Acute illness requiring hospitalization can be overwhelming for children and their families who are coping with illness and the synthesis of information from a variety of healthcare providers.[1] Patient and family centeredness is endorsed by the Institute of Medicine and the American Academy of Pediatrics[2, 3] as central to quality healthcare. In academic institutions, the presence of medical students and residents adds to the number of providers families encounter. In July 2011, the Accreditation Council for Graduate Medical Education implemented new duty hour restrictions, limiting first year residents to a maximum of 16 hour shifts.[4] Consequently, caregivers and patients may be in contact with more healthcare providers; this fractured care may confuse patients and caregivers, and increase dissatisfaction with care.[5]

The primary objective of our study was to determine the effect of a face sheet tool on the percentage of medical team members correctly identified by caregivers. The secondary objective was to determine the effect of a face sheet tool on the evaluation and satisfaction rating of the medical team by caregivers. We hypothesized that caregivers who receive the face sheet tool will correctly identify a greater percentage of team members by name and role and have higher overall satisfaction with their hospital stay.

METHODS

We performed a prospective controlled study on 2 general pediatric units at Cincinnati Children's Hospital Medical Center (CCHMC). Patients on the intervention unit received the face sheet tool, whereas the concurrent control unit maintained usual procedures. Both units have 24 beds and care for general pediatric patients primarily covered by 4 resident teams and the hospital medicine faculty. Two paired resident teams composed of 2 senior residents, 3 to 4 interns, and 4 medical students primarily admit to each general pediatric unit. Team members rotate through day and night shifts. All employees and rotating students are required to wear the hospital issued identification badge that includes their names, photos, credentials, and role. The study was conducted from November 1, 2011 to November 30, 2011.

Included patients were admitted to the study units by the usual protocol at our hospital, in which nurse patient‐flow coordinators determine bed assignments. We excluded families whose children had an inpatient hospital stay of 12 hours and families who did not speak English. All patient families scheduled to be discharged later in the day on weekday mornings from the 2 study units were approached for study participation. Families were not compensated for their participation.

A face sheet tool, which is a sheet of paper with pictures and names of the intervention team attendings, senior residents, interns, and medical students as well as a description of team member roles, was distributed to patients and their caregivers. The face sheet tools were created using Microsoft Publisher (Microsoft Corp., Redmond, WA). Neither families nor providers were blinded to the intervention, and the residents assumed responsibility for introducing the face sheet tool to families.

For our primary outcome measure, the research coordinator asked participating caregivers to match provider photographs with names and roles by placing laminated pictures backed with Velcro tape in the appropriate position on a laminated poster sheet. Initially, we collected overall accuracy of identification by name and role. In the second week, we began collecting specific data on the attending physician.

The satisfaction survey consisted of the American Board of Internal Medicine (ABIM) patient satisfaction questionnaire, composed of 10, 5‐point Likert scale questions,[6, 7] and an overall rating of hospital question, On a scale from 1 to 10, with 1 being the worst possible hospital and 10 being the best possible hospital, what number would you rate this hospital? from the Hospital Consumer Assessment of Health Plans Survey.[8] Questions were asked aloud and families responded to the questions orally. A written list was also provided to families. We collected data on length of stay (LOS) at the time of outcome assessment as well as previous hospitalizations.

RESULTS

A total of 96 families were approached for enrollment (50 in the intervention and 46 in the control). Of these, 86 families agreed to participate. Three families in the intervention group did not receive the face sheet tool and were excluded from analysis, leaving an analytic cohort of 83 (41 in intervention and 42 in control). Attending recognition by role was collected from 54 families (28 in intervention group and 26 in control group) and by name from 34 families (15 in intervention group and 19 in control group). Table 1 displays characteristics of each group. Among the 83 study participants, LOS at time of outcome assessment ranged from 0.4 to 12.0 days, and the number of medical team members that cared for these patients ranged from 3 to 14.

Families in the intervention group had a higher percentage of correctly identified members of the medical team by name and role as compared to the control group (Table 2). These findings remained significant after adjusting for LOS and prior hospitalization. In addition, in a subset of families with attending data available, more families accurately identified attending name and attending role in the intervention as compared to control group.

No significant differences were noted between the groups when comparing all individual ABIM survey question scores or the overall hospital satisfaction rating (Table 2). Scores in both intervention and control groups were high in all categories.

DISCUSSION

Caregivers given the face sheet tool were better able to identify medical team members by name and role than caregivers in the control group. Previous studies have shown similar results.[9, 10] Families encountered a large number of providers (median of 8) during stays that were on average quite brief (median LOS of 23.6 hours). Despite the significant increase in caregivers' ability to identify providers, the effect was modest.

Our findings add to prior work on face sheet tools in pediatrics and internal medicine.[9, 10, 11] Our study occurred after the residency duty hour restrictions. We described the high number of providers that families encounter in this context. It is the first study to our knowledge to quantify the number of providers that families encounter after these changes and to report on how well families can identify these clinicians by name and role. Unlike other studies, satisfaction scores were not improved.[9] Potential reasons for this include: (1) caregiver knowledge of 2 to 4 key members of the team and not the whole team may be the primary driver of satisfaction, (2) caregiver activation or empowerment may be a more responsive measure than overall satisfaction, and (3) our satisfaction measures may have ceiling effects and/or be elevated in both groups by social desirability bias.

Our study highlights the need for further investigation of quality outcomes associated with residency work hour changes.[12, 13, 14] Specifically, exposure to large numbers of providers may hinder families from accurately identifying those entrusted with the care of their loved one. Of note, our research coordinator needed to present as many as 14 provider pictures to 1 family with a hospital stay of 24 hours. Large numbers of providers may create challenges in building rapport, ensuring effective communication and developing trust with families. We chose to evaluate identification of each team member by caregivers; our findings are suggestive of the need for alternative strategies. A more valuable intervention might target identification of key team members (eg, attending, primary intern, primary senior resident). A policy statement regarding transitions of care recommended the establishment of mechanisms to ensure patients and their families know who is responsible for their care.[15] Efforts toward achieving this goal are essential.

This study has several limitations. The study was completed at a single institution, and thus generalizability may be limited. Although the intervention and control units have similar characteristics, randomization did not occur at the patient level. The control group had significantly more patients who had greater than 1 admission compared to the intervention group. Patients enrolled in the study were from a weekday convenience sample; therefore, potential differences in results based on weekend admissions were unable to be assessed. The exclusion of nonEnglish‐speaking families could limit generalizability to this population. Social desirability bias may have elevated the scores in both groups. Providers tasked with the responsibility of introducing the face sheet tool to families did so in a nonstandardized way and may have interacted differently with families compared to the control team. Finally, our project's aim was focused on the effect of a face sheet tool on the identification and satisfaction rating of the medical team by caregivers. Truly family‐centered care would include efforts to improve families' knowledge of and satisfaction with all members of the healthcare team.

A photo‐based face sheet tool helped caregivers better identify their child's care providers by name and role in the hospital. Satisfaction scores were similar in both groups.

