types. We reported the difference between the 10th and 90th percentile for each distribution to quantify variation. To evaluate how individual episode components contributed to overall spending variation, we used linear regression and applied analysis of variance to each episode component. Specifically, we regressed episode spending on each episode component (hospital, SNF, other) separately and used these results to generate predicted episode spending values for each hospital based on its value for each spending component. We then calculated the differen-ces (ie, residuals) between predicted and actual total episode spending values. We plotted residuals for each component, with lower residual plot variation (ie, a flatter curve) representing larger contribution of a spending component to overall spending variation.

 

Pearson correlation coefficients were used to assess within-­hospital CEBP correlation (ie, the extent to which performance was hospital specific). We evaluated if and how components of spending varied across hospitals by comparing spending groups (for individual episode types) and cost groups (for all episode types). To test the robustness of these categories, we conducted sensitivity analyses using high spending vs other spending groups (for individual episode types) and high SNF cost vs low SNF cost groups (for all episode types).

To assess concordance between CEBP and Medicare Spending Per Beneficiary, we cross tabulated hospital CEBP performance (high vs low vs mixed cost) and Medicare Spending Per Beneficiary performance (high vs low MSPB cost). This approached allowed us to quantify the number of hospitals that have concordant performance for both types of spending measures (ie, high cost or low cost on both) and the number with discordant performance (eg, high cost on one spending measure but low cost on the other). We used Pearson correlation coefficients to assess correlation between CEBP and Medicare Spending Per Beneficiary, with evaluation of CEBP performance in aggregate form (ie, hospitals’ average CEBP performance across all eligible episode types) and by individual episode types.

Chi-square and Kruskal-Wallis tests were used to compare categorical and continuous variables, respectively. To compare spending amounts, we evaluated the distribution of total episode spending (Appendix Figure 1) and used ordinary least squares regression with spending as the dependent variable and hospital group, episode components, and their interaction as independent variables. Because CEBP dollar amounts are reported through Hospital Compare on a risk-adjusted and payment-standardized basis, no additional adjustments were applied. Analyses were performed using SAS version 9.4 (SAS Institute; Cary, NC) and all tests of significance were two-tailed at alpha=0.05.

RESULTS

Of 3,129 hospitals, 1,778 achieved minimum thresholds and had CEBPs calculated for at least one of the six CEBP episode types.

Variation in CEBP Performance

For each episode type, spending varied across eligible hospitals (Appendix Figure 2). In particular, the difference between the 10th and 90th percentile values for cellulitis, kidney/UTI, and gastrointestinal hemorrhage were $2,873, $3,514, and $2,982, respectively. Differences were greater for procedural episodes of aortic aneurysm ($17,860), spinal fusion ($11,893), and cholecystectomy ($3,689). Evaluated across all episode types, the proportion of episode spending attributed to SNF care also varied across hospitals (Appendix Figure 3), with a difference of 24.7% between the 10th (4.5%) and 90th (29.2%) percentile values.

Residual plots demonstrated differences in which episode components accounted for variation in overall spending. For aortic aneurysm episodes, variation in the SNF episode component best explained variation in episode spending and thus had the lowest residual plot variation, followed by other and hospital components (Figure). Similar patterns were observed for spinal fusion and cholecystectomy episodes. In contrast, for cellulitis episodes, all three components had comparable residual-plot variation, which indicates that the variation in the components explained episode spending variation similarly (Figure)—a pattern reflected in kidney/UTI and gastrointestinal hemorrhage episodes.

Correlation in Performance on CEBP Measures

 

Across hospitals in our sample, within-hospital correlations were generally low (Appendix Table 1). In particular, correlations ranged from –0.079 (between performance on aortic aneurysm and kidney/UTI episodes) to 0.42 (between performance on kidney/UTI and cellulitis episodes), with a median correlation coefficient of 0.13. Within-hospital correlations ranged from 0.037 to 0.28 when considered between procedural episodes and from 0.33 to 0.42 when considered between condition episodes. When assessed among the subset of 1,294 hospitals eligible for at least two CEBP measures, correlations were very similar (ranging from –0.080 to 0.42). Additional analyses among hospitals with more CEBPs (eg, all six measures) yielded correlations that were similar in magnitude.

CEBP Performance by Hospital Groups

Overall spending on specific episode types varied across hospital groups (Table). Spending for aortic aneurysm episodes was $42,633 at hospitals with above average spending and $37,730 at those with below average spending, while spending for spinal fusion episodes was $39,231 at those with above average spending and $34,832 at those with below average spending. In comparison, spending at hospitals deemed above and below average spending for cellulitis episodes was $10,763 and $9,064, respectively, and $11,223 and $9,161 at hospitals deemed above and below average spending for kidney/UTI episodes, respectively.

Spending on specific episode components also differed by hospital group (Table). Though the magnitude of absolute spending amounts and differences varied by specific episode, hospitals with above average spending tended to spend more on SNF than did those with below average spending. For example, hospitals with above average spending for cellulitis episodes spent an average of $2,564 on SNF (24% of overall episode spending) vs $1,293 (14% of episode spending) among those with below average spending. Similarly, hospitals with above and below average spending for kidney/UTI episodes spent $4,068 (36% of episode spending) and $2,232 (24% of episode spending) on SNF, respectively (P < .001 for both episode types). Findings were qualitatively similar in sensitivity analyses (Appendix Table 2).

Among hospitals in our sample, we categorized 481 as high cost (27%), 452 as low cost (25%), and 845 as mixed cost (48%), with hospital groups distributed broadly nationwide (Appendix Figure 4). Evaluated on performance across all six episode types, hospital groups also demonstrated differences in spending by cost components (Table). In particular, spending in SNF ranged from 18.1% of overall episode spending among high-cost hospitals to 10.7% among mixed-cost hospitals and 9.2% among low-cost hospitals. Additionally, spending on hospitalization accounted for 83.3% of overall episode spending among low-cost hospitals, compared with 81.2% and 73.4% among mixed-cost and high-cost hospitals, respectively (P < .001). Comparisons were qualitatively similar in sensitivity analyses (Appendix Table 3).

Comparison of CEBP and Medicare Spending Per Beneficiary Performance

Correlation between Medicare Spending Per Beneficiary and aggregated CEBPs was 0.42 and, for individual episode types, ranged between 0.14 and 0.36 (Appendix Table 2). There was low concordance between hospital performance on CEBP and Medicare Spending Per Beneficiary. Across all eligible hospitals, only 16.3% (290/1778) had positive concordance between performance on the two measure types (ie, low cost for both), while 16.5% (293/1778) had negative concordance (ie, high cost for both). There was discordant performance in most instances (67.2%; 1195/1778), which reflecting favorable performance on one measure type but not the other.

