David W. Baker, MD, MPH

Hospital readmissions pose a major problem both to the patient and the fiscal stability of our health care system.1 Many interventions have attempted to tackle this problem. Interventions exist that utilize transition coaches working intensively with hospitalized patients or nurses performing postdischarge home visits or phoning patients.2, 3 Although beneficial, these strategies are costly and require additional, highly trained personnel. Consequently, they have been difficult to sustain financially in a fee‐for‐service environment, and difficult to generalize at other locales. Recent policies to decrease hospital payments for readmissions will incentivize hospitals to implement discharge programs.4 However, all hospital systems will still want to do this in the most efficient manner possible.

One important way to maximize benefits and minimize costs is to target the most intensive, expensive interventions to the highest risk patients who are most likely to be rehospitalized. By targeting the highest risk patients, we could significantly reduce costs. However, models predicting rehospitalization have had limited accuracy, even for condition‐specific models such as heart failure. Two studies in this issue work to better identify high‐risk patients. Mudge and colleagues5 prospectively examined risk factors for recurrent readmissions in an Australian hospital and found that chronic disease, depression, and underweight were independent risk factors for repeat readmission. Allaudeen6 examined risk factors for readmission to their own institution among general medicine patients. In a retrospective analysis of administrative data, they found that several variables predicted hospital readmission, including black race, insurance coverage through Medicaid, prescription of steroids or narcotics, and diagnoses of heart failure, renal disease, cancer, anemia, and weight loss.

These studies raise two questions that are critical if we are to develop better predictive modeling of who will benefit most from intensive interventions to reduce readmissions. First, what are the risk factors for preventable hospitalizations? People with multiple readmissions seem an obvious target on which to focus. However, it may be that these individuals are just very sick with multiple comorbidities, and many of their readmissions may not be preventable. Rich and colleagues reported that a multidisciplinary discharge intervention reduced readmissions for heart failure by 56%.7 What is often forgotten is that in their pilot study they were not able to reduce admissions for the most severely ill, and their final study population excluded the sickest patients. By targeting moderate‐risk patients, they were able to reduce readmissions significantly.8 In the studies by Mudge et al.5 and Allaudeen,6 the fact that chronic diseases predicted rehospitalization is only moderately helpful. It is possible, perhaps likely, that many of the readmissions for heart failure were preventable while many of the readmissions for cancer were not. The challenge for researchers is to develop methods for classifying admissions/readmissions as preventable.9 Using a defined set of diagnostic categories to classify readmissions (eg, ambulatory care sensitive conditions) may misclassify many cases.10 Determining preventable hospitalizations through detailed chart review is expensive and may have limited interobserver reliability. Nevertheless, physician review and classification may be necessary for future research to advance the field.

Second, what predictor variables are causally related to preventable hospitalizations (and presumably actionable), and which are merely markers of true causal factors and therefore harder to interpret and more difficult to act upon? In addition to chronic disease, Mudge et al.5 found that depression and low body mass index were independent risk factors for readmission. These conditions often go hand in hand. Patients who are burdened with chronic disease may be depressed and not eat. Conversely, patients who are depressed may not eat and allow their chronic disease to worsen. But it seems that depression is the more likely of the two to be causal. Depression is an important predictor of medication nonadherence and worsening illness.11, 12 Screening hospitalized patients for depression could provide valuable information on which patients may need treatment or more rigorous postdischarge follow‐up. In contrast, being underweight may not truly cause readmissions, but could be a marker of frailty and difficulty in meeting activities of daily living.

Similarly, Allaudeen6 found that black race, Medicaid use, steroid use, and narcotic use were independently associated with hospital readmission (in addition to chronic diseases and weight loss). Can being on steroids or narcotics cause readmissions? Does enrollment in Medicaid or being of black race cause one to be readmitted? While these may be markers which are statistically significant, they are unlikely to be true causes of rehospitalization. It is more likely that these variables are markers for true causal factors, such as financial barriers to medications or access barriers to primary care. Many other studies have used administrative databases to examine variables linked to readmission. We need to drill deeper to determine what is actually causing readmissions. Did the patient misinterpret how to take their steroid taper or were they so sick that they needed to return to the hospital? Perhaps they decided to wait on taking the steroids until they spoke with their primary care physician. This deeper level of understanding cannot be ascertained through third party administrative data sets. Primary data collection is needed to correctly determine who to target and the specific foci of interventions.

Future research on risk factors for readmissions (and interventions to decrease readmissions) should begin with a theoretical framework that addresses the patient, the hospital, and the receiving outpatient primary care physician or specialist, and the interfaces between each pair that could lead to preventable readmissions (see Figure 1).

Figure 1

With every potential variable affecting readmission, we need to systematically evaluate whether they are causal and preventable. When a variable is both causal and modifiable, we can then develop interventions to target these variables. We designed Table 1 as a framework to consider when moving forward in creating and implementing interventions.

To advance this area, we need to be stringent about how we perform research and interpret findings. Studies that examine risk factors for readmission to a single hospital may be biased; for example, in the study by Allaudeen,6 it is possible that patients with Medicaid may have been equally likely to be readmitted to any hospital but more likely to be readmitted to the hospital that was the sole source of admission data. Even if findings from a single site are valid, they may not be generalizable. Ideally, studies of risk factors (and interventions to reduce readmissions) should be conducted in multiple sites that can track all hospitalizations and examine differences in risk factors for rehospitalization across hospitals. We have learned a tremendous amount over the last few years about risk markers for all‐cause readmission, and interventions to improve safety and quality of transitions in care. To advance further, multicenter studies are needed that focus on plausible causal variables of preventable readmissions and risk factors beyond the walls of the hospital (eg, access and quality of outpatient care for newly discharged patients). Only then will we better understand which patients can have their readmissions prevented and how to improve upon current strategies to improve outcomes.

Hospital readmissions pose a major problem both to the patient and the fiscal stability of our health care system.1 Many interventions have attempted to tackle this problem. Interventions exist that utilize transition coaches working intensively with hospitalized patients or nurses performing postdischarge home visits or phoning patients.2, 3 Although beneficial, these strategies are costly and require additional, highly trained personnel. Consequently, they have been difficult to sustain financially in a fee‐for‐service environment, and difficult to generalize at other locales. Recent policies to decrease hospital payments for readmissions will incentivize hospitals to implement discharge programs.4 However, all hospital systems will still want to do this in the most efficient manner possible.

One important way to maximize benefits and minimize costs is to target the most intensive, expensive interventions to the highest risk patients who are most likely to be rehospitalized. By targeting the highest risk patients, we could significantly reduce costs. However, models predicting rehospitalization have had limited accuracy, even for condition‐specific models such as heart failure. Two studies in this issue work to better identify high‐risk patients. Mudge and colleagues5 prospectively examined risk factors for recurrent readmissions in an Australian hospital and found that chronic disease, depression, and underweight were independent risk factors for repeat readmission. Allaudeen6 examined risk factors for readmission to their own institution among general medicine patients. In a retrospective analysis of administrative data, they found that several variables predicted hospital readmission, including black race, insurance coverage through Medicaid, prescription of steroids or narcotics, and diagnoses of heart failure, renal disease, cancer, anemia, and weight loss.

These studies raise two questions that are critical if we are to develop better predictive modeling of who will benefit most from intensive interventions to reduce readmissions. First, what are the risk factors for preventable hospitalizations? People with multiple readmissions seem an obvious target on which to focus. However, it may be that these individuals are just very sick with multiple comorbidities, and many of their readmissions may not be preventable. Rich and colleagues reported that a multidisciplinary discharge intervention reduced readmissions for heart failure by 56%.7 What is often forgotten is that in their pilot study they were not able to reduce admissions for the most severely ill, and their final study population excluded the sickest patients. By targeting moderate‐risk patients, they were able to reduce readmissions significantly.8 In the studies by Mudge et al.5 and Allaudeen,6 the fact that chronic diseases predicted rehospitalization is only moderately helpful. It is possible, perhaps likely, that many of the readmissions for heart failure were preventable while many of the readmissions for cancer were not. The challenge for researchers is to develop methods for classifying admissions/readmissions as preventable.9 Using a defined set of diagnostic categories to classify readmissions (eg, ambulatory care sensitive conditions) may misclassify many cases.10 Determining preventable hospitalizations through detailed chart review is expensive and may have limited interobserver reliability. Nevertheless, physician review and classification may be necessary for future research to advance the field.

Second, what predictor variables are causally related to preventable hospitalizations (and presumably actionable), and which are merely markers of true causal factors and therefore harder to interpret and more difficult to act upon? In addition to chronic disease, Mudge et al.5 found that depression and low body mass index were independent risk factors for readmission. These conditions often go hand in hand. Patients who are burdened with chronic disease may be depressed and not eat. Conversely, patients who are depressed may not eat and allow their chronic disease to worsen. But it seems that depression is the more likely of the two to be causal. Depression is an important predictor of medication nonadherence and worsening illness.11, 12 Screening hospitalized patients for depression could provide valuable information on which patients may need treatment or more rigorous postdischarge follow‐up. In contrast, being underweight may not truly cause readmissions, but could be a marker of frailty and difficulty in meeting activities of daily living.

Similarly, Allaudeen6 found that black race, Medicaid use, steroid use, and narcotic use were independently associated with hospital readmission (in addition to chronic diseases and weight loss). Can being on steroids or narcotics cause readmissions? Does enrollment in Medicaid or being of black race cause one to be readmitted? While these may be markers which are statistically significant, they are unlikely to be true causes of rehospitalization. It is more likely that these variables are markers for true causal factors, such as financial barriers to medications or access barriers to primary care. Many other studies have used administrative databases to examine variables linked to readmission. We need to drill deeper to determine what is actually causing readmissions. Did the patient misinterpret how to take their steroid taper or were they so sick that they needed to return to the hospital? Perhaps they decided to wait on taking the steroids until they spoke with their primary care physician. This deeper level of understanding cannot be ascertained through third party administrative data sets. Primary data collection is needed to correctly determine who to target and the specific foci of interventions.

Future research on risk factors for readmissions (and interventions to decrease readmissions) should begin with a theoretical framework that addresses the patient, the hospital, and the receiving outpatient primary care physician or specialist, and the interfaces between each pair that could lead to preventable readmissions (see Figure 1).

Figure 1

With every potential variable affecting readmission, we need to systematically evaluate whether they are causal and preventable. When a variable is both causal and modifiable, we can then develop interventions to target these variables. We designed Table 1 as a framework to consider when moving forward in creating and implementing interventions.