Acute illness requiring hospitalization can be overwhelming for children and their families who are coping with illness and the synthesis of information from a variety of healthcare providers.[1] Patient and family centeredness is endorsed by the Institute of Medicine and the American Academy of Pediatrics[2, 3] as central to quality healthcare. In academic institutions, the presence of medical students and residents adds to the number of providers families encounter. In July 2011, the Accreditation Council for Graduate Medical Education implemented new duty hour restrictions, limiting first year residents to a maximum of 16 hour shifts.[4] Consequently, caregivers and patients may be in contact with more healthcare providers; this fractured care may confuse patients and caregivers, and increase dissatisfaction with care.[5]

The primary objective of our study was to determine the effect of a face sheet tool on the percentage of medical team members correctly identified by caregivers. The secondary objective was to determine the effect of a face sheet tool on the evaluation and satisfaction rating of the medical team by caregivers. We hypothesized that caregivers who receive the face sheet tool will correctly identify a greater percentage of team members by name and role and have higher overall satisfaction with their hospital stay.

METHODS

We performed a prospective controlled study on 2 general pediatric units at Cincinnati Children's Hospital Medical Center (CCHMC). Patients on the intervention unit received the face sheet tool, whereas the concurrent control unit maintained usual procedures. Both units have 24 beds and care for general pediatric patients primarily covered by 4 resident teams and the hospital medicine faculty. Two paired resident teams composed of 2 senior residents, 3 to 4 interns, and 4 medical students primarily admit to each general pediatric unit. Team members rotate through day and night shifts. All employees and rotating students are required to wear the hospital issued identification badge that includes their names, photos, credentials, and role. The study was conducted from November 1, 2011 to November 30, 2011.

Included patients were admitted to the study units by the usual protocol at our hospital, in which nurse patient‐flow coordinators determine bed assignments. We excluded families whose children had an inpatient hospital stay of 12 hours and families who did not speak English. All patient families scheduled to be discharged later in the day on weekday mornings from the 2 study units were approached for study participation. Families were not compensated for their participation.

A face sheet tool, which is a sheet of paper with pictures and names of the intervention team attendings, senior residents, interns, and medical students as well as a description of team member roles, was distributed to patients and their caregivers. The face sheet tools were created using Microsoft Publisher (Microsoft Corp., Redmond, WA). Neither families nor providers were blinded to the intervention, and the residents assumed responsibility for introducing the face sheet tool to families.

For our primary outcome measure, the research coordinator asked participating caregivers to match provider photographs with names and roles by placing laminated pictures backed with Velcro tape in the appropriate position on a laminated poster sheet. Initially, we collected overall accuracy of identification by name and role. In the second week, we began collecting specific data on the attending physician.

The satisfaction survey consisted of the American Board of Internal Medicine (ABIM) patient satisfaction questionnaire, composed of 10, 5‐point Likert scale questions,[6, 7] and an overall rating of hospital question, On a scale from 1 to 10, with 1 being the worst possible hospital and 10 being the best possible hospital, what number would you rate this hospital? from the Hospital Consumer Assessment of Health Plans Survey.[8] Questions were asked aloud and families responded to the questions orally. A written list was also provided to families. We collected data on length of stay (LOS) at the time of outcome assessment as well as previous hospitalizations.

RESULTS

A total of 96 families were approached for enrollment (50 in the intervention and 46 in the control). Of these, 86 families agreed to participate. Three families in the intervention group did not receive the face sheet tool and were excluded from analysis, leaving an analytic cohort of 83 (41 in intervention and 42 in control). Attending recognition by role was collected from 54 families (28 in intervention group and 26 in control group) and by name from 34 families (15 in intervention group and 19 in control group). Table 1 displays characteristics of each group. Among the 83 study participants, LOS at time of outcome assessment ranged from 0.4 to 12.0 days, and the number of medical team members that cared for these patients ranged from 3 to 14.

Families in the intervention group had a higher percentage of correctly identified members of the medical team by name and role as compared to the control group (Table 2). These findings remained significant after adjusting for LOS and prior hospitalization. In addition, in a subset of families with attending data available, more families accurately identified attending name and attending role in the intervention as compared to control group.

No significant differences were noted between the groups when comparing all individual ABIM survey question scores or the overall hospital satisfaction rating (Table 2). Scores in both intervention and control groups were high in all categories.

DISCUSSION

Caregivers given the face sheet tool were better able to identify medical team members by name and role than caregivers in the control group. Previous studies have shown similar results.[9, 10] Families encountered a large number of providers (median of 8) during stays that were on average quite brief (median LOS of 23.6 hours). Despite the significant increase in caregivers' ability to identify providers, the effect was modest.

Our findings add to prior work on face sheet tools in pediatrics and internal medicine.[9, 10, 11] Our study occurred after the residency duty hour restrictions. We described the high number of providers that families encounter in this context. It is the first study to our knowledge to quantify the number of providers that families encounter after these changes and to report on how well families can identify these clinicians by name and role. Unlike other studies, satisfaction scores were not improved.[9] Potential reasons for this include: (1) caregiver knowledge of 2 to 4 key members of the team and not the whole team may be the primary driver of satisfaction, (2) caregiver activation or empowerment may be a more responsive measure than overall satisfaction, and (3) our satisfaction measures may have ceiling effects and/or be elevated in both groups by social desirability bias.

Our study highlights the need for further investigation of quality outcomes associated with residency work hour changes.[12, 13, 14] Specifically, exposure to large numbers of providers may hinder families from accurately identifying those entrusted with the care of their loved one. Of note, our research coordinator needed to present as many as 14 provider pictures to 1 family with a hospital stay of 24 hours. Large numbers of providers may create challenges in building rapport, ensuring effective communication and developing trust with families. We chose to evaluate identification of each team member by caregivers; our findings are suggestive of the need for alternative strategies. A more valuable intervention might target identification of key team members (eg, attending, primary intern, primary senior resident). A policy statement regarding transitions of care recommended the establishment of mechanisms to ensure patients and their families know who is responsible for their care.[15] Efforts toward achieving this goal are essential.

This study has several limitations. The study was completed at a single institution, and thus generalizability may be limited. Although the intervention and control units have similar characteristics, randomization did not occur at the patient level. The control group had significantly more patients who had greater than 1 admission compared to the intervention group. Patients enrolled in the study were from a weekday convenience sample; therefore, potential differences in results based on weekend admissions were unable to be assessed. The exclusion of nonEnglish‐speaking families could limit generalizability to this population. Social desirability bias may have elevated the scores in both groups. Providers tasked with the responsibility of introducing the face sheet tool to families did so in a nonstandardized way and may have interacted differently with families compared to the control team. Finally, our project's aim was focused on the effect of a face sheet tool on the identification and satisfaction rating of the medical team by caregivers. Truly family‐centered care would include efforts to improve families' knowledge of and satisfaction with all members of the healthcare team.

A photo‐based face sheet tool helped caregivers better identify their child's care providers by name and role in the hospital. Satisfaction scores were similar in both groups.