 

DISCUSSION

To our knowledge, this study is the first to describe hospitals’ episode-specific spending performance nationwide. It demonstrated significant variation across hospitals driven by different episode components for different episode types. It also showed low correlation between individual episode spending measures and poor concordance between episode-specific and global hospital spending measures. Two practice and policy implications are noteworthy.

First, our findings corroborate and build upon evidence from bundled payment programs about the opportunity for hospitals to improve their cost efficiency. Findings from bundled payment evaluations of surgical episodes suggest that the major area for cost savings is in the reduction of institutional post-acute care use such as that of SNFs.^7-9 We demonstrated similar opportunity in a national sample of hospitals, finding that, for the three evaluated procedural CEBPs, SNF care accounted for more variation in overall episode spending than did other components. While variation may imply opportunity for greater efficiency and standardization, it is important to note that variation itself is not inherently problematic. Additional studies are needed to distinguish between warranted and unwarranted variation in procedural episodes, as well as identify strategies for reducing the latter.

Though bundled payment evaluations have predominantly emphasized procedural episodes, existing evidence suggests that participation in medical condition bundles has not been associated with cost savings or utilization changes.^7-15 Findings from our analysis of variance—that there appear to be smaller variation-reduction opportunities for condition episodes than for procedural episodes—offer insight into this issue. Existing episodes are initiated by hospitalization and extend into the postacute period, a design that may not afford substantial post-acute care savings opportunities for condition episodes. This is an important insight as policymakers consider how to best design condition-based episodes in the future (eg, whether to use non–hospital based episode triggers). Future work should evaluate whether our findings reflect inherent differences between condition and procedural episodes¹⁶ or whether interventions can still optimize SNF care for these episodes despite smaller variation.

Second, our results highlight the potential limitations of global performance measures such as Medicare Spending Per Beneficiary. As a general measure of hospital spending, Medicare Spending Per Beneficiary is based on the premise that hospitals can be categorized as high or low cost with consideration of all inpatient episodic care. However, our analyses suggest that hospitals may be high cost for certain episodes and low cost for others—a fact highlighted by the low correlation and high discordance observed between hospital CEBP and Medicare Spending Per Beneficiary performance. Because overarching measures may miss spending differen-ces related to underlying clinical scenarios, episode-specific spending measures would provide important perspective and complements to global measures for assessing hospital cost performance, particularly in an era of value-based payments. Policymakers should consider prioritizing the development and implementation of such measures.

Our study has limitations. First, it is descriptive in nature, and future work should evaluate the association between episode-­specific spending measure performance and clinical and quality outcomes. Second, we evaluated all CEBP-eligible hospitals nationwide to provide a broad view of episode-specific spending. However, future studies should assess performance among hospital subtypes, such as vertically integrated or safety-­net organizations, because they may be more or less able to perform on these spending measures. Third, though findings may not be generalizable to other clinical episodes, our results were qualitatively consistent across episode types and broadly consistent with evidence from episode-based payment models. Fourth, we analyzed cost from the perspective of utilization and did not incorporate price considerations, which may be more relevant for commercial insurers than it is for Medicare.

Nonetheless, the emergence of CEBPs reflects the ongoing shift in policymaker attention toward episode-specific spending. In particular, though further scale or use of CEBP measures has been put on hold amid other payment reform changes, their nationwide implementation in 2017 signals Medicare’s broad interest in evaluating all hospitals on episode-specific spending efficiency, in addition to other facets of spending, quality, safety, and patient experience. Importantly, such efforts complement other ongoing nationwide initiatives for emphasizing episode spending, such as use of episode-based cost measures within the Merit-Based Incentive Payment System¹⁷ to score clinicians and groups in part based on their episode-specific spending efficiency. Insight about episode spending performance could help hospitals prepare for environments with increasing focus on episode spending and as policymakers incorporate this perspective into quality and value-­based payment policies.

 

Over the last 15 years, studies have demonstrated the efficacy of small-bore chest tubes (SBCTs), or pigtail catheters (PCs, most commonly ≤14 French), in treating pneumothorax (PTX),^1-5 traumatic hemothorax (THTX), hemopneumothorax (HPTX),^6,7 parapneumonic effusions (PPEs),^8,9 pleural infections,¹⁰ and symptomatic malignant pleural effusions.¹¹ A randomized, controlled trial also showed that PC placement resulted in better pain scores, compared with large-bore chest tubes (LBCTs), for traumatic PTX.⁵ The British Thoracic Society does state that LBCTs may be needed for PTXs with very large air leaks, especially postoperatively. Further, LBCTs may be indicated if small-bore drainage fails, but otherwise they recommend PCs as first-line therapy for PTX, free flowing pleural effusions, and pleural infections.¹²

BEDSIDE PROCEDURE SERVICE DEVELOPMENT

The Medical College of Wisconsin (MCW) provides hospitalist services to Froedtert Hospital, a large, tertiary care, teaching hospital in Milwaukee, Wisconsin. A subset of hospitalists started the bedside procedure service (BPS) in 2013. The BPS initially performed procedures within the traditional scope of internal medicine–trained physicians (eg, thoracentesis, paracentesis, lumbar puncture, and arthrocentesis). Because of hospital need, the BPS began to include procedures not traditionally performed by hospitalists, including bone marrow biopsies and nontunneled central access venous catheters. With the service’s low complication rate and high volume of procedures, it was sought by cardiothoracic (CT) surgery services to assist in PC placement as an alternative to interventional radiology (IR).

BPS Pigtail Catheter Training

CT surgery initially trained the BPS director in PC placement using the Seldinger technique in 2015. The director’s training period with CT surgery included direct observation by CT surgery providers for 5 PC placements. Prior to placing PCs, the director had performed approximately 400 ultrasound-guided thoracenteses. The BPS director then independently trained the remaining BPS and has placed or supervised over half of the service’s 124 PCs. Initial credentialing for each BPS physician requires 5 PC placements and 20 thoracenteses under direct supervision of credentialed BPS members. Credentialing is maintained by BPS physicians completing 3 PCs and 15 thoracenteses per year.

Newly credentialed providers are capable of independently placing most PCs. However, the requirements for credentialing are minimal and newly credentialed physicians still encounter PC placements with challenging factors not addressed in their training, such as anterior approach, small effusions, atypical effusion location, mild to moderate coagulopathy, recent therapeutic anticoagulation, and large body habitus. To address these challenges, the BPS has instituted an “on call” system. This system is typically staffed by the BPS director or associate director, already attending on a separate medical service. When needed, the “on call” physician will supervise the newer BPS members to ensure safety while the less experienced physician places the PC. Although rare, if an “on call” member is not available, then it is the practice of the BPS to recommend IR for PC placement.