To advance this area, we need to be stringent about how we perform research and interpret findings. Studies that examine risk factors for readmission to a single hospital may be biased; for example, in the study by Allaudeen,6 it is possible that patients with Medicaid may have been equally likely to be readmitted to any hospital but more likely to be readmitted to the hospital that was the sole source of admission data. Even if findings from a single site are valid, they may not be generalizable. Ideally, studies of risk factors (and interventions to reduce readmissions) should be conducted in multiple sites that can track all hospitalizations and examine differences in risk factors for rehospitalization across hospitals. We have learned a tremendous amount over the last few years about risk markers for all‐cause readmission, and interventions to improve safety and quality of transitions in care. To advance further, multicenter studies are needed that focus on plausible causal variables of preventable readmissions and risk factors beyond the walls of the hospital (eg, access and quality of outpatient care for newly discharged patients). Only then will we better understand which patients can have their readmissions prevented and how to improve upon current strategies to improve outcomes.

Hospital readmissions pose a major problem both to the patient and the fiscal stability of our health care system.1 Many interventions have attempted to tackle this problem. Interventions exist that utilize transition coaches working intensively with hospitalized patients or nurses performing postdischarge home visits or phoning patients.2, 3 Although beneficial, these strategies are costly and require additional, highly trained personnel. Consequently, they have been difficult to sustain financially in a fee‐for‐service environment, and difficult to generalize at other locales. Recent policies to decrease hospital payments for readmissions will incentivize hospitals to implement discharge programs.4 However, all hospital systems will still want to do this in the most efficient manner possible.

One important way to maximize benefits and minimize costs is to target the most intensive, expensive interventions to the highest risk patients who are most likely to be rehospitalized. By targeting the highest risk patients, we could significantly reduce costs. However, models predicting rehospitalization have had limited accuracy, even for condition‐specific models such as heart failure. Two studies in this issue work to better identify high‐risk patients. Mudge and colleagues5 prospectively examined risk factors for recurrent readmissions in an Australian hospital and found that chronic disease, depression, and underweight were independent risk factors for repeat readmission. Allaudeen6 examined risk factors for readmission to their own institution among general medicine patients. In a retrospective analysis of administrative data, they found that several variables predicted hospital readmission, including black race, insurance coverage through Medicaid, prescription of steroids or narcotics, and diagnoses of heart failure, renal disease, cancer, anemia, and weight loss.

These studies raise two questions that are critical if we are to develop better predictive modeling of who will benefit most from intensive interventions to reduce readmissions. First, what are the risk factors for preventable hospitalizations? People with multiple readmissions seem an obvious target on which to focus. However, it may be that these individuals are just very sick with multiple comorbidities, and many of their readmissions may not be preventable. Rich and colleagues reported that a multidisciplinary discharge intervention reduced readmissions for heart failure by 56%.7 What is often forgotten is that in their pilot study they were not able to reduce admissions for the most severely ill, and their final study population excluded the sickest patients. By targeting moderate‐risk patients, they were able to reduce readmissions significantly.8 In the studies by Mudge et al.5 and Allaudeen,6 the fact that chronic diseases predicted rehospitalization is only moderately helpful. It is possible, perhaps likely, that many of the readmissions for heart failure were preventable while many of the readmissions for cancer were not. The challenge for researchers is to develop methods for classifying admissions/readmissions as preventable.9 Using a defined set of diagnostic categories to classify readmissions (eg, ambulatory care sensitive conditions) may misclassify many cases.10 Determining preventable hospitalizations through detailed chart review is expensive and may have limited interobserver reliability. Nevertheless, physician review and classification may be necessary for future research to advance the field.

Second, what predictor variables are causally related to preventable hospitalizations (and presumably actionable), and which are merely markers of true causal factors and therefore harder to interpret and more difficult to act upon? In addition to chronic disease, Mudge et al.5 found that depression and low body mass index were independent risk factors for readmission. These conditions often go hand in hand. Patients who are burdened with chronic disease may be depressed and not eat. Conversely, patients who are depressed may not eat and allow their chronic disease to worsen. But it seems that depression is the more likely of the two to be causal. Depression is an important predictor of medication nonadherence and worsening illness.11, 12 Screening hospitalized patients for depression could provide valuable information on which patients may need treatment or more rigorous postdischarge follow‐up. In contrast, being underweight may not truly cause readmissions, but could be a marker of frailty and difficulty in meeting activities of daily living.

Similarly, Allaudeen6 found that black race, Medicaid use, steroid use, and narcotic use were independently associated with hospital readmission (in addition to chronic diseases and weight loss). Can being on steroids or narcotics cause readmissions? Does enrollment in Medicaid or being of black race cause one to be readmitted? While these may be markers which are statistically significant, they are unlikely to be true causes of rehospitalization. It is more likely that these variables are markers for true causal factors, such as financial barriers to medications or access barriers to primary care. Many other studies have used administrative databases to examine variables linked to readmission. We need to drill deeper to determine what is actually causing readmissions. Did the patient misinterpret how to take their steroid taper or were they so sick that they needed to return to the hospital? Perhaps they decided to wait on taking the steroids until they spoke with their primary care physician. This deeper level of understanding cannot be ascertained through third party administrative data sets. Primary data collection is needed to correctly determine who to target and the specific foci of interventions.

Future research on risk factors for readmissions (and interventions to decrease readmissions) should begin with a theoretical framework that addresses the patient, the hospital, and the receiving outpatient primary care physician or specialist, and the interfaces between each pair that could lead to preventable readmissions (see Figure 1).

Figure 1

With every potential variable affecting readmission, we need to systematically evaluate whether they are causal and preventable. When a variable is both causal and modifiable, we can then develop interventions to target these variables. We designed Table 1 as a framework to consider when moving forward in creating and implementing interventions.

To advance this area, we need to be stringent about how we perform research and interpret findings. Studies that examine risk factors for readmission to a single hospital may be biased; for example, in the study by Allaudeen,6 it is possible that patients with Medicaid may have been equally likely to be readmitted to any hospital but more likely to be readmitted to the hospital that was the sole source of admission data. Even if findings from a single site are valid, they may not be generalizable. Ideally, studies of risk factors (and interventions to reduce readmissions) should be conducted in multiple sites that can track all hospitalizations and examine differences in risk factors for rehospitalization across hospitals. We have learned a tremendous amount over the last few years about risk markers for all‐cause readmission, and interventions to improve safety and quality of transitions in care. To advance further, multicenter studies are needed that focus on plausible causal variables of preventable readmissions and risk factors beyond the walls of the hospital (eg, access and quality of outpatient care for newly discharged patients). Only then will we better understand which patients can have their readmissions prevented and how to improve upon current strategies to improve outcomes.

Preventable or ameliorable adverse events have been reported to occur in 12% of patients in the period immediately following hospital discharge.1, 2 A potential contributor to this is the inadequate transfer of clinical information at hospital discharge. The discharge summary comprises a vital component of the information transfer between the inpatient and outpatient settings. Unfortunately, discharge summaries are often unavailable at the time of follow‐up care and often lack important content.37

A growing number of hospitals are implementing electronic medical records (EMR). This creates the opportunity to standardize the content of clinical documentation and creates the potential to assemble, immediately at the time of hospital discharge, major components of a discharge summary. With enhanced communication systems, this information can be delivered in a variety of ways with minimal delay. Previously, we reported the results of a survey of medicine faculty at an urban academic medical center evaluating the timeliness and quality of discharge summaries, the perceived incidence of preventable adverse events related to suboptimal information transfer at discharge, and a needs assessment for an electronically generated discharge summary that we planned to design.8 We now report the results of the follow‐up survey of outpatient physicians and an evaluation of the quality and timeliness of the electronic discharge summary we created.

Materials and Methods

Results

Discussion

Our study found that an electronic discharge summary was well accepted by inpatient physicians and significantly improved the quality and timeliness of discharge summaries. Prior studies have shown that the use of electronically entered discharge summaries improved the timeliness of discharge summaries.1416 However, the discharge summaries used in these studies required manual input of data into a computer system separate from the patient's medical record. To our knowledge, this is the first study to report the impact of discharge summaries generated from an EMR. Leveraging the EMR, we were able to automate the insertion of specific patient data elements, streamline delivery, and create an electronic reminder system to inpatient physicians for summaries not completed 24 hours after discharge.

Prior research has shown that the quality of discharges summaries is improved with the use of standardized content.10, 17 Using a standardized template for the electronic discharge summary, we likewise demonstrated improved quality of discharge summaries. Key discharge summary elements, specifically discussion of follow‐up issues, pending test results, and information provided to the patient and/or family, were present more reliably after the implementation of the electronic discharge summary. The importance of identifying pending test results is underscored by a recent study showing that many patients are discharged from hospitals with test results still pending, and that physicians are often unaware when results are abnormal.18 One discharge summary element, significant laboratory findings, was present less often after the implementation of the electronic discharge summary. Our template did not designate significant laboratory findings under a separate heading. Instead, we used a heading entitled Key Results (labs, imaging, pathology). Physicians completing the discharge summaries may have prioritized the report of imaging and pathology results in this section. A simple revision of our discharge summary template to include a separate heading for significant laboratory findings may result in improvement in this regard.

Timeliness of discharge summaries was improved in our study, but remained less than optimal. Although nearly three‐quarters of electronic discharge summaries were completed within 3 days of discharge, our ultimate goal is to have 100% of discharge summaries completed within 3 days. This is especially important for complicated patients requiring outpatient follow‐up soon after discharge. We are currently in the process of designing further modifications to the electronic discharge summary completion process. One modification that may be beneficial is the automation of additional patient specific data elements into the discharge summary. We also plan to link performance of medication reconciliation, completion of patient discharge instructions, and completion of the discharge summary into an integrated set of activities performed in the EMR prior to patient discharge.

We found that fewer outpatient physicians reported 1 or more of their patients having a preventable adverse event or near miss as a result of suboptimal transfer of information at discharge after the implementation of the electronic discharge summary. Although we did not measure preventable adverse events directly in our study, this is an important finding in light of the large number of patients who sustain preventable adverse events after hospital discharge1, 2 and prior research showing that the absence of discharge summaries at postdischarge follow‐up visits increased the risk for hospital readmission.19

We had wondered what effect the electronic discharge summary would have on the length and clarity of discharge summaries. A published commentary suggested that notes performed in EMRs were inordinately long and often difficult to read.20 We were pleased to discover that electronic discharge summaries were similar in length to previous discharge summaries and were rated higher with regard to clarity.

Our study has several limitations. First, many inpatient physicians began to use electronic discharge summaries prior to our creation of the final electronic discharge summary product. We had explicitly instructed physicians to continue to dictate discharge summaries in the first phase of our study. The fact that physicians quickly adopted the practice of completing discharge summaries electronically suggests that they preferred this method for completion and may help to explain the improvement in timeliness. A second limitation, as previously mentioned, is that our study did not measure adverse events directly. Instead, we asked outpatient physicians to estimate the number of their patients discharged in the last 6 months who had sustained a preventable adverse event or near miss related to suboptimal information transfer at discharge. We had limited space in the survey to define the meaning of a preventable adverse event; therefore, the description in the survey does not exactly match previous definitions.1, 2 Finally, the ordinal scale used to assess clarity of discharge summaries has not been previously validated.