Acute illness requiring hospitalization can be overwhelming for children and their families who are coping with illness and the synthesis of information from a variety of healthcare providers.[1] Patient and family centeredness is endorsed by the Institute of Medicine and the American Academy of Pediatrics[2, 3] as central to quality healthcare. In academic institutions, the presence of medical students and residents adds to the number of providers families encounter. In July 2011, the Accreditation Council for Graduate Medical Education implemented new duty hour restrictions, limiting first year residents to a maximum of 16 hour shifts.[4] Consequently, caregivers and patients may be in contact with more healthcare providers; this fractured care may confuse patients and caregivers, and increase dissatisfaction with care.[5]

The primary objective of our study was to determine the effect of a face sheet tool on the percentage of medical team members correctly identified by caregivers. The secondary objective was to determine the effect of a face sheet tool on the evaluation and satisfaction rating of the medical team by caregivers. We hypothesized that caregivers who receive the face sheet tool will correctly identify a greater percentage of team members by name and role and have higher overall satisfaction with their hospital stay.

METHODS

We performed a prospective controlled study on 2 general pediatric units at Cincinnati Children's Hospital Medical Center (CCHMC). Patients on the intervention unit received the face sheet tool, whereas the concurrent control unit maintained usual procedures. Both units have 24 beds and care for general pediatric patients primarily covered by 4 resident teams and the hospital medicine faculty. Two paired resident teams composed of 2 senior residents, 3 to 4 interns, and 4 medical students primarily admit to each general pediatric unit. Team members rotate through day and night shifts. All employees and rotating students are required to wear the hospital issued identification badge that includes their names, photos, credentials, and role. The study was conducted from November 1, 2011 to November 30, 2011.

Included patients were admitted to the study units by the usual protocol at our hospital, in which nurse patient‐flow coordinators determine bed assignments. We excluded families whose children had an inpatient hospital stay of 12 hours and families who did not speak English. All patient families scheduled to be discharged later in the day on weekday mornings from the 2 study units were approached for study participation. Families were not compensated for their participation.

A face sheet tool, which is a sheet of paper with pictures and names of the intervention team attendings, senior residents, interns, and medical students as well as a description of team member roles, was distributed to patients and their caregivers. The face sheet tools were created using Microsoft Publisher (Microsoft Corp., Redmond, WA). Neither families nor providers were blinded to the intervention, and the residents assumed responsibility for introducing the face sheet tool to families.

For our primary outcome measure, the research coordinator asked participating caregivers to match provider photographs with names and roles by placing laminated pictures backed with Velcro tape in the appropriate position on a laminated poster sheet. Initially, we collected overall accuracy of identification by name and role. In the second week, we began collecting specific data on the attending physician.

The satisfaction survey consisted of the American Board of Internal Medicine (ABIM) patient satisfaction questionnaire, composed of 10, 5‐point Likert scale questions,[6, 7] and an overall rating of hospital question, On a scale from 1 to 10, with 1 being the worst possible hospital and 10 being the best possible hospital, what number would you rate this hospital? from the Hospital Consumer Assessment of Health Plans Survey.[8] Questions were asked aloud and families responded to the questions orally. A written list was also provided to families. We collected data on length of stay (LOS) at the time of outcome assessment as well as previous hospitalizations.

RESULTS

A total of 96 families were approached for enrollment (50 in the intervention and 46 in the control). Of these, 86 families agreed to participate. Three families in the intervention group did not receive the face sheet tool and were excluded from analysis, leaving an analytic cohort of 83 (41 in intervention and 42 in control). Attending recognition by role was collected from 54 families (28 in intervention group and 26 in control group) and by name from 34 families (15 in intervention group and 19 in control group). Table 1 displays characteristics of each group. Among the 83 study participants, LOS at time of outcome assessment ranged from 0.4 to 12.0 days, and the number of medical team members that cared for these patients ranged from 3 to 14.

Families in the intervention group had a higher percentage of correctly identified members of the medical team by name and role as compared to the control group (Table 2). These findings remained significant after adjusting for LOS and prior hospitalization. In addition, in a subset of families with attending data available, more families accurately identified attending name and attending role in the intervention as compared to control group.

No significant differences were noted between the groups when comparing all individual ABIM survey question scores or the overall hospital satisfaction rating (Table 2). Scores in both intervention and control groups were high in all categories.

DISCUSSION

Caregivers given the face sheet tool were better able to identify medical team members by name and role than caregivers in the control group. Previous studies have shown similar results.[9, 10] Families encountered a large number of providers (median of 8) during stays that were on average quite brief (median LOS of 23.6 hours). Despite the significant increase in caregivers' ability to identify providers, the effect was modest.

Our findings add to prior work on face sheet tools in pediatrics and internal medicine.[9, 10, 11] Our study occurred after the residency duty hour restrictions. We described the high number of providers that families encounter in this context. It is the first study to our knowledge to quantify the number of providers that families encounter after these changes and to report on how well families can identify these clinicians by name and role. Unlike other studies, satisfaction scores were not improved.[9] Potential reasons for this include: (1) caregiver knowledge of 2 to 4 key members of the team and not the whole team may be the primary driver of satisfaction, (2) caregiver activation or empowerment may be a more responsive measure than overall satisfaction, and (3) our satisfaction measures may have ceiling effects and/or be elevated in both groups by social desirability bias.

Our study highlights the need for further investigation of quality outcomes associated with residency work hour changes.[12, 13, 14] Specifically, exposure to large numbers of providers may hinder families from accurately identifying those entrusted with the care of their loved one. Of note, our research coordinator needed to present as many as 14 provider pictures to 1 family with a hospital stay of 24 hours. Large numbers of providers may create challenges in building rapport, ensuring effective communication and developing trust with families. We chose to evaluate identification of each team member by caregivers; our findings are suggestive of the need for alternative strategies. A more valuable intervention might target identification of key team members (eg, attending, primary intern, primary senior resident). A policy statement regarding transitions of care recommended the establishment of mechanisms to ensure patients and their families know who is responsible for their care.[15] Efforts toward achieving this goal are essential.

This study has several limitations. The study was completed at a single institution, and thus generalizability may be limited. Although the intervention and control units have similar characteristics, randomization did not occur at the patient level. The control group had significantly more patients who had greater than 1 admission compared to the intervention group. Patients enrolled in the study were from a weekday convenience sample; therefore, potential differences in results based on weekend admissions were unable to be assessed. The exclusion of nonEnglish‐speaking families could limit generalizability to this population. Social desirability bias may have elevated the scores in both groups. Providers tasked with the responsibility of introducing the face sheet tool to families did so in a nonstandardized way and may have interacted differently with families compared to the control team. Finally, our project's aim was focused on the effect of a face sheet tool on the identification and satisfaction rating of the medical team by caregivers. Truly family‐centered care would include efforts to improve families' knowledge of and satisfaction with all members of the healthcare team.

A photo‐based face sheet tool helped caregivers better identify their child's care providers by name and role in the hospital. Satisfaction scores were similar in both groups.