 

BPS Operation

Daily BPS operation consists of one attending hospitalist, two internal medicine residents, and a third-year medical student. PCs are placed primarily (95%) by the attending on service under ultrasound guidance using the Seldinger technique with lidocaine for anesthetic. For all PC consults, the attending BPS physician reviews the indication prior to placement. If not a direct consult from surgical services, most PC consults are appropriate referrals to the service after the primary medicine service has consulted CT-surgery or p ulmonary consult teams. After review, the primary role of the BPS is assessing safety of PC placement, including whether the patient can tolerate PC placement without procedural sedation. The BPS’s additional standards for safe PC placement are listed in Table 1.

Additionally, it is not routine practice of the BPS to recommend PC placement when consulted for a thoracentesis. The exception to this rule is patients whose PPE sonographic imaging demonstrates loculation or septations. This is consistent with the latest review on pleural disease.¹³ In addition, the institution’s CT surgery services prefer to initially treat septated PPEs with PCs and fibrinolytic therapy rather than immediate video-assisted thoracoscopic surgery (VATS).

The BPS operates a partnership with CT surgery in which, after successful PC placement, CT surgery manages the PC immediately and until removal including the negative pressure applied and need for fibrinolytic therapy. CT surgery also determines if secondary therapy, commonly second PC or VATS, is required. After PC placement, a portable chest x-ray (CXR) is taken and then BPS follows the patient in person the following day to note any insertion-related complications (IRCs).

In this paper, data on the consults to the BPS for PC placement over a 2-year period are presented. Primary outcomes included numbers of and indications for PCs consulted—attempted or not attempted—consulting services, IRCs, unsuccessful attempts (UAs), and adverse outcomes (AOs). PC duration, fluid drainage, need for fibrinolytic therapy, or need for secondary therapy were not measured because these decisions were managed by the CT surgery service.

PATIENTS AND METHODS

Institutional review board approval of this retrospective study was granted by MCW/Froedtert Hospital Institutional Review Board #5 on January 14, 2019 (MCW IRB #PRO00033496). Adult patients hospitalized at Froedtert Hospital whose primary team determined they would clinically benefit from a PC and consulted the BPS service for placement were included. There were no exclusion criteria.

The authors conducted a retrospective review of two secure BPS databases. The first database is a record of all procedure consults, while the second database contains information about all attempted PCs. Initial review of the BPS’s consult database found 142 PC consults. Consults were classified as “declined” or “attempted.” In addition to the database comparison, the authors performed a manual chart review on patients with documented complications (n = 6) to clarify sequela, those with unclear PC indication (n = 2), and to resolve the discrepancies between our two databases (n = 3). Finally, a brief chart review was performed to review procedures in the subsequent 48 hours after a declined PC consult (n = 18).

Complications fell into two categories, IRCs and UAs. IRCs were defined as unintentional PC placement into a location other than the pleural space or PC placement that resulted in an AO according to the judgement of the attending BPS physician. A UA was defined as an unsuccessfully attempted PC placement, with the BPS unable to pass a PC in the pleural space for any reason. An AO was defined as any escalation of care that could be related to the procedure within 24 hours of attempt/placement found in our databases and/or manual chart review (eg, emergent intubation, surgery, death).

 

RESULTS

Over a 2-year period, the BPS was consulted to place 142 PCs. After resolution of the 3 discrepancies, total consults remained 142, PC attempts totaled 124 (87.3%), and declined consults totaled 18 (12.7%).

The 18 declined consults were not performed for reasons relating to procedural safety. These included 15 (83.3%) for insufficient fluid depth, 1 (5.6%) poor window for PTX, and 1 (5.6%) patient unstable per BPS attending judgement. One (5.6%) final consult had a previous drain in same hemithorax that resumed functioning.

The manual chart review of procedures performed 48 hours after declined PC consults found only 3 of 17 (17.6%) patients received a PC within the subsequent 48 hours. The 18th patient was unable to be followed in our electronic medical record because his medical record number was recorded incorrectly.

The remaining 124 consults were deemed safe for PC placement. Indications for PC placement varied; the most common indications were complicated effusion (36.3%), large or recurrent effusions (21.8%), PTX (17%), and hemothorax (HTX; 17%). The most common teams who consulted the BPS for PC were medicine/hospitalists (42.7%) and CT surgery (40.3%).

There were 3 IRCs (Table 2) out of 124 attempted consults (2.4%). Of these cases, 2 patients had AOs. IRC patient No. 1 required a PC for PTX and developed a hemothorax from a right-sided mammary artery laceration. Emergent operative measures were taken, but unfortunately the patient died. IRC patient No. 2 was septic from pneumonia when a PC was placed for a complicated PPE. Unfortunately, the patient went into respiratory failure and required intubation. The postintubation computed tomography scan did note that the PC placed by the BPS likely terminated in the lower lobe of the right lung but without PTX. After a new PC was placed by IR, the patient received antibiotics, 3 days of ventilator support, and was discharged home. The authors believe that sepsis from pneumonia was the more probable cause of the respiratory failure in IRC patient No. 2 instead of the PC placement.

Three UAs were charted in the database, but on review it was determined that only 2 (1.6%) qualified as UAs (Table 3). A PC was attempted with the UA patient No. 3 for a loculated apical PTX. It is clear in the procedure note that the pleural space was accessed, air was appropriately drained, and a PC was advanced safely into the pleural space; however, the PC then stopped draining air. CXR interpretation also noted “pneumothorax described on prior exam is less evident.” Because the pleural space was accessed safely and had a partially therapeutic response, we do not count this PC placement as a UA. The PC may count as “failed,” but determination of a “failure rate” is not the intent of this paper. This point is further discussed in the Discussion section.

In addition, chart review demonstrated that UA patient No. 3 required intubation within the 24-hour period after our PC attempt, which is an AO. Approximately 10 hours after our PC was placed and removed, CT surgery placed a second PC, and 3 hours after their PC placement, the patient was intubated with subsequent bronchoscopy. The patient was extubated after only 17 hours. This sequence of events suggests mucus plugging as a more likely cause for respiratory failure than our PC attempt, but we have included it as an AO given the time frame.

Overall, the AO rate was low. Out of 124 attempted PC placements only 3 (2.4%) had an AO, and as noted above, it is believed that 2 of these patients had an AO caused by other medical problems rather than by PC placement.

 

DISCUSSION

To our knowledge, this is the first report of the experience of procedure-focused hospitalists with PC placement in a partnership with CT surgery. We believe that, at high volume, tertiary care centers similar to Froedtert Hospital, internal medicine–trained, procedure-focused hospitalists can serve as adjuncts to surgery, pulmonary, and IR services in the placement of PCs in hospitalized patients that do not require procedural sedation.