In conclusion, the use of an electronic discharge summary significantly improved the quality and timeliness of discharge summaries. The discharge summary comprises a vital component of the information transfer between the inpatient and outpatient settings during the vulnerable period following hospital discharge. As hospitals expand their use of EMRs, they should take advantage of opportunities to leverage functionality to improve quality and timeliness of discharge summaries.

Preventable or ameliorable adverse events have been reported to occur in 12% of patients in the period immediately following hospital discharge.1, 2 A potential contributor to this is the inadequate transfer of clinical information at hospital discharge. The discharge summary comprises a vital component of the information transfer between the inpatient and outpatient settings. Unfortunately, discharge summaries are often unavailable at the time of follow‐up care and often lack important content.37

A growing number of hospitals are implementing electronic medical records (EMR). This creates the opportunity to standardize the content of clinical documentation and creates the potential to assemble, immediately at the time of hospital discharge, major components of a discharge summary. With enhanced communication systems, this information can be delivered in a variety of ways with minimal delay. Previously, we reported the results of a survey of medicine faculty at an urban academic medical center evaluating the timeliness and quality of discharge summaries, the perceived incidence of preventable adverse events related to suboptimal information transfer at discharge, and a needs assessment for an electronically generated discharge summary that we planned to design.8 We now report the results of the follow‐up survey of outpatient physicians and an evaluation of the quality and timeliness of the electronic discharge summary we created.

Materials and Methods

Results

Discussion

Our study found that an electronic discharge summary was well accepted by inpatient physicians and significantly improved the quality and timeliness of discharge summaries. Prior studies have shown that the use of electronically entered discharge summaries improved the timeliness of discharge summaries.1416 However, the discharge summaries used in these studies required manual input of data into a computer system separate from the patient's medical record. To our knowledge, this is the first study to report the impact of discharge summaries generated from an EMR. Leveraging the EMR, we were able to automate the insertion of specific patient data elements, streamline delivery, and create an electronic reminder system to inpatient physicians for summaries not completed 24 hours after discharge.

Prior research has shown that the quality of discharges summaries is improved with the use of standardized content.10, 17 Using a standardized template for the electronic discharge summary, we likewise demonstrated improved quality of discharge summaries. Key discharge summary elements, specifically discussion of follow‐up issues, pending test results, and information provided to the patient and/or family, were present more reliably after the implementation of the electronic discharge summary. The importance of identifying pending test results is underscored by a recent study showing that many patients are discharged from hospitals with test results still pending, and that physicians are often unaware when results are abnormal.18 One discharge summary element, significant laboratory findings, was present less often after the implementation of the electronic discharge summary. Our template did not designate significant laboratory findings under a separate heading. Instead, we used a heading entitled Key Results (labs, imaging, pathology). Physicians completing the discharge summaries may have prioritized the report of imaging and pathology results in this section. A simple revision of our discharge summary template to include a separate heading for significant laboratory findings may result in improvement in this regard.

Timeliness of discharge summaries was improved in our study, but remained less than optimal. Although nearly three‐quarters of electronic discharge summaries were completed within 3 days of discharge, our ultimate goal is to have 100% of discharge summaries completed within 3 days. This is especially important for complicated patients requiring outpatient follow‐up soon after discharge. We are currently in the process of designing further modifications to the electronic discharge summary completion process. One modification that may be beneficial is the automation of additional patient specific data elements into the discharge summary. We also plan to link performance of medication reconciliation, completion of patient discharge instructions, and completion of the discharge summary into an integrated set of activities performed in the EMR prior to patient discharge.

We found that fewer outpatient physicians reported 1 or more of their patients having a preventable adverse event or near miss as a result of suboptimal transfer of information at discharge after the implementation of the electronic discharge summary. Although we did not measure preventable adverse events directly in our study, this is an important finding in light of the large number of patients who sustain preventable adverse events after hospital discharge1, 2 and prior research showing that the absence of discharge summaries at postdischarge follow‐up visits increased the risk for hospital readmission.19

We had wondered what effect the electronic discharge summary would have on the length and clarity of discharge summaries. A published commentary suggested that notes performed in EMRs were inordinately long and often difficult to read.20 We were pleased to discover that electronic discharge summaries were similar in length to previous discharge summaries and were rated higher with regard to clarity.

Our study has several limitations. First, many inpatient physicians began to use electronic discharge summaries prior to our creation of the final electronic discharge summary product. We had explicitly instructed physicians to continue to dictate discharge summaries in the first phase of our study. The fact that physicians quickly adopted the practice of completing discharge summaries electronically suggests that they preferred this method for completion and may help to explain the improvement in timeliness. A second limitation, as previously mentioned, is that our study did not measure adverse events directly. Instead, we asked outpatient physicians to estimate the number of their patients discharged in the last 6 months who had sustained a preventable adverse event or near miss related to suboptimal information transfer at discharge. We had limited space in the survey to define the meaning of a preventable adverse event; therefore, the description in the survey does not exactly match previous definitions.1, 2 Finally, the ordinal scale used to assess clarity of discharge summaries has not been previously validated.

In conclusion, the use of an electronic discharge summary significantly improved the quality and timeliness of discharge summaries. The discharge summary comprises a vital component of the information transfer between the inpatient and outpatient settings during the vulnerable period following hospital discharge. As hospitals expand their use of EMRs, they should take advantage of opportunities to leverage functionality to improve quality and timeliness of discharge summaries.

Preventable or ameliorable adverse events have been reported to occur in 12% of patients in the period immediately following hospital discharge.1, 2 A potential contributor to this is the inadequate transfer of clinical information at hospital discharge. The discharge summary comprises a vital component of the information transfer between the inpatient and outpatient settings. Unfortunately, discharge summaries are often unavailable at the time of follow‐up care and often lack important content.37

A growing number of hospitals are implementing electronic medical records (EMR). This creates the opportunity to standardize the content of clinical documentation and creates the potential to assemble, immediately at the time of hospital discharge, major components of a discharge summary. With enhanced communication systems, this information can be delivered in a variety of ways with minimal delay. Previously, we reported the results of a survey of medicine faculty at an urban academic medical center evaluating the timeliness and quality of discharge summaries, the perceived incidence of preventable adverse events related to suboptimal information transfer at discharge, and a needs assessment for an electronically generated discharge summary that we planned to design.8 We now report the results of the follow‐up survey of outpatient physicians and an evaluation of the quality and timeliness of the electronic discharge summary we created.

Materials and Methods

Results

Discussion

Our study found that an electronic discharge summary was well accepted by inpatient physicians and significantly improved the quality and timeliness of discharge summaries. Prior studies have shown that the use of electronically entered discharge summaries improved the timeliness of discharge summaries.1416 However, the discharge summaries used in these studies required manual input of data into a computer system separate from the patient's medical record. To our knowledge, this is the first study to report the impact of discharge summaries generated from an EMR. Leveraging the EMR, we were able to automate the insertion of specific patient data elements, streamline delivery, and create an electronic reminder system to inpatient physicians for summaries not completed 24 hours after discharge.

Prior research has shown that the quality of discharges summaries is improved with the use of standardized content.10, 17 Using a standardized template for the electronic discharge summary, we likewise demonstrated improved quality of discharge summaries. Key discharge summary elements, specifically discussion of follow‐up issues, pending test results, and information provided to the patient and/or family, were present more reliably after the implementation of the electronic discharge summary. The importance of identifying pending test results is underscored by a recent study showing that many patients are discharged from hospitals with test results still pending, and that physicians are often unaware when results are abnormal.18 One discharge summary element, significant laboratory findings, was present less often after the implementation of the electronic discharge summary. Our template did not designate significant laboratory findings under a separate heading. Instead, we used a heading entitled Key Results (labs, imaging, pathology). Physicians completing the discharge summaries may have prioritized the report of imaging and pathology results in this section. A simple revision of our discharge summary template to include a separate heading for significant laboratory findings may result in improvement in this regard.

Timeliness of discharge summaries was improved in our study, but remained less than optimal. Although nearly three‐quarters of electronic discharge summaries were completed within 3 days of discharge, our ultimate goal is to have 100% of discharge summaries completed within 3 days. This is especially important for complicated patients requiring outpatient follow‐up soon after discharge. We are currently in the process of designing further modifications to the electronic discharge summary completion process. One modification that may be beneficial is the automation of additional patient specific data elements into the discharge summary. We also plan to link performance of medication reconciliation, completion of patient discharge instructions, and completion of the discharge summary into an integrated set of activities performed in the EMR prior to patient discharge.

We found that fewer outpatient physicians reported 1 or more of their patients having a preventable adverse event or near miss as a result of suboptimal transfer of information at discharge after the implementation of the electronic discharge summary. Although we did not measure preventable adverse events directly in our study, this is an important finding in light of the large number of patients who sustain preventable adverse events after hospital discharge1, 2 and prior research showing that the absence of discharge summaries at postdischarge follow‐up visits increased the risk for hospital readmission.19

We had wondered what effect the electronic discharge summary would have on the length and clarity of discharge summaries. A published commentary suggested that notes performed in EMRs were inordinately long and often difficult to read.20 We were pleased to discover that electronic discharge summaries were similar in length to previous discharge summaries and were rated higher with regard to clarity.

Our study has several limitations. First, many inpatient physicians began to use electronic discharge summaries prior to our creation of the final electronic discharge summary product. We had explicitly instructed physicians to continue to dictate discharge summaries in the first phase of our study. The fact that physicians quickly adopted the practice of completing discharge summaries electronically suggests that they preferred this method for completion and may help to explain the improvement in timeliness. A second limitation, as previously mentioned, is that our study did not measure adverse events directly. Instead, we asked outpatient physicians to estimate the number of their patients discharged in the last 6 months who had sustained a preventable adverse event or near miss related to suboptimal information transfer at discharge. We had limited space in the survey to define the meaning of a preventable adverse event; therefore, the description in the survey does not exactly match previous definitions.1, 2 Finally, the ordinal scale used to assess clarity of discharge summaries has not been previously validated.

In conclusion, the use of an electronic discharge summary significantly improved the quality and timeliness of discharge summaries. The discharge summary comprises a vital component of the information transfer between the inpatient and outpatient settings during the vulnerable period following hospital discharge. As hospitals expand their use of EMRs, they should take advantage of opportunities to leverage functionality to improve quality and timeliness of discharge summaries.