The use of hand‐carried ultrasound by nonspecialists is increasing. Of particular interest to hospitalists is bedside ultrasound assessment of the inferior vena cava (IVC), which more accurately estimates left atrial pressure than does assessment of jugular venous pressure by physical examination.[1] Invasively measured central venous pressure (CVP) also correlates closely with estimates from IVC imaging.[1, 2, 3, 4] Although quick, accurate bedside determination of CVP may have broad potential applications in hospital medicine,[5, 6, 7, 8] of particular interest to patients and their advocates is whether hospitalists are sufficiently skilled to perform this procedure. Lucas et al. found that 8 hospitalists trained to perform 6 cardiac assessments by hand‐carried ultrasound could identify an enlarged IVC with moderate accuracy (sensitivity 56%, specificity 86%).[9] To our knowledge, no other study has examined whether hospitalists can readily develop the skills to accurately assess the IVC by ultrasound. We therefore studied whether the skills needed to acquire and interpret IVC images by ultrasound could be acquired by hospitalists after a brief training program.

METHODS

RESULTS

From among 18 hospitalist volunteers, the 10 board‐certified hospitalists who could attend 1 of the scheduled training sessions were enrolled and completed the study. Hospitalists' demographic information and performance are summarized in Table 1. Hospitalists completed the initial online curriculum in an average of 18.37 minutes. After the in‐person training session, 8 of 10 hospitalists acquired adequate IVC images on all 5 volunteer subjects. One hospitalist obtained adequate images in 4 of 5 patients. Another hospitalist only obtained adequate images in 3 of 5 patients; a hepatic vein and the abdominal aorta were erroneously measured instead of the IVC in 1 subject each. This hospitalist later performed supervised IVC imaging on 7 additional hospital inpatients and was the only hospitalist to request additional direct supervision by the research echocardiography technician. All hospitalists were able to accurately quantify the IVC collapsibility index and estimate the CVP from all 10 prerecorded cases showing still images and video clips of the IVC. Based on IVC images, 1 of the 5 volunteers used in testing each day had a very elevated CVP, and the other 4 had CVPs ranging from low to normal. The volunteer's average BMI was overweight at 27.4, with a range from 15.4 to 37.1.

At 7.40.7 weeks (range, 6.98.6 weeks) follow‐up, 9 of 10 hospitalists obtained adequate IVC images in all 5 volunteer subjects and interpreted them correctly for estimating CVP. The hospitalist who performed most poorly at the initial assessment acquired adequate images and interpreted them correctly in 4 of 5 patients at follow‐up. Overall, hospitalists' visual assessment of IVC collapsibility index agreed with the quantitative collapsibility index calculation in 180 of 198 (91%) of the interpretable encounters. By the time of the follow‐up assessment, hospitalists had performed IVC imaging on 3.93.0 additional hospital inpatients (range, 011 inpatients). Lack of time assigned to the clinical service was the main barrier limiting further IVC imaging during that interval. Hospitalists also identified time constraints and need for secure yet accessible device storage as other barriers.

None of the hospitalists had previous experience imaging the IVC, and prior to training they rated their average confidence to acquire an IVC image and interpret it by the hand‐carried ultrasound device at 3 (3, 4) and 3 (3, 4), respectively. After the initial training session, 9 of 10 hospitalists believed they had received adequate online and in‐person training and were confident in their ability to acquire and interpret IVC images. After all training sessions the hospitalists on average rated their confidence statistically significantly better for acquiring and interpreting IVC images at 2 (1, 2) (P=0.005) and 2 (1, 2) (P=0.004), respectively compared to baseline.

DISCUSSION

This study shows that after a relatively brief training intervention, hospitalists can develop and over a short term retain important skills in the acquisition and interpretation of IVC images to estimate CVP. Estimating CVP is key to the care of many patients, but cannot be done accurately by most physicians.[10] Although our study has a number of limitations, the ability to estimate CVP acquired after only a brief training intervention could have important effects on patient care. Given that a dilated IVC with reduced respiratory collapsibility was found to be a statistically significant predictor of 30‐day readmission for heart failure,¹¹ key clinical outcomes to measure in future work include whether IVC ultrasound assessment can help guide diuresis, limit complications, and ultimately reduce rehospitalizations for heart failure, the most expensive diagnosis for Medicare.[12]

Because hand‐carried ultrasound is a point‐of‐care diagnostic tool, we also examined the ability of hospitalists to visually approximate the IVC collapsibility index. Hospitalists' qualitative performance (IVC collapsibility judged correctly 91% of the time without performing formal measurements) is consistent with studies involving emergency medicine physicians and suggests that CVP may be rapidly and accurately estimated in most instances.[13] There may be, however, value to formally measuring the IVC maximum diameter, because it may be inaccurately visually estimated due to changes in scale when the imaging depth is adjusted. Accurately measuring the IVC maximum diameter is important because a maximum diameter of more than 2.0 cm is evidence of an elevated right atrial pressure (82% sensitivity and 84% specificity for predicting right atrial pressure of 10 mm Hg or above) and an elevated pulmonary capillary wedge pressure (75% sensitivity and 83% specificity for pulmonary capillary wedge pressure of 15 mm Hg or more).[14]

Limitations

Our findings should be interpreted cautiously given the relatively small number of hospitalists and subjects used for hand‐carried ultrasound imaging. Although our direct observations of hospitalist performance in IVC imaging were based on objective measurements performed and interpreted accurately, we did not record the images, which would have allowed separate analyses of inter‐rater reliability measures. The majority of volunteer subjects were chronically ill, but they were nonetheless stable outpatients and may have been easier to position and image relative to acutely ill inpatients. Hospitalist self‐selected participation may introduce a bias favoring hospitalists interested in learning hand‐carried ultrasound skills; however, nearly half of the hospitalist group volunteered and enrollments in the study were based only on their availability for the previously scheduled study dates.

IMPLICATIONS FOR TRAINING

Our study, especially the assessment of the hospitalists' ability to retain their skills, adds to what is known about training hospitalists in hand‐carried ultrasound and may help inform deliberations among hospitalists as to whether to join other professional societies in defining specialty‐specific bedside ultrasound indications and training protocols.[9, 15] As individuals acquire new skills at variable rates, training cannot be defined by the number of procedures performed, but rather by the need to provide objective evidence of acquired procedural skills. Thus, going forward there is also a need to develop and validate tools for assessment of competence in IVC imaging skills.

The use of hand‐carried ultrasound by nonspecialists is increasing. Of particular interest to hospitalists is bedside ultrasound assessment of the inferior vena cava (IVC), which more accurately estimates left atrial pressure than does assessment of jugular venous pressure by physical examination.[1] Invasively measured central venous pressure (CVP) also correlates closely with estimates from IVC imaging.[1, 2, 3, 4] Although quick, accurate bedside determination of CVP may have broad potential applications in hospital medicine,[5, 6, 7, 8] of particular interest to patients and their advocates is whether hospitalists are sufficiently skilled to perform this procedure. Lucas et al. found that 8 hospitalists trained to perform 6 cardiac assessments by hand‐carried ultrasound could identify an enlarged IVC with moderate accuracy (sensitivity 56%, specificity 86%).[9] To our knowledge, no other study has examined whether hospitalists can readily develop the skills to accurately assess the IVC by ultrasound. We therefore studied whether the skills needed to acquire and interpret IVC images by ultrasound could be acquired by hospitalists after a brief training program.