Given the development of this service and the nature of its shared operations with CT surgery, we do not believe that the BPS has an appropriate comparison in the literature; however, the IRCs are similar to previous papers describing PC placement.^5-7,14 Notably, the IRC and AO rates were low, both 2.4%, which indicates safe placement of PCs. Kulvatunyou et al and Bauman et al reported on PC placement from a surgical perspective and reported IRC rates of 4%-10%.^5-7,14 These higher IRC rates likely have a few reasons. First, Kulvatunyou et al and Bauman et al did not use ultrasound guidance. Use of ultrasound guidance may have significantly lowered their IRC rate. Second, the definition of IRC used by Kulvatunyou et al and Bauman et al included dislodgements, but we do not believe this to be an IRC. Dislodgements can happen for several reasons, frequently a result of patient movement or forgetfulness, not because of improper placement. Third, the PCs with this BPS are placed primarily by attending physicians. Resident roles on our BPS in PC placement are primarily as assistants, whereas Kulvatunyou et al and Bauman et al note that both attendings and residents, under attending supervision, placed PCs; however, it is not clear what percentage of PCs were placed by attendings or residents in their studies. Finally, this BPS’s IRCs are self-reported, so they could be perceived as falsely low, but given the small number of physicians involved in the group and its standardized follow-up, we do not suspect this is truly contributing to the low rates.

Other complication rates regarding the use of wire-guided SBCTs and PCs range from 0% to 42%^15-20; however, several differences including tube size, physician training, and PC indication make these studies imperfect comparisons. The most notable difference in our opinion is the variable definition, or lack of definition, of a complication. One study did not define their complications,¹⁹ while other studies list subjective measures like pain,^16,20 cough,¹⁶ bleeding, ^16,20 and hematomas^4,15 as complications. We believe that the lack of consensus definition for PC complication or IRC contributes to the large range of complication rates in the literature. This problem is likely not unique to PC placement, but is instead true across all bedside procedures. In a shared-practice model between hospitalists and CT surgeons, we believe the definition of IRC in this paper is adequate in capturing most complications. The only complication we are currently unable to track well is infection. We consider other items discussed previously, such as pain, cough (often from lung re-expansion), minor bleeding, and even small hematomas, to be a part of the procedure and not a complication.

Finally, regarding the IRCs and associated death, this was a tragic event. Complications for all of the BPS’s procedures are infrequent (0.35% over the same time period) and reviewed between the BPS director and the attending who performed the procedure; in addition, given this mortality, the case was reviewed immediately in detail with our CT surgery colleagues. On review, it was easy to determine that the operator had found a clear lung tip and sonographic signs of PTX; however, CXR review did demonstrate a medial placement of the PC. This was judged to be a poor placement location (even with imaging demonstrating PTX in that area) given the well-known “triangle of safety” defined by the British Thoracic Society.¹²

After review, the primary emphasis for PC placement was safe location. The BPS now strives to place PCs for PTX only in the “triangle of safety.” The BPS believe that most PTXs can be addressed with this placement. In the rare case of a PTX requiring an anterior approach, only the BPS director currently places apical PCs for PTX while on service or “on call.” He discusses the placement with pulmonary and CT surgery directly to determine that the PC is of absolute necessity.

Given the focus on appropriate location, no formal changes were made to the procedural imaging practice described in Table 1. We realize that vascular imaging would seem necessary after this patient’s mammary artery laceration; however, safe location, in addition to the BPS’s current image requirements, is believed to minimize this risk. We feel the imaging criteria align with recommendation No. 5 of the Society of Hospital Medicine’s Position Statement for Ultrasound Guidance for Adult Thoracentesis.²¹ Some BPS members use vascular ultrasound imaging to confirm absence of vascularity, but it is not required and occasionally not possible, such as in the occasional case of PTX with subcutaneous emphysema.

The UA rate is low without a natural comparator in the literature. It is important to clarify the difference between the UAs and the frequently mentioned “failure rate” (FR) in Kulvatunyou et al and Bauman et al^6,7,14 We classify UAs as the inability, for any reason, to access the pleural space and insert a PC. At this stage, these UAs appear to reflect the service’s new experience with PC placement and inability to provide procedural sedation. Kulvatunyou et al and Bauman et al’s FR is defined as an initial PC successfully placed into the pleural space that then required a second PC or intervention (frequently VATS) to resolve the PTX or retained HTX.

We believe calculating the failure rate will be helpful in demonstrating the value of our BPS and our shared-practice model. We look forward to publishing this and other future research, including determination of the cost and time saved by the BPS for PCs and other procedures.

Limitations of this study include its retrospective nature, results from a single center’s experience, and lack of a comparison group.

Our institution feels that there is great benefit in having a BPS operated by procedure-focused hospitalists. It would also be important to determine if our model can be replicated by another institution.

 

Acknowledgments

The authors thank CT surgery for helping to develop this shared-practice model and to both CT surgery and IR physicians here at the Medical College of Wisconsin and Froedtert Hospital who assist us with both IRCs and UAs of pigtail catheters.

The authors also thank Dr. Ricardo Franco-Sadud for his oversight and thoughtful improvements to the paper.

Over the last 15 years, studies have demonstrated the efficacy of small-bore chest tubes (SBCTs), or pigtail catheters (PCs, most commonly ≤14 French), in treating pneumothorax (PTX),^1-5 traumatic hemothorax (THTX), hemopneumothorax (HPTX),^6,7 parapneumonic effusions (PPEs),^8,9 pleural infections,¹⁰ and symptomatic malignant pleural effusions.¹¹ A randomized, controlled trial also showed that PC placement resulted in better pain scores, compared with large-bore chest tubes (LBCTs), for traumatic PTX.⁵ The British Thoracic Society does state that LBCTs may be needed for PTXs with very large air leaks, especially postoperatively. Further, LBCTs may be indicated if small-bore drainage fails, but otherwise they recommend PCs as first-line therapy for PTX, free flowing pleural effusions, and pleural infections.¹²

BEDSIDE PROCEDURE SERVICE DEVELOPMENT

The Medical College of Wisconsin (MCW) provides hospitalist services to Froedtert Hospital, a large, tertiary care, teaching hospital in Milwaukee, Wisconsin. A subset of hospitalists started the bedside procedure service (BPS) in 2013. The BPS initially performed procedures within the traditional scope of internal medicine–trained physicians (eg, thoracentesis, paracentesis, lumbar puncture, and arthrocentesis). Because of hospital need, the BPS began to include procedures not traditionally performed by hospitalists, including bone marrow biopsies and nontunneled central access venous catheters. With the service’s low complication rate and high volume of procedures, it was sought by cardiothoracic (CT) surgery services to assist in PC placement as an alternative to interventional radiology (IR).

BPS Pigtail Catheter Training

CT surgery initially trained the BPS director in PC placement using the Seldinger technique in 2015. The director’s training period with CT surgery included direct observation by CT surgery providers for 5 PC placements. Prior to placing PCs, the director had performed approximately 400 ultrasound-guided thoracenteses. The BPS director then independently trained the remaining BPS and has placed or supervised over half of the service’s 124 PCs. Initial credentialing for each BPS physician requires 5 PC placements and 20 thoracenteses under direct supervision of credentialed BPS members. Credentialing is maintained by BPS physicians completing 3 PCs and 15 thoracenteses per year.