Many hospitalists work with clinical coordinators and case managers.13 The descriptions of these roles often overlap4 and commonly include activities such as obtaining medical records, expediting tests and procedures, coordinating the plan of care with other health care providers, assessing postdischarge needs, completing discharge paperwork, and arranging follow‐up visits.2, 5, 6 Despite the potential to improve patient care and hospital efficiency, few studies have formally evaluated the impact of these roles. Moher et al. found that adding a clinical coordinator to a general medical team decreased length of stay (LOS) and improved patient satisfaction.5 However, this study was conducted at a time when the LOS was routinely longer than it is today. Forster et al. found that adding a clinical coordinator to a general medical team resulted in improved patient satisfaction but did not reduce length of stay or risk of adverse events occurring following hospital discharge.6 Both these studies evaluated the impact of adding a clinical coordinator to resident‐covered medical teams. Yet many hospitalists deliver care without residents, limiting the generalizability of the findings from these studies.

To date, no studies have evaluated the impact of clinical coordinators, case managers, or other nonphysician providers on the hospitalist work experience. This is surprising, as hospital medicine group leaders list daily workload and work hours among their top concerns.7 Clinical coordinators have the potential to improve patient care and hospital efficiency while simultaneously improving the experience of the hospitalists with whom they work. We conducted this study to evaluate the impact of a hospitalistcare coordinator team on hospitalist work experience, patient satisfaction, and hospital efficiency.

METHODS

RESULTS

There were 356 patients cared for by hospitalistHCC teams and 337 patients cared for by control hospitalists. Of the 60 weeks of hospitalist service of the study, hospitalistHCC teams accounted for 31 weeks (52%) and control hospitalists for 29 weeks (48%). Patients cared for by the hospitalistHCC teams were similar in age, sex, ethnicity, payer type, and DRG weight to those cared for by control hospitalists (see Table 2).

Sixty surveys were completed by hospitalists at the end of their week on service (response rate 100%). Of the 31 responses from hospitalists completing a hospitalistHCC team week, 28 (90%) reported that working with an HCC improved their efficiency and 28 (90%) that working with an HCC improved their job satisfaction. The hospitalists indicated that working with an HCC significantly improved the efficiency of most of their activities (see Table 3). Specifically, activities related to communication with nurses and patients and activities involving discharge planning and execution were improved with the use of an HCC. As would be expected, certain other activities did not improve. For example, there were no differences between the groups in the perceived efficiency of performing histories and physicals or placing admission orders. For activities that were significantly different, the Wilcoxon rank sum test and linear regression analysis adjusting for physician clustering showed identical results.

Seventy‐one of 196 eligible patients (36%) completed the discharge satisfaction interview. Of the 71 patients interviewed, 44 (62%) were cared for by hospitalistHCC teams and 27 (38%) were cared for by control hospitalists. Patient satisfaction with the clarity of the verbal and written discharge instructions and overall satisfaction with hospital discharge was similar between the 2 groups (see Table 4).

The unadjusted mean LOS for patients cared for by hospitalistHCC teams was 4.70 4.15 days compared with 5.07 3.99 days for patients cared for by control hospitalists (P = .005; see Table 5). The unadjusted mean cost for patients cared for by hospitalistHCC teams was $10,052.96 $11,708.73 compared with $11,703.19 $20,455.78 for patients cared for by control hospitalists (P = .008). In multivariate analysis using age, sex, ethnicity, payer type, and DRG weight as independent variables and adjusting for physician clustering, LOS remained lower for patients cared for by hospitalistHCC teams; however, this result was not statistically significant (0.28 days, P = .17). Similar multivariate regression analysis showed a trend toward lower cost for patients cared for by the hospitalistHCC teams (585.62, P = .15).

DISCUSSION

Our study found that hospitalists working in a team approach with an HCC rated the efficiency of their daily work and their job satisfaction significantly higher than did control hospitalists. Specific areas of improved efficiency included communication activities and activities related to hospital discharge. A prior study conducted by our group found that hospitalists spend a lot of time on indirect patient care activities such as communication and activities related to the discharge process, while spending relatively little time on direct patient care.8 Improving the efficiency of indirect patient care activities of hospitalists is likely to improve their job satisfaction. The importance of improving hospitalist workload and job satisfaction is underscored by the relatively high number of hospitalists at risk for burnout9 and the growing concern about daily workload among hospital medicine group leaders.7

Patient satisfaction was not significantly affected by the use of the hospitalistHCC team in our study. A priori, we postulated that patient satisfaction with the discharge process might improve with the use of the hospitalistHCC team. We therefore limited survey questions to assessing only satisfaction with hospital discharge rather than other aspects of patient hospital care. A recent study reported that patients rated the quality of discharge instructions significantly lower than they rated the overall quality of their hospital stay.10 However, the patients in our study gave high ratings to both discharge instructions and overall satisfaction with hospital discharge. This may explain why we were unable to detect a difference. Our study was limited by the relatively small number of patients we were able to contact to assess satisfaction. Previous studies evaluating the impact of care coordinators either did not assess patient satisfaction with discharge5 or found no difference in satisfaction with hospital discharge.6

Although our study did not find a difference in patient satisfaction with the discharge process, we believe the hospitalistHCC model has the potential to complement efforts to reduce the risk of adverse events as patients transition out of the hospital. It has been reported that 12% of patients have a preventable or ameliorable adverse event in the period immediately following hospital discharge.11, 12 Although Forster et al. did not find a reduction in the risk of adverse events with the addition of a clinical coordinator to a general medical team, they noted incongruence between the coordinator's role and the outcomes measured.6 Similarly, we would need to modify the role of the HCC from a position designed mainly to improve efficiency to one that complements efforts to improve the quality of the discharge process. Possible ways to enhance the HCC role in this regard include increasing the emphasis on and training in patient education skills. Several recently published articles have emphasized the need to redesign the discharge process in an effort to reduce the risk of adverse events following hospital discharge.1315 A modified HCC role might be an essential feature of a redesigned multidisciplinary discharge process.

We were unable to demonstrate improved efficiency for the hospital. Although LOS and cost were lower for patients cared for by the hospitalistHCC teams, the difference was not statistically significant. One possible explanation for why we did not observe a larger reduction in LOS is that our hospitalist service had a lower‐than‐average patient volume during the study period. The lower volume mirrored an unanticipated dip in hospital volume during the same period. Specifically, our service normally discharges an average of 338 patients per month, but during the study period we discharged an average of 235 patients per month. A potential LOS and cost benefit may have been attenuated by the relatively low volume, as hospitalists had ample time to dedicate to communication and coordination of discharge plans.

Our study had several limitations. It was conducted on a nonteaching hospitalist service at a single site. Hospitalist practices vary widely in their staffing and scheduling models. As previously mentioned, we were only able to perform patient satisfaction surveys during the second half of the study period. In addition, hospitalistHCC team patients made up a larger percentage of the patient survey responses (62%) than did control hospitalist patients (38%). This may have affected our ability to detect differences in satisfaction with the hospital discharge process. As also previously noted, our patient volume was lower than normal during the study period. We believe that a higher volume would have magnified differences in hospitalists' perceived efficiency and perhaps resulted in significant improvements in LOS and cost. Finally, the hospital provided funding for only a 12‐week study. This limited our sample size and the power of the study to detect important differences. It is possible that a larger sample size and/or longer study period may have been able to demonstrate a statistically significant improvement in LOS and cost.

Our findings are of particular importance in light of the persistent concerns about hospitalist workload and job satisfaction. Although many hospitalists work with clinical coordinators and case managers, we believe that having the formal structure of a hospitalistcare coordinator team was the key element to improving hospitalist efficiency and satisfaction. We hope that our study is a precursor to research evaluating models of delivering hospital care and their impact on hospitalist work experience, hospital efficiency, and patient outcomes.

Many hospitalists work with clinical coordinators and case managers.13 The descriptions of these roles often overlap4 and commonly include activities such as obtaining medical records, expediting tests and procedures, coordinating the plan of care with other health care providers, assessing postdischarge needs, completing discharge paperwork, and arranging follow‐up visits.2, 5, 6 Despite the potential to improve patient care and hospital efficiency, few studies have formally evaluated the impact of these roles. Moher et al. found that adding a clinical coordinator to a general medical team decreased length of stay (LOS) and improved patient satisfaction.5 However, this study was conducted at a time when the LOS was routinely longer than it is today. Forster et al. found that adding a clinical coordinator to a general medical team resulted in improved patient satisfaction but did not reduce length of stay or risk of adverse events occurring following hospital discharge.6 Both these studies evaluated the impact of adding a clinical coordinator to resident‐covered medical teams. Yet many hospitalists deliver care without residents, limiting the generalizability of the findings from these studies.

To date, no studies have evaluated the impact of clinical coordinators, case managers, or other nonphysician providers on the hospitalist work experience. This is surprising, as hospital medicine group leaders list daily workload and work hours among their top concerns.7 Clinical coordinators have the potential to improve patient care and hospital efficiency while simultaneously improving the experience of the hospitalists with whom they work. We conducted this study to evaluate the impact of a hospitalistcare coordinator team on hospitalist work experience, patient satisfaction, and hospital efficiency.

METHODS

RESULTS

There were 356 patients cared for by hospitalistHCC teams and 337 patients cared for by control hospitalists. Of the 60 weeks of hospitalist service of the study, hospitalistHCC teams accounted for 31 weeks (52%) and control hospitalists for 29 weeks (48%). Patients cared for by the hospitalistHCC teams were similar in age, sex, ethnicity, payer type, and DRG weight to those cared for by control hospitalists (see Table 2).

Sixty surveys were completed by hospitalists at the end of their week on service (response rate 100%). Of the 31 responses from hospitalists completing a hospitalistHCC team week, 28 (90%) reported that working with an HCC improved their efficiency and 28 (90%) that working with an HCC improved their job satisfaction. The hospitalists indicated that working with an HCC significantly improved the efficiency of most of their activities (see Table 3). Specifically, activities related to communication with nurses and patients and activities involving discharge planning and execution were improved with the use of an HCC. As would be expected, certain other activities did not improve. For example, there were no differences between the groups in the perceived efficiency of performing histories and physicals or placing admission orders. For activities that were significantly different, the Wilcoxon rank sum test and linear regression analysis adjusting for physician clustering showed identical results.

Seventy‐one of 196 eligible patients (36%) completed the discharge satisfaction interview. Of the 71 patients interviewed, 44 (62%) were cared for by hospitalistHCC teams and 27 (38%) were cared for by control hospitalists. Patient satisfaction with the clarity of the verbal and written discharge instructions and overall satisfaction with hospital discharge was similar between the 2 groups (see Table 4).