METHODS

RESULTS

From among 18 hospitalist volunteers, the 10 board‐certified hospitalists who could attend 1 of the scheduled training sessions were enrolled and completed the study. Hospitalists' demographic information and performance are summarized in Table 1. Hospitalists completed the initial online curriculum in an average of 18.37 minutes. After the in‐person training session, 8 of 10 hospitalists acquired adequate IVC images on all 5 volunteer subjects. One hospitalist obtained adequate images in 4 of 5 patients. Another hospitalist only obtained adequate images in 3 of 5 patients; a hepatic vein and the abdominal aorta were erroneously measured instead of the IVC in 1 subject each. This hospitalist later performed supervised IVC imaging on 7 additional hospital inpatients and was the only hospitalist to request additional direct supervision by the research echocardiography technician. All hospitalists were able to accurately quantify the IVC collapsibility index and estimate the CVP from all 10 prerecorded cases showing still images and video clips of the IVC. Based on IVC images, 1 of the 5 volunteers used in testing each day had a very elevated CVP, and the other 4 had CVPs ranging from low to normal. The volunteer's average BMI was overweight at 27.4, with a range from 15.4 to 37.1.

At 7.40.7 weeks (range, 6.98.6 weeks) follow‐up, 9 of 10 hospitalists obtained adequate IVC images in all 5 volunteer subjects and interpreted them correctly for estimating CVP. The hospitalist who performed most poorly at the initial assessment acquired adequate images and interpreted them correctly in 4 of 5 patients at follow‐up. Overall, hospitalists' visual assessment of IVC collapsibility index agreed with the quantitative collapsibility index calculation in 180 of 198 (91%) of the interpretable encounters. By the time of the follow‐up assessment, hospitalists had performed IVC imaging on 3.93.0 additional hospital inpatients (range, 011 inpatients). Lack of time assigned to the clinical service was the main barrier limiting further IVC imaging during that interval. Hospitalists also identified time constraints and need for secure yet accessible device storage as other barriers.

None of the hospitalists had previous experience imaging the IVC, and prior to training they rated their average confidence to acquire an IVC image and interpret it by the hand‐carried ultrasound device at 3 (3, 4) and 3 (3, 4), respectively. After the initial training session, 9 of 10 hospitalists believed they had received adequate online and in‐person training and were confident in their ability to acquire and interpret IVC images. After all training sessions the hospitalists on average rated their confidence statistically significantly better for acquiring and interpreting IVC images at 2 (1, 2) (P=0.005) and 2 (1, 2) (P=0.004), respectively compared to baseline.

DISCUSSION

This study shows that after a relatively brief training intervention, hospitalists can develop and over a short term retain important skills in the acquisition and interpretation of IVC images to estimate CVP. Estimating CVP is key to the care of many patients, but cannot be done accurately by most physicians.[10] Although our study has a number of limitations, the ability to estimate CVP acquired after only a brief training intervention could have important effects on patient care. Given that a dilated IVC with reduced respiratory collapsibility was found to be a statistically significant predictor of 30‐day readmission for heart failure,¹¹ key clinical outcomes to measure in future work include whether IVC ultrasound assessment can help guide diuresis, limit complications, and ultimately reduce rehospitalizations for heart failure, the most expensive diagnosis for Medicare.[12]

Because hand‐carried ultrasound is a point‐of‐care diagnostic tool, we also examined the ability of hospitalists to visually approximate the IVC collapsibility index. Hospitalists' qualitative performance (IVC collapsibility judged correctly 91% of the time without performing formal measurements) is consistent with studies involving emergency medicine physicians and suggests that CVP may be rapidly and accurately estimated in most instances.[13] There may be, however, value to formally measuring the IVC maximum diameter, because it may be inaccurately visually estimated due to changes in scale when the imaging depth is adjusted. Accurately measuring the IVC maximum diameter is important because a maximum diameter of more than 2.0 cm is evidence of an elevated right atrial pressure (82% sensitivity and 84% specificity for predicting right atrial pressure of 10 mm Hg or above) and an elevated pulmonary capillary wedge pressure (75% sensitivity and 83% specificity for pulmonary capillary wedge pressure of 15 mm Hg or more).[14]

Limitations

Our findings should be interpreted cautiously given the relatively small number of hospitalists and subjects used for hand‐carried ultrasound imaging. Although our direct observations of hospitalist performance in IVC imaging were based on objective measurements performed and interpreted accurately, we did not record the images, which would have allowed separate analyses of inter‐rater reliability measures. The majority of volunteer subjects were chronically ill, but they were nonetheless stable outpatients and may have been easier to position and image relative to acutely ill inpatients. Hospitalist self‐selected participation may introduce a bias favoring hospitalists interested in learning hand‐carried ultrasound skills; however, nearly half of the hospitalist group volunteered and enrollments in the study were based only on their availability for the previously scheduled study dates.

IMPLICATIONS FOR TRAINING

Our study, especially the assessment of the hospitalists' ability to retain their skills, adds to what is known about training hospitalists in hand‐carried ultrasound and may help inform deliberations among hospitalists as to whether to join other professional societies in defining specialty‐specific bedside ultrasound indications and training protocols.[9, 15] As individuals acquire new skills at variable rates, training cannot be defined by the number of procedures performed, but rather by the need to provide objective evidence of acquired procedural skills. Thus, going forward there is also a need to develop and validate tools for assessment of competence in IVC imaging skills.

The use of hand‐carried ultrasound by nonspecialists is increasing. Of particular interest to hospitalists is bedside ultrasound assessment of the inferior vena cava (IVC), which more accurately estimates left atrial pressure than does assessment of jugular venous pressure by physical examination.[1] Invasively measured central venous pressure (CVP) also correlates closely with estimates from IVC imaging.[1, 2, 3, 4] Although quick, accurate bedside determination of CVP may have broad potential applications in hospital medicine,[5, 6, 7, 8] of particular interest to patients and their advocates is whether hospitalists are sufficiently skilled to perform this procedure. Lucas et al. found that 8 hospitalists trained to perform 6 cardiac assessments by hand‐carried ultrasound could identify an enlarged IVC with moderate accuracy (sensitivity 56%, specificity 86%).[9] To our knowledge, no other study has examined whether hospitalists can readily develop the skills to accurately assess the IVC by ultrasound. We therefore studied whether the skills needed to acquire and interpret IVC images by ultrasound could be acquired by hospitalists after a brief training program.

METHODS

RESULTS

From among 18 hospitalist volunteers, the 10 board‐certified hospitalists who could attend 1 of the scheduled training sessions were enrolled and completed the study. Hospitalists' demographic information and performance are summarized in Table 1. Hospitalists completed the initial online curriculum in an average of 18.37 minutes. After the in‐person training session, 8 of 10 hospitalists acquired adequate IVC images on all 5 volunteer subjects. One hospitalist obtained adequate images in 4 of 5 patients. Another hospitalist only obtained adequate images in 3 of 5 patients; a hepatic vein and the abdominal aorta were erroneously measured instead of the IVC in 1 subject each. This hospitalist later performed supervised IVC imaging on 7 additional hospital inpatients and was the only hospitalist to request additional direct supervision by the research echocardiography technician. All hospitalists were able to accurately quantify the IVC collapsibility index and estimate the CVP from all 10 prerecorded cases showing still images and video clips of the IVC. Based on IVC images, 1 of the 5 volunteers used in testing each day had a very elevated CVP, and the other 4 had CVPs ranging from low to normal. The volunteer's average BMI was overweight at 27.4, with a range from 15.4 to 37.1.