Newly credentialed providers are capable of independently placing most PCs. However, the requirements for credentialing are minimal and newly credentialed physicians still encounter PC placements with challenging factors not addressed in their training, such as anterior approach, small effusions, atypical effusion location, mild to moderate coagulopathy, recent therapeutic anticoagulation, and large body habitus. To address these challenges, the BPS has instituted an “on call” system. This system is typically staffed by the BPS director or associate director, already attending on a separate medical service. When needed, the “on call” physician will supervise the newer BPS members to ensure safety while the less experienced physician places the PC. Although rare, if an “on call” member is not available, then it is the practice of the BPS to recommend IR for PC placement.

 

BPS Operation

Daily BPS operation consists of one attending hospitalist, two internal medicine residents, and a third-year medical student. PCs are placed primarily (95%) by the attending on service under ultrasound guidance using the Seldinger technique with lidocaine for anesthetic. For all PC consults, the attending BPS physician reviews the indication prior to placement. If not a direct consult from surgical services, most PC consults are appropriate referrals to the service after the primary medicine service has consulted CT-surgery or p ulmonary consult teams. After review, the primary role of the BPS is assessing safety of PC placement, including whether the patient can tolerate PC placement without procedural sedation. The BPS’s additional standards for safe PC placement are listed in Table 1.

Additionally, it is not routine practice of the BPS to recommend PC placement when consulted for a thoracentesis. The exception to this rule is patients whose PPE sonographic imaging demonstrates loculation or septations. This is consistent with the latest review on pleural disease.¹³ In addition, the institution’s CT surgery services prefer to initially treat septated PPEs with PCs and fibrinolytic therapy rather than immediate video-assisted thoracoscopic surgery (VATS).

The BPS operates a partnership with CT surgery in which, after successful PC placement, CT surgery manages the PC immediately and until removal including the negative pressure applied and need for fibrinolytic therapy. CT surgery also determines if secondary therapy, commonly second PC or VATS, is required. After PC placement, a portable chest x-ray (CXR) is taken and then BPS follows the patient in person the following day to note any insertion-related complications (IRCs).

In this paper, data on the consults to the BPS for PC placement over a 2-year period are presented. Primary outcomes included numbers of and indications for PCs consulted—attempted or not attempted—consulting services, IRCs, unsuccessful attempts (UAs), and adverse outcomes (AOs). PC duration, fluid drainage, need for fibrinolytic therapy, or need for secondary therapy were not measured because these decisions were managed by the CT surgery service.

PATIENTS AND METHODS

Institutional review board approval of this retrospective study was granted by MCW/Froedtert Hospital Institutional Review Board #5 on January 14, 2019 (MCW IRB #PRO00033496). Adult patients hospitalized at Froedtert Hospital whose primary team determined they would clinically benefit from a PC and consulted the BPS service for placement were included. There were no exclusion criteria.

The authors conducted a retrospective review of two secure BPS databases. The first database is a record of all procedure consults, while the second database contains information about all attempted PCs. Initial review of the BPS’s consult database found 142 PC consults. Consults were classified as “declined” or “attempted.” In addition to the database comparison, the authors performed a manual chart review on patients with documented complications (n = 6) to clarify sequela, those with unclear PC indication (n = 2), and to resolve the discrepancies between our two databases (n = 3). Finally, a brief chart review was performed to review procedures in the subsequent 48 hours after a declined PC consult (n = 18).

Complications fell into two categories, IRCs and UAs. IRCs were defined as unintentional PC placement into a location other than the pleural space or PC placement that resulted in an AO according to the judgement of the attending BPS physician. A UA was defined as an unsuccessfully attempted PC placement, with the BPS unable to pass a PC in the pleural space for any reason. An AO was defined as any escalation of care that could be related to the procedure within 24 hours of attempt/placement found in our databases and/or manual chart review (eg, emergent intubation, surgery, death).

 

RESULTS

Over a 2-year period, the BPS was consulted to place 142 PCs. After resolution of the 3 discrepancies, total consults remained 142, PC attempts totaled 124 (87.3%), and declined consults totaled 18 (12.7%).

The 18 declined consults were not performed for reasons relating to procedural safety. These included 15 (83.3%) for insufficient fluid depth, 1 (5.6%) poor window for PTX, and 1 (5.6%) patient unstable per BPS attending judgement. One (5.6%) final consult had a previous drain in same hemithorax that resumed functioning.

The manual chart review of procedures performed 48 hours after declined PC consults found only 3 of 17 (17.6%) patients received a PC within the subsequent 48 hours. The 18th patient was unable to be followed in our electronic medical record because his medical record number was recorded incorrectly.

The remaining 124 consults were deemed safe for PC placement. Indications for PC placement varied; the most common indications were complicated effusion (36.3%), large or recurrent effusions (21.8%), PTX (17%), and hemothorax (HTX; 17%). The most common teams who consulted the BPS for PC were medicine/hospitalists (42.7%) and CT surgery (40.3%).

There were 3 IRCs (Table 2) out of 124 attempted consults (2.4%). Of these cases, 2 patients had AOs. IRC patient No. 1 required a PC for PTX and developed a hemothorax from a right-sided mammary artery laceration. Emergent operative measures were taken, but unfortunately the patient died. IRC patient No. 2 was septic from pneumonia when a PC was placed for a complicated PPE. Unfortunately, the patient went into respiratory failure and required intubation. The postintubation computed tomography scan did note that the PC placed by the BPS likely terminated in the lower lobe of the right lung but without PTX. After a new PC was placed by IR, the patient received antibiotics, 3 days of ventilator support, and was discharged home. The authors believe that sepsis from pneumonia was the more probable cause of the respiratory failure in IRC patient No. 2 instead of the PC placement.

Three UAs were charted in the database, but on review it was determined that only 2 (1.6%) qualified as UAs (Table 3). A PC was attempted with the UA patient No. 3 for a loculated apical PTX. It is clear in the procedure note that the pleural space was accessed, air was appropriately drained, and a PC was advanced safely into the pleural space; however, the PC then stopped draining air. CXR interpretation also noted “pneumothorax described on prior exam is less evident.” Because the pleural space was accessed safely and had a partially therapeutic response, we do not count this PC placement as a UA. The PC may count as “failed,” but determination of a “failure rate” is not the intent of this paper. This point is further discussed in the Discussion section.

In addition, chart review demonstrated that UA patient No. 3 required intubation within the 24-hour period after our PC attempt, which is an AO. Approximately 10 hours after our PC was placed and removed, CT surgery placed a second PC, and 3 hours after their PC placement, the patient was intubated with subsequent bronchoscopy. The patient was extubated after only 17 hours. This sequence of events suggests mucus plugging as a more likely cause for respiratory failure than our PC attempt, but we have included it as an AO given the time frame.