The unadjusted mean LOS for patients cared for by hospitalistHCC teams was 4.70 4.15 days compared with 5.07 3.99 days for patients cared for by control hospitalists (P = .005; see Table 5). The unadjusted mean cost for patients cared for by hospitalistHCC teams was $10,052.96 $11,708.73 compared with $11,703.19 $20,455.78 for patients cared for by control hospitalists (P = .008). In multivariate analysis using age, sex, ethnicity, payer type, and DRG weight as independent variables and adjusting for physician clustering, LOS remained lower for patients cared for by hospitalistHCC teams; however, this result was not statistically significant (0.28 days, P = .17). Similar multivariate regression analysis showed a trend toward lower cost for patients cared for by the hospitalistHCC teams (585.62, P = .15).

DISCUSSION

Our study found that hospitalists working in a team approach with an HCC rated the efficiency of their daily work and their job satisfaction significantly higher than did control hospitalists. Specific areas of improved efficiency included communication activities and activities related to hospital discharge. A prior study conducted by our group found that hospitalists spend a lot of time on indirect patient care activities such as communication and activities related to the discharge process, while spending relatively little time on direct patient care.8 Improving the efficiency of indirect patient care activities of hospitalists is likely to improve their job satisfaction. The importance of improving hospitalist workload and job satisfaction is underscored by the relatively high number of hospitalists at risk for burnout9 and the growing concern about daily workload among hospital medicine group leaders.7

Patient satisfaction was not significantly affected by the use of the hospitalistHCC team in our study. A priori, we postulated that patient satisfaction with the discharge process might improve with the use of the hospitalistHCC team. We therefore limited survey questions to assessing only satisfaction with hospital discharge rather than other aspects of patient hospital care. A recent study reported that patients rated the quality of discharge instructions significantly lower than they rated the overall quality of their hospital stay.10 However, the patients in our study gave high ratings to both discharge instructions and overall satisfaction with hospital discharge. This may explain why we were unable to detect a difference. Our study was limited by the relatively small number of patients we were able to contact to assess satisfaction. Previous studies evaluating the impact of care coordinators either did not assess patient satisfaction with discharge5 or found no difference in satisfaction with hospital discharge.6

Although our study did not find a difference in patient satisfaction with the discharge process, we believe the hospitalistHCC model has the potential to complement efforts to reduce the risk of adverse events as patients transition out of the hospital. It has been reported that 12% of patients have a preventable or ameliorable adverse event in the period immediately following hospital discharge.11, 12 Although Forster et al. did not find a reduction in the risk of adverse events with the addition of a clinical coordinator to a general medical team, they noted incongruence between the coordinator's role and the outcomes measured.6 Similarly, we would need to modify the role of the HCC from a position designed mainly to improve efficiency to one that complements efforts to improve the quality of the discharge process. Possible ways to enhance the HCC role in this regard include increasing the emphasis on and training in patient education skills. Several recently published articles have emphasized the need to redesign the discharge process in an effort to reduce the risk of adverse events following hospital discharge.1315 A modified HCC role might be an essential feature of a redesigned multidisciplinary discharge process.

We were unable to demonstrate improved efficiency for the hospital. Although LOS and cost were lower for patients cared for by the hospitalistHCC teams, the difference was not statistically significant. One possible explanation for why we did not observe a larger reduction in LOS is that our hospitalist service had a lower‐than‐average patient volume during the study period. The lower volume mirrored an unanticipated dip in hospital volume during the same period. Specifically, our service normally discharges an average of 338 patients per month, but during the study period we discharged an average of 235 patients per month. A potential LOS and cost benefit may have been attenuated by the relatively low volume, as hospitalists had ample time to dedicate to communication and coordination of discharge plans.

Our study had several limitations. It was conducted on a nonteaching hospitalist service at a single site. Hospitalist practices vary widely in their staffing and scheduling models. As previously mentioned, we were only able to perform patient satisfaction surveys during the second half of the study period. In addition, hospitalistHCC team patients made up a larger percentage of the patient survey responses (62%) than did control hospitalist patients (38%). This may have affected our ability to detect differences in satisfaction with the hospital discharge process. As also previously noted, our patient volume was lower than normal during the study period. We believe that a higher volume would have magnified differences in hospitalists' perceived efficiency and perhaps resulted in significant improvements in LOS and cost. Finally, the hospital provided funding for only a 12‐week study. This limited our sample size and the power of the study to detect important differences. It is possible that a larger sample size and/or longer study period may have been able to demonstrate a statistically significant improvement in LOS and cost.

Our findings are of particular importance in light of the persistent concerns about hospitalist workload and job satisfaction. Although many hospitalists work with clinical coordinators and case managers, we believe that having the formal structure of a hospitalistcare coordinator team was the key element to improving hospitalist efficiency and satisfaction. We hope that our study is a precursor to research evaluating models of delivering hospital care and their impact on hospitalist work experience, hospital efficiency, and patient outcomes.

Many hospitalists work with clinical coordinators and case managers.13 The descriptions of these roles often overlap4 and commonly include activities such as obtaining medical records, expediting tests and procedures, coordinating the plan of care with other health care providers, assessing postdischarge needs, completing discharge paperwork, and arranging follow‐up visits.2, 5, 6 Despite the potential to improve patient care and hospital efficiency, few studies have formally evaluated the impact of these roles. Moher et al. found that adding a clinical coordinator to a general medical team decreased length of stay (LOS) and improved patient satisfaction.5 However, this study was conducted at a time when the LOS was routinely longer than it is today. Forster et al. found that adding a clinical coordinator to a general medical team resulted in improved patient satisfaction but did not reduce length of stay or risk of adverse events occurring following hospital discharge.6 Both these studies evaluated the impact of adding a clinical coordinator to resident‐covered medical teams. Yet many hospitalists deliver care without residents, limiting the generalizability of the findings from these studies.

To date, no studies have evaluated the impact of clinical coordinators, case managers, or other nonphysician providers on the hospitalist work experience. This is surprising, as hospital medicine group leaders list daily workload and work hours among their top concerns.7 Clinical coordinators have the potential to improve patient care and hospital efficiency while simultaneously improving the experience of the hospitalists with whom they work. We conducted this study to evaluate the impact of a hospitalistcare coordinator team on hospitalist work experience, patient satisfaction, and hospital efficiency.

METHODS

RESULTS

There were 356 patients cared for by hospitalistHCC teams and 337 patients cared for by control hospitalists. Of the 60 weeks of hospitalist service of the study, hospitalistHCC teams accounted for 31 weeks (52%) and control hospitalists for 29 weeks (48%). Patients cared for by the hospitalistHCC teams were similar in age, sex, ethnicity, payer type, and DRG weight to those cared for by control hospitalists (see Table 2).

Sixty surveys were completed by hospitalists at the end of their week on service (response rate 100%). Of the 31 responses from hospitalists completing a hospitalistHCC team week, 28 (90%) reported that working with an HCC improved their efficiency and 28 (90%) that working with an HCC improved their job satisfaction. The hospitalists indicated that working with an HCC significantly improved the efficiency of most of their activities (see Table 3). Specifically, activities related to communication with nurses and patients and activities involving discharge planning and execution were improved with the use of an HCC. As would be expected, certain other activities did not improve. For example, there were no differences between the groups in the perceived efficiency of performing histories and physicals or placing admission orders. For activities that were significantly different, the Wilcoxon rank sum test and linear regression analysis adjusting for physician clustering showed identical results.

Seventy‐one of 196 eligible patients (36%) completed the discharge satisfaction interview. Of the 71 patients interviewed, 44 (62%) were cared for by hospitalistHCC teams and 27 (38%) were cared for by control hospitalists. Patient satisfaction with the clarity of the verbal and written discharge instructions and overall satisfaction with hospital discharge was similar between the 2 groups (see Table 4).

The unadjusted mean LOS for patients cared for by hospitalistHCC teams was 4.70 4.15 days compared with 5.07 3.99 days for patients cared for by control hospitalists (P = .005; see Table 5). The unadjusted mean cost for patients cared for by hospitalistHCC teams was $10,052.96 $11,708.73 compared with $11,703.19 $20,455.78 for patients cared for by control hospitalists (P = .008). In multivariate analysis using age, sex, ethnicity, payer type, and DRG weight as independent variables and adjusting for physician clustering, LOS remained lower for patients cared for by hospitalistHCC teams; however, this result was not statistically significant (0.28 days, P = .17). Similar multivariate regression analysis showed a trend toward lower cost for patients cared for by the hospitalistHCC teams (585.62, P = .15).

DISCUSSION

Our study found that hospitalists working in a team approach with an HCC rated the efficiency of their daily work and their job satisfaction significantly higher than did control hospitalists. Specific areas of improved efficiency included communication activities and activities related to hospital discharge. A prior study conducted by our group found that hospitalists spend a lot of time on indirect patient care activities such as communication and activities related to the discharge process, while spending relatively little time on direct patient care.8 Improving the efficiency of indirect patient care activities of hospitalists is likely to improve their job satisfaction. The importance of improving hospitalist workload and job satisfaction is underscored by the relatively high number of hospitalists at risk for burnout9 and the growing concern about daily workload among hospital medicine group leaders.7

Patient satisfaction was not significantly affected by the use of the hospitalistHCC team in our study. A priori, we postulated that patient satisfaction with the discharge process might improve with the use of the hospitalistHCC team. We therefore limited survey questions to assessing only satisfaction with hospital discharge rather than other aspects of patient hospital care. A recent study reported that patients rated the quality of discharge instructions significantly lower than they rated the overall quality of their hospital stay.10 However, the patients in our study gave high ratings to both discharge instructions and overall satisfaction with hospital discharge. This may explain why we were unable to detect a difference. Our study was limited by the relatively small number of patients we were able to contact to assess satisfaction. Previous studies evaluating the impact of care coordinators either did not assess patient satisfaction with discharge5 or found no difference in satisfaction with hospital discharge.6

Although our study did not find a difference in patient satisfaction with the discharge process, we believe the hospitalistHCC model has the potential to complement efforts to reduce the risk of adverse events as patients transition out of the hospital. It has been reported that 12% of patients have a preventable or ameliorable adverse event in the period immediately following hospital discharge.11, 12 Although Forster et al. did not find a reduction in the risk of adverse events with the addition of a clinical coordinator to a general medical team, they noted incongruence between the coordinator's role and the outcomes measured.6 Similarly, we would need to modify the role of the HCC from a position designed mainly to improve efficiency to one that complements efforts to improve the quality of the discharge process. Possible ways to enhance the HCC role in this regard include increasing the emphasis on and training in patient education skills. Several recently published articles have emphasized the need to redesign the discharge process in an effort to reduce the risk of adverse events following hospital discharge.1315 A modified HCC role might be an essential feature of a redesigned multidisciplinary discharge process.

We were unable to demonstrate improved efficiency for the hospital. Although LOS and cost were lower for patients cared for by the hospitalistHCC teams, the difference was not statistically significant. One possible explanation for why we did not observe a larger reduction in LOS is that our hospitalist service had a lower‐than‐average patient volume during the study period. The lower volume mirrored an unanticipated dip in hospital volume during the same period. Specifically, our service normally discharges an average of 338 patients per month, but during the study period we discharged an average of 235 patients per month. A potential LOS and cost benefit may have been attenuated by the relatively low volume, as hospitalists had ample time to dedicate to communication and coordination of discharge plans.