At 7.40.7 weeks (range, 6.98.6 weeks) follow‐up, 9 of 10 hospitalists obtained adequate IVC images in all 5 volunteer subjects and interpreted them correctly for estimating CVP. The hospitalist who performed most poorly at the initial assessment acquired adequate images and interpreted them correctly in 4 of 5 patients at follow‐up. Overall, hospitalists' visual assessment of IVC collapsibility index agreed with the quantitative collapsibility index calculation in 180 of 198 (91%) of the interpretable encounters. By the time of the follow‐up assessment, hospitalists had performed IVC imaging on 3.93.0 additional hospital inpatients (range, 011 inpatients). Lack of time assigned to the clinical service was the main barrier limiting further IVC imaging during that interval. Hospitalists also identified time constraints and need for secure yet accessible device storage as other barriers.

None of the hospitalists had previous experience imaging the IVC, and prior to training they rated their average confidence to acquire an IVC image and interpret it by the hand‐carried ultrasound device at 3 (3, 4) and 3 (3, 4), respectively. After the initial training session, 9 of 10 hospitalists believed they had received adequate online and in‐person training and were confident in their ability to acquire and interpret IVC images. After all training sessions the hospitalists on average rated their confidence statistically significantly better for acquiring and interpreting IVC images at 2 (1, 2) (P=0.005) and 2 (1, 2) (P=0.004), respectively compared to baseline.

DISCUSSION

This study shows that after a relatively brief training intervention, hospitalists can develop and over a short term retain important skills in the acquisition and interpretation of IVC images to estimate CVP. Estimating CVP is key to the care of many patients, but cannot be done accurately by most physicians.[10] Although our study has a number of limitations, the ability to estimate CVP acquired after only a brief training intervention could have important effects on patient care. Given that a dilated IVC with reduced respiratory collapsibility was found to be a statistically significant predictor of 30‐day readmission for heart failure,¹¹ key clinical outcomes to measure in future work include whether IVC ultrasound assessment can help guide diuresis, limit complications, and ultimately reduce rehospitalizations for heart failure, the most expensive diagnosis for Medicare.[12]

Because hand‐carried ultrasound is a point‐of‐care diagnostic tool, we also examined the ability of hospitalists to visually approximate the IVC collapsibility index. Hospitalists' qualitative performance (IVC collapsibility judged correctly 91% of the time without performing formal measurements) is consistent with studies involving emergency medicine physicians and suggests that CVP may be rapidly and accurately estimated in most instances.[13] There may be, however, value to formally measuring the IVC maximum diameter, because it may be inaccurately visually estimated due to changes in scale when the imaging depth is adjusted. Accurately measuring the IVC maximum diameter is important because a maximum diameter of more than 2.0 cm is evidence of an elevated right atrial pressure (82% sensitivity and 84% specificity for predicting right atrial pressure of 10 mm Hg or above) and an elevated pulmonary capillary wedge pressure (75% sensitivity and 83% specificity for pulmonary capillary wedge pressure of 15 mm Hg or more).[14]

Limitations

Our findings should be interpreted cautiously given the relatively small number of hospitalists and subjects used for hand‐carried ultrasound imaging. Although our direct observations of hospitalist performance in IVC imaging were based on objective measurements performed and interpreted accurately, we did not record the images, which would have allowed separate analyses of inter‐rater reliability measures. The majority of volunteer subjects were chronically ill, but they were nonetheless stable outpatients and may have been easier to position and image relative to acutely ill inpatients. Hospitalist self‐selected participation may introduce a bias favoring hospitalists interested in learning hand‐carried ultrasound skills; however, nearly half of the hospitalist group volunteered and enrollments in the study were based only on their availability for the previously scheduled study dates.

IMPLICATIONS FOR TRAINING

Our study, especially the assessment of the hospitalists' ability to retain their skills, adds to what is known about training hospitalists in hand‐carried ultrasound and may help inform deliberations among hospitalists as to whether to join other professional societies in defining specialty‐specific bedside ultrasound indications and training protocols.[9, 15] As individuals acquire new skills at variable rates, training cannot be defined by the number of procedures performed, but rather by the need to provide objective evidence of acquired procedural skills. Thus, going forward there is also a need to develop and validate tools for assessment of competence in IVC imaging skills.

Recent reports have drawn attention to the high and increasing rates of opioid prescribing and overdose‐related deaths in the United States.[1, 2, 3, 4, 5, 6, 7, 8, 9] These studies have focused on community‐based and emergency department prescribing, leaving prescribing practices in the inpatient setting unexamined. Given that pain is a frequent complaint in hospitalized patients, and that the Joint Commission mandates assessing pain as a vital sign, hospitalization is potentially a time of heightened use of such medications and could significantly contribute to nosocomial complications and subsequent outpatient use.[10] Variation in prescribing practices, unrelated to patient characteristics, could be a marker of inappropriate prescribing practices and poor quality of care.

Using a large, nationally representative cohort of admissions from July 2009 to June 2010, we sought to determine patterns and predictors of opioid utilization in nonsurgical admissions to US medical centers, hospital variation in use, and the association between hospital‐level use and the risk of opioid‐related adverse events. We hypothesized that hospitals with higher rates of opioid use would have an increased risk of an opioid‐related adverse event per patient exposed.

METHODS

RESULTS

Variation in Opioid Prescribing

Figure 1 shows the histograms of hospital opioid prescribing rate for the 286 hospitals in our cohort (A) before and (B) after adjustment for patient characteristics. The observed rates ranged from 5% in the lowest‐prescribing hospital to 72% in the highest‐prescribing hospital, with a mean (SD) of 51% (10%). After adjusting for patient characteristics, the adjusted opioid‐prescribing rates ranged from 33% to 64%, with a mean (SD) of 50% (4%).

Figure 1

DISCUSSION

In this analysis of a large cohort of hospitalized nonsurgical patients, we found that more than half of all patients received opioids, with 43% of those exposed receiving multiple opioids during their admission and 52% receiving opioids on the day of discharge. Considerable hospital variation in opioid use was evident, and this was not fully explained by patient characteristics. Severe opioid‐related adverse events occurred more frequently at hospitals with higher opioid‐prescribing rates, and the relative risk of a severe adverse event per patient prescribed opioids was also higher in these hospitals. To our knowledge, this is the first study to describe the scope of opioid utilization and the relationship between utilization and severe opioid‐related adverse events in a sample of nonsurgical patients in US acute‐care facilities.