Overall, the AO rate was low. Out of 124 attempted PC placements only 3 (2.4%) had an AO, and as noted above, it is believed that 2 of these patients had an AO caused by other medical problems rather than by PC placement.

 

DISCUSSION

To our knowledge, this is the first report of the experience of procedure-focused hospitalists with PC placement in a partnership with CT surgery. We believe that, at high volume, tertiary care centers similar to Froedtert Hospital, internal medicine–trained, procedure-focused hospitalists can serve as adjuncts to surgery, pulmonary, and IR services in the placement of PCs in hospitalized patients that do not require procedural sedation.

Given the development of this service and the nature of its shared operations with CT surgery, we do not believe that the BPS has an appropriate comparison in the literature; however, the IRCs are similar to previous papers describing PC placement.^5-7,14 Notably, the IRC and AO rates were low, both 2.4%, which indicates safe placement of PCs. Kulvatunyou et al and Bauman et al reported on PC placement from a surgical perspective and reported IRC rates of 4%-10%.^5-7,14 These higher IRC rates likely have a few reasons. First, Kulvatunyou et al and Bauman et al did not use ultrasound guidance. Use of ultrasound guidance may have significantly lowered their IRC rate. Second, the definition of IRC used by Kulvatunyou et al and Bauman et al included dislodgements, but we do not believe this to be an IRC. Dislodgements can happen for several reasons, frequently a result of patient movement or forgetfulness, not because of improper placement. Third, the PCs with this BPS are placed primarily by attending physicians. Resident roles on our BPS in PC placement are primarily as assistants, whereas Kulvatunyou et al and Bauman et al note that both attendings and residents, under attending supervision, placed PCs; however, it is not clear what percentage of PCs were placed by attendings or residents in their studies. Finally, this BPS’s IRCs are self-reported, so they could be perceived as falsely low, but given the small number of physicians involved in the group and its standardized follow-up, we do not suspect this is truly contributing to the low rates.

Other complication rates regarding the use of wire-guided SBCTs and PCs range from 0% to 42%^15-20; however, several differences including tube size, physician training, and PC indication make these studies imperfect comparisons. The most notable difference in our opinion is the variable definition, or lack of definition, of a complication. One study did not define their complications,¹⁹ while other studies list subjective measures like pain,^16,20 cough,¹⁶ bleeding, ^16,20 and hematomas^4,15 as complications. We believe that the lack of consensus definition for PC complication or IRC contributes to the large range of complication rates in the literature. This problem is likely not unique to PC placement, but is instead true across all bedside procedures. In a shared-practice model between hospitalists and CT surgeons, we believe the definition of IRC in this paper is adequate in capturing most complications. The only complication we are currently unable to track well is infection. We consider other items discussed previously, such as pain, cough (often from lung re-expansion), minor bleeding, and even small hematomas, to be a part of the procedure and not a complication.

Finally, regarding the IRCs and associated death, this was a tragic event. Complications for all of the BPS’s procedures are infrequent (0.35% over the same time period) and reviewed between the BPS director and the attending who performed the procedure; in addition, given this mortality, the case was reviewed immediately in detail with our CT surgery colleagues. On review, it was easy to determine that the operator had found a clear lung tip and sonographic signs of PTX; however, CXR review did demonstrate a medial placement of the PC. This was judged to be a poor placement location (even with imaging demonstrating PTX in that area) given the well-known “triangle of safety” defined by the British Thoracic Society.¹²

After review, the primary emphasis for PC placement was safe location. The BPS now strives to place PCs for PTX only in the “triangle of safety.” The BPS believe that most PTXs can be addressed with this placement. In the rare case of a PTX requiring an anterior approach, only the BPS director currently places apical PCs for PTX while on service or “on call.” He discusses the placement with pulmonary and CT surgery directly to determine that the PC is of absolute necessity.

Given the focus on appropriate location, no formal changes were made to the procedural imaging practice described in Table 1. We realize that vascular imaging would seem necessary after this patient’s mammary artery laceration; however, safe location, in addition to the BPS’s current image requirements, is believed to minimize this risk. We feel the imaging criteria align with recommendation No. 5 of the Society of Hospital Medicine’s Position Statement for Ultrasound Guidance for Adult Thoracentesis.²¹ Some BPS members use vascular ultrasound imaging to confirm absence of vascularity, but it is not required and occasionally not possible, such as in the occasional case of PTX with subcutaneous emphysema.

The UA rate is low without a natural comparator in the literature. It is important to clarify the difference between the UAs and the frequently mentioned “failure rate” (FR) in Kulvatunyou et al and Bauman et al^6,7,14 We classify UAs as the inability, for any reason, to access the pleural space and insert a PC. At this stage, these UAs appear to reflect the service’s new experience with PC placement and inability to provide procedural sedation. Kulvatunyou et al and Bauman et al’s FR is defined as an initial PC successfully placed into the pleural space that then required a second PC or intervention (frequently VATS) to resolve the PTX or retained HTX.

We believe calculating the failure rate will be helpful in demonstrating the value of our BPS and our shared-practice model. We look forward to publishing this and other future research, including determination of the cost and time saved by the BPS for PCs and other procedures.

Limitations of this study include its retrospective nature, results from a single center’s experience, and lack of a comparison group.

Our institution feels that there is great benefit in having a BPS operated by procedure-focused hospitalists. It would also be important to determine if our model can be replicated by another institution.

 

Acknowledgments

The authors thank CT surgery for helping to develop this shared-practice model and to both CT surgery and IR physicians here at the Medical College of Wisconsin and Froedtert Hospital who assist us with both IRCs and UAs of pigtail catheters.

The authors also thank Dr. Ricardo Franco-Sadud for his oversight and thoughtful improvements to the paper.

Over the last 15 years, studies have demonstrated the efficacy of small-bore chest tubes (SBCTs), or pigtail catheters (PCs, most commonly ≤14 French), in treating pneumothorax (PTX),^1-5 traumatic hemothorax (THTX), hemopneumothorax (HPTX),^6,7 parapneumonic effusions (PPEs),^8,9 pleural infections,¹⁰ and symptomatic malignant pleural effusions.¹¹ A randomized, controlled trial also showed that PC placement resulted in better pain scores, compared with large-bore chest tubes (LBCTs), for traumatic PTX.⁵ The British Thoracic Society does state that LBCTs may be needed for PTXs with very large air leaks, especially postoperatively. Further, LBCTs may be indicated if small-bore drainage fails, but otherwise they recommend PCs as first-line therapy for PTX, free flowing pleural effusions, and pleural infections.¹²

BEDSIDE PROCEDURE SERVICE DEVELOPMENT

The Medical College of Wisconsin (MCW) provides hospitalist services to Froedtert Hospital, a large, tertiary care, teaching hospital in Milwaukee, Wisconsin. A subset of hospitalists started the bedside procedure service (BPS) in 2013. The BPS initially performed procedures within the traditional scope of internal medicine–trained physicians (eg, thoracentesis, paracentesis, lumbar puncture, and arthrocentesis). Because of hospital need, the BPS began to include procedures not traditionally performed by hospitalists, including bone marrow biopsies and nontunneled central access venous catheters. With the service’s low complication rate and high volume of procedures, it was sought by cardiothoracic (CT) surgery services to assist in PC placement as an alternative to interventional radiology (IR).