Our study had several limitations. It was conducted on a nonteaching hospitalist service at a single site. Hospitalist practices vary widely in their staffing and scheduling models. As previously mentioned, we were only able to perform patient satisfaction surveys during the second half of the study period. In addition, hospitalistHCC team patients made up a larger percentage of the patient survey responses (62%) than did control hospitalist patients (38%). This may have affected our ability to detect differences in satisfaction with the hospital discharge process. As also previously noted, our patient volume was lower than normal during the study period. We believe that a higher volume would have magnified differences in hospitalists' perceived efficiency and perhaps resulted in significant improvements in LOS and cost. Finally, the hospital provided funding for only a 12‐week study. This limited our sample size and the power of the study to detect important differences. It is possible that a larger sample size and/or longer study period may have been able to demonstrate a statistically significant improvement in LOS and cost.

Our findings are of particular importance in light of the persistent concerns about hospitalist workload and job satisfaction. Although many hospitalists work with clinical coordinators and case managers, we believe that having the formal structure of a hospitalistcare coordinator team was the key element to improving hospitalist efficiency and satisfaction. We hope that our study is a precursor to research evaluating models of delivering hospital care and their impact on hospitalist work experience, hospital efficiency, and patient outcomes.

Twelve percent of patients have been reported to have preventable or ameliorable adverse events in the period immediately following hospital discharge.1, 2 A potential contributor to the number of adverse events is inadequate transfer of clinical information at hospital discharge. The discharge summary is a vital component of the transfer of information from the inpatient to the outpatient setting. Unfortunately, discharge summaries are often unavailable when follow‐up care occurs and often lack important content.36

Many hospitals are implementing an electronic medical record systems. This creates the opportunity at hospital discharge to immediately assemble the major components of a discharge summary. With enhanced communication systems, this information can be delivered in a variety of ways with minimal delay. We report the results and evaluation of a survey of medicine faculty at an urban academic medical center about the timeliness and quality of discharge summaries, the perceived incidence of adverse events related to suboptimal information transfer at discharge, and the need for the electronically generated discharge summary we plan to design.

METHODS

RESULTS

DISCUSSION

Our study found that outpatient physicians were not satisfied with the timeliness or the quality of current discharge summaries. Our findings are in agreement with previous studies demonstrating that discharge summaries were often not available to outpatient physicians^3,4 and were often of poor quality.5, 6

Preventable or ameliorable adverse events have been reported to occur in 12% of patients in the period immediately following hospital discharge.1, 2 No studies have evaluated the relationship between discharge summaries and preventable adverse events following discharge. Our study found that 41% of outpatient physicians believed that at least one of their patients in the 6 months prior to the survey had sustained a preventable adverse event related to the suboptimal transfer of information at hospital discharge. In addition, the likelihood of physicians reporting one or more preventable adverse events increased with the frequency of seeing patients for follow‐up prior to discharge summary arrival.

In preparation for revising the discharge summary, we asked outpatient physicians to rate the importance of discharge summary content and their preference for method of delivery of discharge summaries. As in previous studies, the outpatient physicians rated discharge medications, discharge diagnosis, test results, and follow‐up plans as highly important.7, 8 Much of this clinical data is now available in the electronic medical record. Therefore, it is possible to electronically assemble much, if not all, of discharge summary content. One recent study demonstrated that database‐generated discharge summaries significantly increased the likelihood that a discharge summary was generated within 4 weeks of hospital discharge.9 The database used in that study required manual data input from a handwritten form. To our knowledge, no study has reported the experience of discharge summaries generated from an electronic medical record.

Our study had several limitations. First, our study used physician survey to assess the timeliness of receiving discharge summaries. Measuring the time to actual receipt of discharge summaries by physicians was beyond the scope of our study. Second, our study did not measure adverse events directly. Instead, we asked outpatient physicians to estimate how many of their patients discharged in the last 6 months had sustained a preventable adverse event related to suboptimal information transfer at discharge. We had limited space in the questionnaire to define the meaning of a preventable adverse event; therefore, the description in the survey does not exactly match previous definitions.1, 2 Our study had a response rate of 54%. It is possible that nonresponders may have been more satisfied with the quality and timeliness of discharge summaries and may have believed fewer patients experienced preventable adverse events related to suboptimal information transfer at discharge.

The results of our study suggest that the use of systems to improve the quality and delivery of discharge summaries has the potential to improve outpatient physician satisfaction and to reduce the number of preventable adverse events that occur during the vulnerable period following hospital discharge. With the use of electronic medical records, we now have the potential to automate the process of assembling and delivering clinical information with minimal delay. We are now using the information from this study to design a partially automated, high‐quality discharge summary that can be delivered to outpatient physicians immediately on discharge.

Twelve percent of patients have been reported to have preventable or ameliorable adverse events in the period immediately following hospital discharge.1, 2 A potential contributor to the number of adverse events is inadequate transfer of clinical information at hospital discharge. The discharge summary is a vital component of the transfer of information from the inpatient to the outpatient setting. Unfortunately, discharge summaries are often unavailable when follow‐up care occurs and often lack important content.36

Many hospitals are implementing an electronic medical record systems. This creates the opportunity at hospital discharge to immediately assemble the major components of a discharge summary. With enhanced communication systems, this information can be delivered in a variety of ways with minimal delay. We report the results and evaluation of a survey of medicine faculty at an urban academic medical center about the timeliness and quality of discharge summaries, the perceived incidence of adverse events related to suboptimal information transfer at discharge, and the need for the electronically generated discharge summary we plan to design.

METHODS

RESULTS

DISCUSSION

Our study found that outpatient physicians were not satisfied with the timeliness or the quality of current discharge summaries. Our findings are in agreement with previous studies demonstrating that discharge summaries were often not available to outpatient physicians^3,4 and were often of poor quality.5, 6

Preventable or ameliorable adverse events have been reported to occur in 12% of patients in the period immediately following hospital discharge.1, 2 No studies have evaluated the relationship between discharge summaries and preventable adverse events following discharge. Our study found that 41% of outpatient physicians believed that at least one of their patients in the 6 months prior to the survey had sustained a preventable adverse event related to the suboptimal transfer of information at hospital discharge. In addition, the likelihood of physicians reporting one or more preventable adverse events increased with the frequency of seeing patients for follow‐up prior to discharge summary arrival.

In preparation for revising the discharge summary, we asked outpatient physicians to rate the importance of discharge summary content and their preference for method of delivery of discharge summaries. As in previous studies, the outpatient physicians rated discharge medications, discharge diagnosis, test results, and follow‐up plans as highly important.7, 8 Much of this clinical data is now available in the electronic medical record. Therefore, it is possible to electronically assemble much, if not all, of discharge summary content. One recent study demonstrated that database‐generated discharge summaries significantly increased the likelihood that a discharge summary was generated within 4 weeks of hospital discharge.9 The database used in that study required manual data input from a handwritten form. To our knowledge, no study has reported the experience of discharge summaries generated from an electronic medical record.

Our study had several limitations. First, our study used physician survey to assess the timeliness of receiving discharge summaries. Measuring the time to actual receipt of discharge summaries by physicians was beyond the scope of our study. Second, our study did not measure adverse events directly. Instead, we asked outpatient physicians to estimate how many of their patients discharged in the last 6 months had sustained a preventable adverse event related to suboptimal information transfer at discharge. We had limited space in the questionnaire to define the meaning of a preventable adverse event; therefore, the description in the survey does not exactly match previous definitions.1, 2 Our study had a response rate of 54%. It is possible that nonresponders may have been more satisfied with the quality and timeliness of discharge summaries and may have believed fewer patients experienced preventable adverse events related to suboptimal information transfer at discharge.

The results of our study suggest that the use of systems to improve the quality and delivery of discharge summaries has the potential to improve outpatient physician satisfaction and to reduce the number of preventable adverse events that occur during the vulnerable period following hospital discharge. With the use of electronic medical records, we now have the potential to automate the process of assembling and delivering clinical information with minimal delay. We are now using the information from this study to design a partially automated, high‐quality discharge summary that can be delivered to outpatient physicians immediately on discharge.

Twelve percent of patients have been reported to have preventable or ameliorable adverse events in the period immediately following hospital discharge.1, 2 A potential contributor to the number of adverse events is inadequate transfer of clinical information at hospital discharge. The discharge summary is a vital component of the transfer of information from the inpatient to the outpatient setting. Unfortunately, discharge summaries are often unavailable when follow‐up care occurs and often lack important content.36

Many hospitals are implementing an electronic medical record systems. This creates the opportunity at hospital discharge to immediately assemble the major components of a discharge summary. With enhanced communication systems, this information can be delivered in a variety of ways with minimal delay. We report the results and evaluation of a survey of medicine faculty at an urban academic medical center about the timeliness and quality of discharge summaries, the perceived incidence of adverse events related to suboptimal information transfer at discharge, and the need for the electronically generated discharge summary we plan to design.

METHODS

RESULTS

DISCUSSION

Our study found that outpatient physicians were not satisfied with the timeliness or the quality of current discharge summaries. Our findings are in agreement with previous studies demonstrating that discharge summaries were often not available to outpatient physicians^3,4 and were often of poor quality.5, 6

Preventable or ameliorable adverse events have been reported to occur in 12% of patients in the period immediately following hospital discharge.1, 2 No studies have evaluated the relationship between discharge summaries and preventable adverse events following discharge. Our study found that 41% of outpatient physicians believed that at least one of their patients in the 6 months prior to the survey had sustained a preventable adverse event related to the suboptimal transfer of information at hospital discharge. In addition, the likelihood of physicians reporting one or more preventable adverse events increased with the frequency of seeing patients for follow‐up prior to discharge summary arrival.

In preparation for revising the discharge summary, we asked outpatient physicians to rate the importance of discharge summary content and their preference for method of delivery of discharge summaries. As in previous studies, the outpatient physicians rated discharge medications, discharge diagnosis, test results, and follow‐up plans as highly important.7, 8 Much of this clinical data is now available in the electronic medical record. Therefore, it is possible to electronically assemble much, if not all, of discharge summary content. One recent study demonstrated that database‐generated discharge summaries significantly increased the likelihood that a discharge summary was generated within 4 weeks of hospital discharge.9 The database used in that study required manual data input from a handwritten form. To our knowledge, no study has reported the experience of discharge summaries generated from an electronic medical record.