Our use of naloxone charges and opioid‐specific ICD‐9‐CM coding to define an opioid‐related adverse event was intended to capture only the most severe opioid‐related adverse events. We chose to focus on these events in our analysis to maximize the specificity of our outcome definition and thereby minimize confounding in our observed associations. The rate of less‐severe opioid‐related adverse events, such as nausea, constipation, and pruritis, is likely much higher and not captured in our outcome definition. Prior analyses have found variable rates of opioid‐related adverse events of approximately 1.8% to 13.6% of exposed patients.[22, 23, 24] However, these analyses focused on surgical patients and included less‐severe events. To our knowledge, ours is the first analysis of severe opioid‐related adverse events in nonsurgical patients.

Our finding that severe opioid‐related adverse events increase as opioid prescribing increases is consistent with that which has been demonstrated in the community setting, where rates of opioid‐related adverse events and mortality are higher in communities with higher levels of opioid prescribing.[2, 8, 25] This finding is expected, as greater use of a class of medications with known side effects would be expected to result in a higher overall rate of adverse events. More concerning, however, is the fact that this relationship persists when focusing exclusively on opioid‐exposed patients. Among similar patients receiving opioids at different hospitals, those hospitalized in facilities with higher opioid‐prescribing rates have higher rates of severe opioid‐related adverse events. This suggests that hospitals that use opioids more frequently do not do so more safely. Rather, the increased overall prescribing rates are associated with heightened risk for a serious adverse event per patient exposed and may reflect unsafe prescribing practices.

Furthermore, our results demonstrate both regional and hospital variation in use of opioids not fully explained by patient characteristics, similar to that which has been demonstrated for other drugs and heathcare services.[26, 27, 28, 29, 30] The implications of these findings are limited by our lack of information on pain severity or prior outpatient treatment, and our resultant inability to evaluate the appropriateness of opioid use in this analysis. Additionally, although we controlled for a large number of patient and hospital characteristics, there could be other significant predictors of use not accounted for in our analysis. However, it seems unlikely that differential pain severity or patient characteristics between patients in different regions of the country could fully explain a 37% relative difference in prescribing between the lowest‐ and highest‐prescribing regions, after accounting for the 44 patient‐level variables in our models. Whereas variation in use unrelated to patient factors could represent inappropriate prescribing practices, it could also be a marker of uncertainty regarding what constitutes appropriate prescribing and high‐quality care in this realm. Although guidelines advocate for standard pain assessments and a step‐up approach to treatment,[31, 32, 33] the lack of objective measures of pain severity and lack of evidence‐based recommendations on the use of opioids for noncancer pain[34] will almost certainly lead to persistent variation in opioid prescribing despite guideline‐driven care.

Nonetheless, our findings suggest that opportunities exist to make opioid prescribing safer in hospitalized patients. Studies aimed at elucidating the source of regional and hospital variation are necessary. Additionally, efforts should focus on identifying patient and prescribing characteristics associated with heightened risk of opioid‐related adverse events. Prior studies have demonstrated that the risks of opioid medications increase with increasing age of the patient.[35, 36] Although opioid use in our cohort declined with age, 44% of admissions age 65 years had charges for opioid medications. Studies in outpatients have also demonstrated that the risks of opioid overdose and overdose‐related death increase with dose.[5, 7] One study demonstrated a 3.7‐fold increased risk of overdose at doses of 5099 mg/day in oral morphine equivalents, and an 8.9‐fold increased risk at doses of 100 mg/day, compared with doses of 20 mg/day.[7] The prevalence of high dose exposure observed in our cohort, coupled with the older age of hospitalized patients, suggests potential targets for promoting safer use in hospitalized patients through interventions such as computerized decision support and enhanced monitoring in those at highest risk.

Because medications after discharge were unavailable in our dataset, the percentage of patients given a prescription for opioid medication on discharge is unknown. However, given that opioids are often tapered rather than abruptly discontinued, our finding that 26% of hospitalized nonsurgical patients received opioids on the day of discharge suggests that a substantial proportion of patients may be discharged with a prescription for opioid medication. Given the possibility of coexistent outpatient opioid prescriptions, these findings draw attention to the importance of assuring development and streamlined accessibility of data from state prescription drug monitoring programs and suggest that increased attention should be paid to the role that inpatient opioid prescribing plays in the increased rates of chronic opioid use and overdose‐related deaths in the United States.

There are additional limitations to our analysis. First, although the database used for this analysis captures a large proportion of admissions to US acute‐care facilities and is similar in composition, it is possible that participating medical centers differ from nonparticipating medical centers in ways that could be associated with opioid prescribing. Additionally, although Premier performs extensive validation and correction processes to assure the quality of their data, there is still likely to be a small amount of random error in the database, which could particularly impact dosage calculations. The lack of preadmission medications in our database precluded identification of the proportion of patients newly started on opioid medications. Lastly, it is possible that the hospital prescribing‐rate quartile is associated with patient characteristics unaccounted for in our analysis, and, therefore, the possibility of residual confounding still exists.

In conclusion, the majority of hospitalized nonsurgical patients are exposed to opioid medications during the course of their hospitalizations, often at high doses. More than half of those exposed are still receiving these medications on the day of discharge. We found hospital and regional variation in opioid use that was not fully explained by patient characteristics, and higher levels of hospital use were associated with higher risk of severe opioid‐related adverse events in opioid‐exposed patients. Further research is necessary to investigate the appropriateness of opioid use in this patient population, the sources of variation in use, and the predictors of opioid‐related adverse events in hospitalized patients to allow development of interventions to make hospital use safer.

Recent reports have drawn attention to the high and increasing rates of opioid prescribing and overdose‐related deaths in the United States.[1, 2, 3, 4, 5, 6, 7, 8, 9] These studies have focused on community‐based and emergency department prescribing, leaving prescribing practices in the inpatient setting unexamined. Given that pain is a frequent complaint in hospitalized patients, and that the Joint Commission mandates assessing pain as a vital sign, hospitalization is potentially a time of heightened use of such medications and could significantly contribute to nosocomial complications and subsequent outpatient use.[10] Variation in prescribing practices, unrelated to patient characteristics, could be a marker of inappropriate prescribing practices and poor quality of care.

Using a large, nationally representative cohort of admissions from July 2009 to June 2010, we sought to determine patterns and predictors of opioid utilization in nonsurgical admissions to US medical centers, hospital variation in use, and the association between hospital‐level use and the risk of opioid‐related adverse events. We hypothesized that hospitals with higher rates of opioid use would have an increased risk of an opioid‐related adverse event per patient exposed.

METHODS

RESULTS

Variation in Opioid Prescribing

Figure 1 shows the histograms of hospital opioid prescribing rate for the 286 hospitals in our cohort (A) before and (B) after adjustment for patient characteristics. The observed rates ranged from 5% in the lowest‐prescribing hospital to 72% in the highest‐prescribing hospital, with a mean (SD) of 51% (10%). After adjusting for patient characteristics, the adjusted opioid‐prescribing rates ranged from 33% to 64%, with a mean (SD) of 50% (4%).