BPS Pigtail Catheter Training

CT surgery initially trained the BPS director in PC placement using the Seldinger technique in 2015. The director’s training period with CT surgery included direct observation by CT surgery providers for 5 PC placements. Prior to placing PCs, the director had performed approximately 400 ultrasound-guided thoracenteses. The BPS director then independently trained the remaining BPS and has placed or supervised over half of the service’s 124 PCs. Initial credentialing for each BPS physician requires 5 PC placements and 20 thoracenteses under direct supervision of credentialed BPS members. Credentialing is maintained by BPS physicians completing 3 PCs and 15 thoracenteses per year.

Newly credentialed providers are capable of independently placing most PCs. However, the requirements for credentialing are minimal and newly credentialed physicians still encounter PC placements with challenging factors not addressed in their training, such as anterior approach, small effusions, atypical effusion location, mild to moderate coagulopathy, recent therapeutic anticoagulation, and large body habitus. To address these challenges, the BPS has instituted an “on call” system. This system is typically staffed by the BPS director or associate director, already attending on a separate medical service. When needed, the “on call” physician will supervise the newer BPS members to ensure safety while the less experienced physician places the PC. Although rare, if an “on call” member is not available, then it is the practice of the BPS to recommend IR for PC placement.

 

BPS Operation

Daily BPS operation consists of one attending hospitalist, two internal medicine residents, and a third-year medical student. PCs are placed primarily (95%) by the attending on service under ultrasound guidance using the Seldinger technique with lidocaine for anesthetic. For all PC consults, the attending BPS physician reviews the indication prior to placement. If not a direct consult from surgical services, most PC consults are appropriate referrals to the service after the primary medicine service has consulted CT-surgery or p ulmonary consult teams. After review, the primary role of the BPS is assessing safety of PC placement, including whether the patient can tolerate PC placement without procedural sedation. The BPS’s additional standards for safe PC placement are listed in Table 1.

Additionally, it is not routine practice of the BPS to recommend PC placement when consulted for a thoracentesis. The exception to this rule is patients whose PPE sonographic imaging demonstrates loculation or septations. This is consistent with the latest review on pleural disease.¹³ In addition, the institution’s CT surgery services prefer to initially treat septated PPEs with PCs and fibrinolytic therapy rather than immediate video-assisted thoracoscopic surgery (VATS).

The BPS operates a partnership with CT surgery in which, after successful PC placement, CT surgery manages the PC immediately and until removal including the negative pressure applied and need for fibrinolytic therapy. CT surgery also determines if secondary therapy, commonly second PC or VATS, is required. After PC placement, a portable chest x-ray (CXR) is taken and then BPS follows the patient in person the following day to note any insertion-related complications (IRCs).

In this paper, data on the consults to the BPS for PC placement over a 2-year period are presented. Primary outcomes included numbers of and indications for PCs consulted—attempted or not attempted—consulting services, IRCs, unsuccessful attempts (UAs), and adverse outcomes (AOs). PC duration, fluid drainage, need for fibrinolytic therapy, or need for secondary therapy were not measured because these decisions were managed by the CT surgery service.

PATIENTS AND METHODS

Institutional review board approval of this retrospective study was granted by MCW/Froedtert Hospital Institutional Review Board #5 on January 14, 2019 (MCW IRB #PRO00033496). Adult patients hospitalized at Froedtert Hospital whose primary team determined they would clinically benefit from a PC and consulted the BPS service for placement were included. There were no exclusion criteria.

The authors conducted a retrospective review of two secure BPS databases. The first database is a record of all procedure consults, while the second database contains information about all attempted PCs. Initial review of the BPS’s consult database found 142 PC consults. Consults were classified as “declined” or “attempted.” In addition to the database comparison, the authors performed a manual chart review on patients with documented complications (n = 6) to clarify sequela, those with unclear PC indication (n = 2), and to resolve the discrepancies between our two databases (n = 3). Finally, a brief chart review was performed to review procedures in the subsequent 48 hours after a declined PC consult (n = 18).

Complications fell into two categories, IRCs and UAs. IRCs were defined as unintentional PC placement into a location other than the pleural space or PC placement that resulted in an AO according to the judgement of the attending BPS physician. A UA was defined as an unsuccessfully attempted PC placement, with the BPS unable to pass a PC in the pleural space for any reason. An AO was defined as any escalation of care that could be related to the procedure within 24 hours of attempt/placement found in our databases and/or manual chart review (eg, emergent intubation, surgery, death).

 

RESULTS

Over a 2-year period, the BPS was consulted to place 142 PCs. After resolution of the 3 discrepancies, total consults remained 142, PC attempts totaled 124 (87.3%), and declined consults totaled 18 (12.7%).

The 18 declined consults were not performed for reasons relating to procedural safety. These included 15 (83.3%) for insufficient fluid depth, 1 (5.6%) poor window for PTX, and 1 (5.6%) patient unstable per BPS attending judgement. One (5.6%) final consult had a previous drain in same hemithorax that resumed functioning.

The manual chart review of procedures performed 48 hours after declined PC consults found only 3 of 17 (17.6%) patients received a PC within the subsequent 48 hours. The 18th patient was unable to be followed in our electronic medical record because his medical record number was recorded incorrectly.

The remaining 124 consults were deemed safe for PC placement. Indications for PC placement varied; the most common indications were complicated effusion (36.3%), large or recurrent effusions (21.8%), PTX (17%), and hemothorax (HTX; 17%). The most common teams who consulted the BPS for PC were medicine/hospitalists (42.7%) and CT surgery (40.3%).

There were 3 IRCs (Table 2) out of 124 attempted consults (2.4%). Of these cases, 2 patients had AOs. IRC patient No. 1 required a PC for PTX and developed a hemothorax from a right-sided mammary artery laceration. Emergent operative measures were taken, but unfortunately the patient died. IRC patient No. 2 was septic from pneumonia when a PC was placed for a complicated PPE. Unfortunately, the patient went into respiratory failure and required intubation. The postintubation computed tomography scan did note that the PC placed by the BPS likely terminated in the lower lobe of the right lung but without PTX. After a new PC was placed by IR, the patient received antibiotics, 3 days of ventilator support, and was discharged home. The authors believe that sepsis from pneumonia was the more probable cause of the respiratory failure in IRC patient No. 2 instead of the PC placement.