Our study had several limitations. First, our study used physician survey to assess the timeliness of receiving discharge summaries. Measuring the time to actual receipt of discharge summaries by physicians was beyond the scope of our study. Second, our study did not measure adverse events directly. Instead, we asked outpatient physicians to estimate how many of their patients discharged in the last 6 months had sustained a preventable adverse event related to suboptimal information transfer at discharge. We had limited space in the questionnaire to define the meaning of a preventable adverse event; therefore, the description in the survey does not exactly match previous definitions.1, 2 Our study had a response rate of 54%. It is possible that nonresponders may have been more satisfied with the quality and timeliness of discharge summaries and may have believed fewer patients experienced preventable adverse events related to suboptimal information transfer at discharge.

The results of our study suggest that the use of systems to improve the quality and delivery of discharge summaries has the potential to improve outpatient physician satisfaction and to reduce the number of preventable adverse events that occur during the vulnerable period following hospital discharge. With the use of electronic medical records, we now have the potential to automate the process of assembling and delivering clinical information with minimal delay. We are now using the information from this study to design a partially automated, high‐quality discharge summary that can be delivered to outpatient physicians immediately on discharge.

The hospitalist model of care has experienced dramatic growth. In 2003 it was estimated that there were 8000 US hospitalists, a number projected to ultimately reach more than 19 000.1, 2 This rapid growth has largely been driven by improvements in clinical efficiency as a result of hospitalist programs. There is a substantial body of evidence showing that hospitalists reduce length of stay and inpatient costs.3 Despite the rapid growth and proven benefit to clinical efficiency, no studies have evaluated the type and frequency of activities that hospitalists perform during routine work. Although the use of hospitalists improves clinical efficiency for the hospital, relatively little is known about how the hospital can improve efficiency for the hospitalist.

Our institution greatly expanded our hospitalist program in June 2003 to create a resident‐uncovered hospitalist service. The impetus for this change was the need to comply with newly revised Accreditation Council for Graduate Medicine Education (ACGME) program requirements regarding resident duty hours. Many teaching hospitals have implemented similar resident‐uncovered hospitalist services.4 Inefficiencies in their work activities quickly became apparent to our hospitalists. Furthermore, our hospitalists believed that they frequently performed simultaneous activities and that they were excessively interrupted by pages.

To evaluate the type and frequency of activities that the hospitalists performed during routine work, we performed a time‐motion study of hospitalist physicians on the resident‐uncovered hospitalist service. Our goal was to identify areas for systems improvements and activities that were better suited for nonphysician providers and to quantify the time spent multitasking and the frequency of paging interruptions.

METHODS

Northwestern Memorial Hospital (NMH) is a 753‐bed hospital in Chicago, Illinois. NMH is the primary teaching hospital affiliated with the Feinberg School of Medicine of Northwestern University. There are 2 general medicine services at NMH: a traditional resident‐covered ward service and the resident‐uncovered hospitalist service. Patients are admitted to one of these 2 services on the basis of, in order of importance, capacity of the services, preference of the outpatient physician, and potential educational value of the admission. Patients admitted to the hospitalist service are preferentially given beds on specific wards intended for hospitalist service patients. Fourth‐year medical students are frequently paired with hospitalists during their medicine subinternship.

The resident‐uncovered hospitalist service comprises 5 daytime hospitalists on duty at a time. The hospitalists are on service for 7 consecutive days, usually followed by 7 consecutive days off. Hospitalists pick up new patients from the night float hospitalist each morning. Daytime admitting duties rotate on a daily basis. One hospitalist accepts new admissions each morning from 7:00 _AM until noon. Two hospitalists accept admissions from noon until 5:00 _PM. One hospitalist accepts admissions from 5:00 _PM until 9:00 _PM. One hospitalist is free from accepting new admissions each day. All daytime hospitalists begin the workday at 7:00 AM and leave when their duties are completed for the day. One night float hospitalist is on duty each night of the week. The night float hospitalist performs admissions and all cross cover activities from 7:00 _PM until 7:00 _AM.

We first conducted a pilot study to help identify specific activities that our hospitalists routinely perform. Broad categories and subcategories of activities were created based on the results of our pilot study, and a published time‐motion study performed on emergency medicine physicians5 (Table 1). Once activities were defined and codes established, our research assistant unobtrusively shadowed hospitalist physicians for periods lasting 3‐5 hours. The observation periods were distributed in order to sample all activities that a daytime hospitalist would perform throughout a typical week. Observation periods included 2 morning admitting periods, 4 morning nonadmitting periods, 4 afternoon admitting periods, 4 afternoon nonadmitting periods, and 2 admitting periods from 5:00 _PM to 9:00 _PM. Activities were recorded on a standardized data collection form in 1‐minute intervals. When multiple activities were performed at the same time, all activities were recorded in the same 1‐minute interval. Incoming pages were recorded as well. To minimize the possibility that observation would affect hospitalist behavior, the research assistant was instructed not to initiate conversation with the hospitalists.

The data collection forms were manually abstracted and minutes tallied for each category and subcategory, for which summary statistics were converted to percentage of total minutes.

RESULTS

Ten hospitalists were shadowed by a single research assistant for a total of 4467 minutes. Seven hospitalists were male and 3 were female. The hospitalists were a mean age of 31 1.6 years of age and had been practicing as a hospitalist for a mean of 2.1 1.0 years. The hospitalists saw an average of 9.4 4.0 patients on the days they were shadowed by the research assistant. Because simultaneous activities were recorded, a total of 5557 minutes of activities were recorded.

The distribution of total minutes recorded in each activity category is shown in Figure 1. Hospitalists spent 18% of their time doing direct patient care, 69% on indirect patient care, 4% on personal activities, and 3% each on professional development, education, and travel.

Figure 1

Of the time hospitalists directly cared for patients, 18% was spent obtaining histories and performing physical examinations on new patients, 53% seeing patients in follow‐up visits, 16% going over discharge instructions, and 13% in family meetings (Figure 2). Of the time hospitalists spent doing indirect patient care, 37% was taken up by documentation, 21% by reviewing results, 7% by orders, and 35% by communication (Figure 2).

Figure 2

As just explained, communication accounted for 35% of indirect patient care activities; it also accounted for 24% of the total activity minutes. The time spent by hospitalists on communication was further broken down as 23% paging other physicians, 31% returning pages, 34% in face‐to‐face communication, 5% taking report on new admissions, 4% on sign‐out to the night float hospitalist, and 3% receiving sign‐out from the night float hospitalist.

Multitasking, performing more than 1 activity at the same time, was done 21% of the time. Hospitalists received an average of 3.4 1.5 pages per hour, and 7% of total activity time was spent returning pages. Other forms of interruption were not evaluated.

DISCUSSION

Our study had several important findings. First, hospitalists spent most of their time on indirect patient care activities and relatively little time on direct patient care. Time‐motion studies of nonhospitalist physicians have reported similar findings.5, 6 A considerable amount of hospitalist time was spent on documentation. This finding also has been reported in studies of nonhospitalist physicians.5, 7

A unique finding in our study was the large amount of time, 24% of total minutes, spent on communication. A study of emergency medicine physicians by Hollingsworth found that 13% of their time was spent on communication activities.5 The large amount of time spent on communication in our study underscores the need for hospitalists to have outstanding communication skills and systems that support efficient communication. Hospitalists spent 6% of their total time paging other physicians and 7% returning pages. Improvements in the efficiency of paging communication could greatly reduce the amount of time communicating by page. Our paging system provides unidirectional alphanumeric paging. In an effort to improve the efficiency of paging, we have asked nurses and consultants to include FYI and callback in the text of the page so it is clear whether the person who has paged the hospitalist needs to be called back. This simple solution to help reduce the number of unnecessary callbacks has previously been proposed by others.8

Another part of solving this problem is adopting the use of 2‐way pagers instead of alphanumeric pagers. Two‐way paging can increase the efficiency of communication even further. For example, a nurse sends a hospitalist a page that asks if the previous diet orders for a patient just returned from a procedure can be resumed. This hospitalist is on another floor in another patient's room. Rather than spending time leaving the other patient's room, finding a phone, calling the floor, waiting for an answer, and then waiting on hold, the hospitalist simply texts a 1‐word answer, Yes, in the 2‐way paging system. In addition to the time occupied by paging activities, hospitalists spent a large amount of time in face‐to‐face communication (8% of total activity time). On the one hand, having hospitalists discuss patient care with consultants and nurses in person on an ongoing basis throughout the day may improve clinical efficiency. On the other hand, the constant potential for interruption may be problematic. Similarly, 2‐way paging could facilitate communication to such a degree that it could actually increase the frequency of interruptions. Research on improvements in communications systems, interventions to improve communication skills, and team‐based care is warranted in order to evaluate the impact on hospitalist workflow.

An important finding in our study was that multitasking and paging interruptions were common. Although this may come as no surprise to practicing hospitalists, the distraction caused by interruptions and multitasking is an important potential cause of medical errors.911 A thorough examination of the types of activities performed simultaneously and whether they contributed to medical error was beyond the scope of our study. Some activities, such as documenting a note on a patient while reviewing the patient's lab results, are concordant (ie, conducted for the same patient) and therefore may be unlikely to contribute to medical error. Other combinations of activities, such as returning a page about one patient while documenting a note on a different patient or having face‐to‐face communication about one patient while entering an order on another patient, are discordant. Discordant activities may contribute to medical error. Further research of the effect of hospitalist multitasking and interruption on medical error is warranted and should be conducted within the framework of concordant versus discordant activities.

We had hoped to find activities that could be performed by non‐physician providers. No high impact activities were discovered that would be better suited for a non‐physician provider in this study. Clerical tasks, such as calling for radiology orders or obtaining medical records, amounted to a small percentage of hospitalist time (less than 1% combined). We did identify several activities in which automation or process improvement would be helpful. Hospitalists spent 5% of time on the combined activity of documenting discharge instructions and writing out prescriptions. Our institution is in the process of implementing an electronic medical record and computerized physician order entry. We are currently working on an automated process to generate printed discharge instructions and prescriptions. This has the potential not only to improve efficiency, but also to eliminate medication errors, as care is transitioned to the outpatient setting.

Our study had several limitations. First, our findings reflect the experience at one institution. Hospitalist practices vary widely in their staffing and scheduling models as well as in their organizational support. The amount of time that hospitalists spend on activities may differ between practices and between individual hospitalists in the same practice. Another limitation to our study pertains to the workflow of our hospitalists and the locations of their patients. As discussed earlier, patients were assigned to a hospitalist according to time of admission, not location of admission. Because of this, the hospitalists were caring for patients on as many as 5 wards. Although travel time amounted to only 3% of total minutes, it is possible that communication time could have been reduced if patients were distributed to hospitalists on the basis of patient location rather than time of admission of patient. For example, physicians and nurses might spend less time communicating in person compared to communicating via unidirectional paging, which frequently requires waiting for a callback. Finally, our study only observed activities performed by the daytime hospitalists at our hospital. The distribution and types of activities performed by nighttime hospitalists may be somewhat different.