Figure 1

DISCUSSION

In this analysis of a large cohort of hospitalized nonsurgical patients, we found that more than half of all patients received opioids, with 43% of those exposed receiving multiple opioids during their admission and 52% receiving opioids on the day of discharge. Considerable hospital variation in opioid use was evident, and this was not fully explained by patient characteristics. Severe opioid‐related adverse events occurred more frequently at hospitals with higher opioid‐prescribing rates, and the relative risk of a severe adverse event per patient prescribed opioids was also higher in these hospitals. To our knowledge, this is the first study to describe the scope of opioid utilization and the relationship between utilization and severe opioid‐related adverse events in a sample of nonsurgical patients in US acute‐care facilities.

Our use of naloxone charges and opioid‐specific ICD‐9‐CM coding to define an opioid‐related adverse event was intended to capture only the most severe opioid‐related adverse events. We chose to focus on these events in our analysis to maximize the specificity of our outcome definition and thereby minimize confounding in our observed associations. The rate of less‐severe opioid‐related adverse events, such as nausea, constipation, and pruritis, is likely much higher and not captured in our outcome definition. Prior analyses have found variable rates of opioid‐related adverse events of approximately 1.8% to 13.6% of exposed patients.[22, 23, 24] However, these analyses focused on surgical patients and included less‐severe events. To our knowledge, ours is the first analysis of severe opioid‐related adverse events in nonsurgical patients.

Our finding that severe opioid‐related adverse events increase as opioid prescribing increases is consistent with that which has been demonstrated in the community setting, where rates of opioid‐related adverse events and mortality are higher in communities with higher levels of opioid prescribing.[2, 8, 25] This finding is expected, as greater use of a class of medications with known side effects would be expected to result in a higher overall rate of adverse events. More concerning, however, is the fact that this relationship persists when focusing exclusively on opioid‐exposed patients. Among similar patients receiving opioids at different hospitals, those hospitalized in facilities with higher opioid‐prescribing rates have higher rates of severe opioid‐related adverse events. This suggests that hospitals that use opioids more frequently do not do so more safely. Rather, the increased overall prescribing rates are associated with heightened risk for a serious adverse event per patient exposed and may reflect unsafe prescribing practices.

Furthermore, our results demonstrate both regional and hospital variation in use of opioids not fully explained by patient characteristics, similar to that which has been demonstrated for other drugs and heathcare services.[26, 27, 28, 29, 30] The implications of these findings are limited by our lack of information on pain severity or prior outpatient treatment, and our resultant inability to evaluate the appropriateness of opioid use in this analysis. Additionally, although we controlled for a large number of patient and hospital characteristics, there could be other significant predictors of use not accounted for in our analysis. However, it seems unlikely that differential pain severity or patient characteristics between patients in different regions of the country could fully explain a 37% relative difference in prescribing between the lowest‐ and highest‐prescribing regions, after accounting for the 44 patient‐level variables in our models. Whereas variation in use unrelated to patient factors could represent inappropriate prescribing practices, it could also be a marker of uncertainty regarding what constitutes appropriate prescribing and high‐quality care in this realm. Although guidelines advocate for standard pain assessments and a step‐up approach to treatment,[31, 32, 33] the lack of objective measures of pain severity and lack of evidence‐based recommendations on the use of opioids for noncancer pain[34] will almost certainly lead to persistent variation in opioid prescribing despite guideline‐driven care.

Nonetheless, our findings suggest that opportunities exist to make opioid prescribing safer in hospitalized patients. Studies aimed at elucidating the source of regional and hospital variation are necessary. Additionally, efforts should focus on identifying patient and prescribing characteristics associated with heightened risk of opioid‐related adverse events. Prior studies have demonstrated that the risks of opioid medications increase with increasing age of the patient.[35, 36] Although opioid use in our cohort declined with age, 44% of admissions age 65 years had charges for opioid medications. Studies in outpatients have also demonstrated that the risks of opioid overdose and overdose‐related death increase with dose.[5, 7] One study demonstrated a 3.7‐fold increased risk of overdose at doses of 5099 mg/day in oral morphine equivalents, and an 8.9‐fold increased risk at doses of 100 mg/day, compared with doses of 20 mg/day.[7] The prevalence of high dose exposure observed in our cohort, coupled with the older age of hospitalized patients, suggests potential targets for promoting safer use in hospitalized patients through interventions such as computerized decision support and enhanced monitoring in those at highest risk.

Because medications after discharge were unavailable in our dataset, the percentage of patients given a prescription for opioid medication on discharge is unknown. However, given that opioids are often tapered rather than abruptly discontinued, our finding that 26% of hospitalized nonsurgical patients received opioids on the day of discharge suggests that a substantial proportion of patients may be discharged with a prescription for opioid medication. Given the possibility of coexistent outpatient opioid prescriptions, these findings draw attention to the importance of assuring development and streamlined accessibility of data from state prescription drug monitoring programs and suggest that increased attention should be paid to the role that inpatient opioid prescribing plays in the increased rates of chronic opioid use and overdose‐related deaths in the United States.

There are additional limitations to our analysis. First, although the database used for this analysis captures a large proportion of admissions to US acute‐care facilities and is similar in composition, it is possible that participating medical centers differ from nonparticipating medical centers in ways that could be associated with opioid prescribing. Additionally, although Premier performs extensive validation and correction processes to assure the quality of their data, there is still likely to be a small amount of random error in the database, which could particularly impact dosage calculations. The lack of preadmission medications in our database precluded identification of the proportion of patients newly started on opioid medications. Lastly, it is possible that the hospital prescribing‐rate quartile is associated with patient characteristics unaccounted for in our analysis, and, therefore, the possibility of residual confounding still exists.

In conclusion, the majority of hospitalized nonsurgical patients are exposed to opioid medications during the course of their hospitalizations, often at high doses. More than half of those exposed are still receiving these medications on the day of discharge. We found hospital and regional variation in opioid use that was not fully explained by patient characteristics, and higher levels of hospital use were associated with higher risk of severe opioid‐related adverse events in opioid‐exposed patients. Further research is necessary to investigate the appropriateness of opioid use in this patient population, the sources of variation in use, and the predictors of opioid‐related adverse events in hospitalized patients to allow development of interventions to make hospital use safer.

Recent reports have drawn attention to the high and increasing rates of opioid prescribing and overdose‐related deaths in the United States.[1, 2, 3, 4, 5, 6, 7, 8, 9] These studies have focused on community‐based and emergency department prescribing, leaving prescribing practices in the inpatient setting unexamined. Given that pain is a frequent complaint in hospitalized patients, and that the Joint Commission mandates assessing pain as a vital sign, hospitalization is potentially a time of heightened use of such medications and could significantly contribute to nosocomial complications and subsequent outpatient use.[10] Variation in prescribing practices, unrelated to patient characteristics, could be a marker of inappropriate prescribing practices and poor quality of care.

Using a large, nationally representative cohort of admissions from July 2009 to June 2010, we sought to determine patterns and predictors of opioid utilization in nonsurgical admissions to US medical centers, hospital variation in use, and the association between hospital‐level use and the risk of opioid‐related adverse events. We hypothesized that hospitals with higher rates of opioid use would have an increased risk of an opioid‐related adverse event per patient exposed.

METHODS