Three UAs were charted in the database, but on review it was determined that only 2 (1.6%) qualified as UAs (Table 3). A PC was attempted with the UA patient No. 3 for a loculated apical PTX. It is clear in the procedure note that the pleural space was accessed, air was appropriately drained, and a PC was advanced safely into the pleural space; however, the PC then stopped draining air. CXR interpretation also noted “pneumothorax described on prior exam is less evident.” Because the pleural space was accessed safely and had a partially therapeutic response, we do not count this PC placement as a UA. The PC may count as “failed,” but determination of a “failure rate” is not the intent of this paper. This point is further discussed in the Discussion section.

In addition, chart review demonstrated that UA patient No. 3 required intubation within the 24-hour period after our PC attempt, which is an AO. Approximately 10 hours after our PC was placed and removed, CT surgery placed a second PC, and 3 hours after their PC placement, the patient was intubated with subsequent bronchoscopy. The patient was extubated after only 17 hours. This sequence of events suggests mucus plugging as a more likely cause for respiratory failure than our PC attempt, but we have included it as an AO given the time frame.

Overall, the AO rate was low. Out of 124 attempted PC placements only 3 (2.4%) had an AO, and as noted above, it is believed that 2 of these patients had an AO caused by other medical problems rather than by PC placement.

 

DISCUSSION

To our knowledge, this is the first report of the experience of procedure-focused hospitalists with PC placement in a partnership with CT surgery. We believe that, at high volume, tertiary care centers similar to Froedtert Hospital, internal medicine–trained, procedure-focused hospitalists can serve as adjuncts to surgery, pulmonary, and IR services in the placement of PCs in hospitalized patients that do not require procedural sedation.

Given the development of this service and the nature of its shared operations with CT surgery, we do not believe that the BPS has an appropriate comparison in the literature; however, the IRCs are similar to previous papers describing PC placement.^5-7,14 Notably, the IRC and AO rates were low, both 2.4%, which indicates safe placement of PCs. Kulvatunyou et al and Bauman et al reported on PC placement from a surgical perspective and reported IRC rates of 4%-10%.^5-7,14 These higher IRC rates likely have a few reasons. First, Kulvatunyou et al and Bauman et al did not use ultrasound guidance. Use of ultrasound guidance may have significantly lowered their IRC rate. Second, the definition of IRC used by Kulvatunyou et al and Bauman et al included dislodgements, but we do not believe this to be an IRC. Dislodgements can happen for several reasons, frequently a result of patient movement or forgetfulness, not because of improper placement. Third, the PCs with this BPS are placed primarily by attending physicians. Resident roles on our BPS in PC placement are primarily as assistants, whereas Kulvatunyou et al and Bauman et al note that both attendings and residents, under attending supervision, placed PCs; however, it is not clear what percentage of PCs were placed by attendings or residents in their studies. Finally, this BPS’s IRCs are self-reported, so they could be perceived as falsely low, but given the small number of physicians involved in the group and its standardized follow-up, we do not suspect this is truly contributing to the low rates.

Other complication rates regarding the use of wire-guided SBCTs and PCs range from 0% to 42%^15-20; however, several differences including tube size, physician training, and PC indication make these studies imperfect comparisons. The most notable difference in our opinion is the variable definition, or lack of definition, of a complication. One study did not define their complications,¹⁹ while other studies list subjective measures like pain,^16,20 cough,¹⁶ bleeding, ^16,20 and hematomas^4,15 as complications. We believe that the lack of consensus definition for PC complication or IRC contributes to the large range of complication rates in the literature. This problem is likely not unique to PC placement, but is instead true across all bedside procedures. In a shared-practice model between hospitalists and CT surgeons, we believe the definition of IRC in this paper is adequate in capturing most complications. The only complication we are currently unable to track well is infection. We consider other items discussed previously, such as pain, cough (often from lung re-expansion), minor bleeding, and even small hematomas, to be a part of the procedure and not a complication.

Finally, regarding the IRCs and associated death, this was a tragic event. Complications for all of the BPS’s procedures are infrequent (0.35% over the same time period) and reviewed between the BPS director and the attending who performed the procedure; in addition, given this mortality, the case was reviewed immediately in detail with our CT surgery colleagues. On review, it was easy to determine that the operator had found a clear lung tip and sonographic signs of PTX; however, CXR review did demonstrate a medial placement of the PC. This was judged to be a poor placement location (even with imaging demonstrating PTX in that area) given the well-known “triangle of safety” defined by the British Thoracic Society.¹²

After review, the primary emphasis for PC placement was safe location. The BPS now strives to place PCs for PTX only in the “triangle of safety.” The BPS believe that most PTXs can be addressed with this placement. In the rare case of a PTX requiring an anterior approach, only the BPS director currently places apical PCs for PTX while on service or “on call.” He discusses the placement with pulmonary and CT surgery directly to determine that the PC is of absolute necessity.

Given the focus on appropriate location, no formal changes were made to the procedural imaging practice described in Table 1. We realize that vascular imaging would seem necessary after this patient’s mammary artery laceration; however, safe location, in addition to the BPS’s current image requirements, is believed to minimize this risk. We feel the imaging criteria align with recommendation No. 5 of the Society of Hospital Medicine’s Position Statement for Ultrasound Guidance for Adult Thoracentesis.²¹ Some BPS members use vascular ultrasound imaging to confirm absence of vascularity, but it is not required and occasionally not possible, such as in the occasional case of PTX with subcutaneous emphysema.

The UA rate is low without a natural comparator in the literature. It is important to clarify the difference between the UAs and the frequently mentioned “failure rate” (FR) in Kulvatunyou et al and Bauman et al^6,7,14 We classify UAs as the inability, for any reason, to access the pleural space and insert a PC. At this stage, these UAs appear to reflect the service’s new experience with PC placement and inability to provide procedural sedation. Kulvatunyou et al and Bauman et al’s FR is defined as an initial PC successfully placed into the pleural space that then required a second PC or intervention (frequently VATS) to resolve the PTX or retained HTX.

We believe calculating the failure rate will be helpful in demonstrating the value of our BPS and our shared-practice model. We look forward to publishing this and other future research, including determination of the cost and time saved by the BPS for PCs and other procedures.

Limitations of this study include its retrospective nature, results from a single center’s experience, and lack of a comparison group.

Our institution feels that there is great benefit in having a BPS operated by procedure-focused hospitalists. It would also be important to determine if our model can be replicated by another institution.

 

Acknowledgments

The authors thank CT surgery for helping to develop this shared-practice model and to both CT surgery and IR physicians here at the Medical College of Wisconsin and Froedtert Hospital who assist us with both IRCs and UAs of pigtail catheters.

The authors also thank Dr. Ricardo Franco-Sadud for his oversight and thoughtful improvements to the paper.