Our study may serve as a model for hospitalist time‐motion studies in other settings. Our findings are of particular importance to resident‐uncovered hospitalist programs in academic hospitals, a setting in which operational inefficiencies may be abundant as house staff members have been poorly positioned in the hospital organization to lobby for process change. We hope that our study is a precursor to research evaluating modifications to the environments and systems in which hospitalists work. Such modifications have the potential to improve productivity and work conditions and promote career satisfaction.

The hospitalist model of care has experienced dramatic growth. In 2003 it was estimated that there were 8000 US hospitalists, a number projected to ultimately reach more than 19 000.1, 2 This rapid growth has largely been driven by improvements in clinical efficiency as a result of hospitalist programs. There is a substantial body of evidence showing that hospitalists reduce length of stay and inpatient costs.3 Despite the rapid growth and proven benefit to clinical efficiency, no studies have evaluated the type and frequency of activities that hospitalists perform during routine work. Although the use of hospitalists improves clinical efficiency for the hospital, relatively little is known about how the hospital can improve efficiency for the hospitalist.

Our institution greatly expanded our hospitalist program in June 2003 to create a resident‐uncovered hospitalist service. The impetus for this change was the need to comply with newly revised Accreditation Council for Graduate Medicine Education (ACGME) program requirements regarding resident duty hours. Many teaching hospitals have implemented similar resident‐uncovered hospitalist services.4 Inefficiencies in their work activities quickly became apparent to our hospitalists. Furthermore, our hospitalists believed that they frequently performed simultaneous activities and that they were excessively interrupted by pages.

To evaluate the type and frequency of activities that the hospitalists performed during routine work, we performed a time‐motion study of hospitalist physicians on the resident‐uncovered hospitalist service. Our goal was to identify areas for systems improvements and activities that were better suited for nonphysician providers and to quantify the time spent multitasking and the frequency of paging interruptions.

METHODS

Northwestern Memorial Hospital (NMH) is a 753‐bed hospital in Chicago, Illinois. NMH is the primary teaching hospital affiliated with the Feinberg School of Medicine of Northwestern University. There are 2 general medicine services at NMH: a traditional resident‐covered ward service and the resident‐uncovered hospitalist service. Patients are admitted to one of these 2 services on the basis of, in order of importance, capacity of the services, preference of the outpatient physician, and potential educational value of the admission. Patients admitted to the hospitalist service are preferentially given beds on specific wards intended for hospitalist service patients. Fourth‐year medical students are frequently paired with hospitalists during their medicine subinternship.

The resident‐uncovered hospitalist service comprises 5 daytime hospitalists on duty at a time. The hospitalists are on service for 7 consecutive days, usually followed by 7 consecutive days off. Hospitalists pick up new patients from the night float hospitalist each morning. Daytime admitting duties rotate on a daily basis. One hospitalist accepts new admissions each morning from 7:00 _AM until noon. Two hospitalists accept admissions from noon until 5:00 _PM. One hospitalist accepts admissions from 5:00 _PM until 9:00 _PM. One hospitalist is free from accepting new admissions each day. All daytime hospitalists begin the workday at 7:00 AM and leave when their duties are completed for the day. One night float hospitalist is on duty each night of the week. The night float hospitalist performs admissions and all cross cover activities from 7:00 _PM until 7:00 _AM.

We first conducted a pilot study to help identify specific activities that our hospitalists routinely perform. Broad categories and subcategories of activities were created based on the results of our pilot study, and a published time‐motion study performed on emergency medicine physicians5 (Table 1). Once activities were defined and codes established, our research assistant unobtrusively shadowed hospitalist physicians for periods lasting 3‐5 hours. The observation periods were distributed in order to sample all activities that a daytime hospitalist would perform throughout a typical week. Observation periods included 2 morning admitting periods, 4 morning nonadmitting periods, 4 afternoon admitting periods, 4 afternoon nonadmitting periods, and 2 admitting periods from 5:00 _PM to 9:00 _PM. Activities were recorded on a standardized data collection form in 1‐minute intervals. When multiple activities were performed at the same time, all activities were recorded in the same 1‐minute interval. Incoming pages were recorded as well. To minimize the possibility that observation would affect hospitalist behavior, the research assistant was instructed not to initiate conversation with the hospitalists.

The data collection forms were manually abstracted and minutes tallied for each category and subcategory, for which summary statistics were converted to percentage of total minutes.

RESULTS

Ten hospitalists were shadowed by a single research assistant for a total of 4467 minutes. Seven hospitalists were male and 3 were female. The hospitalists were a mean age of 31 1.6 years of age and had been practicing as a hospitalist for a mean of 2.1 1.0 years. The hospitalists saw an average of 9.4 4.0 patients on the days they were shadowed by the research assistant. Because simultaneous activities were recorded, a total of 5557 minutes of activities were recorded.

The distribution of total minutes recorded in each activity category is shown in Figure 1. Hospitalists spent 18% of their time doing direct patient care, 69% on indirect patient care, 4% on personal activities, and 3% each on professional development, education, and travel.

Figure 1

Of the time hospitalists directly cared for patients, 18% was spent obtaining histories and performing physical examinations on new patients, 53% seeing patients in follow‐up visits, 16% going over discharge instructions, and 13% in family meetings (Figure 2). Of the time hospitalists spent doing indirect patient care, 37% was taken up by documentation, 21% by reviewing results, 7% by orders, and 35% by communication (Figure 2).

Figure 2

As just explained, communication accounted for 35% of indirect patient care activities; it also accounted for 24% of the total activity minutes. The time spent by hospitalists on communication was further broken down as 23% paging other physicians, 31% returning pages, 34% in face‐to‐face communication, 5% taking report on new admissions, 4% on sign‐out to the night float hospitalist, and 3% receiving sign‐out from the night float hospitalist.

Multitasking, performing more than 1 activity at the same time, was done 21% of the time. Hospitalists received an average of 3.4 1.5 pages per hour, and 7% of total activity time was spent returning pages. Other forms of interruption were not evaluated.

DISCUSSION

Our study had several important findings. First, hospitalists spent most of their time on indirect patient care activities and relatively little time on direct patient care. Time‐motion studies of nonhospitalist physicians have reported similar findings.5, 6 A considerable amount of hospitalist time was spent on documentation. This finding also has been reported in studies of nonhospitalist physicians.5, 7

A unique finding in our study was the large amount of time, 24% of total minutes, spent on communication. A study of emergency medicine physicians by Hollingsworth found that 13% of their time was spent on communication activities.5 The large amount of time spent on communication in our study underscores the need for hospitalists to have outstanding communication skills and systems that support efficient communication. Hospitalists spent 6% of their total time paging other physicians and 7% returning pages. Improvements in the efficiency of paging communication could greatly reduce the amount of time communicating by page. Our paging system provides unidirectional alphanumeric paging. In an effort to improve the efficiency of paging, we have asked nurses and consultants to include FYI and callback in the text of the page so it is clear whether the person who has paged the hospitalist needs to be called back. This simple solution to help reduce the number of unnecessary callbacks has previously been proposed by others.8

Another part of solving this problem is adopting the use of 2‐way pagers instead of alphanumeric pagers. Two‐way paging can increase the efficiency of communication even further. For example, a nurse sends a hospitalist a page that asks if the previous diet orders for a patient just returned from a procedure can be resumed. This hospitalist is on another floor in another patient's room. Rather than spending time leaving the other patient's room, finding a phone, calling the floor, waiting for an answer, and then waiting on hold, the hospitalist simply texts a 1‐word answer, Yes, in the 2‐way paging system. In addition to the time occupied by paging activities, hospitalists spent a large amount of time in face‐to‐face communication (8% of total activity time). On the one hand, having hospitalists discuss patient care with consultants and nurses in person on an ongoing basis throughout the day may improve clinical efficiency. On the other hand, the constant potential for interruption may be problematic. Similarly, 2‐way paging could facilitate communication to such a degree that it could actually increase the frequency of interruptions. Research on improvements in communications systems, interventions to improve communication skills, and team‐based care is warranted in order to evaluate the impact on hospitalist workflow.

An important finding in our study was that multitasking and paging interruptions were common. Although this may come as no surprise to practicing hospitalists, the distraction caused by interruptions and multitasking is an important potential cause of medical errors.911 A thorough examination of the types of activities performed simultaneously and whether they contributed to medical error was beyond the scope of our study. Some activities, such as documenting a note on a patient while reviewing the patient's lab results, are concordant (ie, conducted for the same patient) and therefore may be unlikely to contribute to medical error. Other combinations of activities, such as returning a page about one patient while documenting a note on a different patient or having face‐to‐face communication about one patient while entering an order on another patient, are discordant. Discordant activities may contribute to medical error. Further research of the effect of hospitalist multitasking and interruption on medical error is warranted and should be conducted within the framework of concordant versus discordant activities.

We had hoped to find activities that could be performed by non‐physician providers. No high impact activities were discovered that would be better suited for a non‐physician provider in this study. Clerical tasks, such as calling for radiology orders or obtaining medical records, amounted to a small percentage of hospitalist time (less than 1% combined). We did identify several activities in which automation or process improvement would be helpful. Hospitalists spent 5% of time on the combined activity of documenting discharge instructions and writing out prescriptions. Our institution is in the process of implementing an electronic medical record and computerized physician order entry. We are currently working on an automated process to generate printed discharge instructions and prescriptions. This has the potential not only to improve efficiency, but also to eliminate medication errors, as care is transitioned to the outpatient setting.

Our study had several limitations. First, our findings reflect the experience at one institution. Hospitalist practices vary widely in their staffing and scheduling models as well as in their organizational support. The amount of time that hospitalists spend on activities may differ between practices and between individual hospitalists in the same practice. Another limitation to our study pertains to the workflow of our hospitalists and the locations of their patients. As discussed earlier, patients were assigned to a hospitalist according to time of admission, not location of admission. Because of this, the hospitalists were caring for patients on as many as 5 wards. Although travel time amounted to only 3% of total minutes, it is possible that communication time could have been reduced if patients were distributed to hospitalists on the basis of patient location rather than time of admission of patient. For example, physicians and nurses might spend less time communicating in person compared to communicating via unidirectional paging, which frequently requires waiting for a callback. Finally, our study only observed activities performed by the daytime hospitalists at our hospital. The distribution and types of activities performed by nighttime hospitalists may be somewhat different.

Our study may serve as a model for hospitalist time‐motion studies in other settings. Our findings are of particular importance to resident‐uncovered hospitalist programs in academic hospitals, a setting in which operational inefficiencies may be abundant as house staff members have been poorly positioned in the hospital organization to lobby for process change. We hope that our study is a precursor to research evaluating modifications to the environments and systems in which hospitalists work. Such modifications have the potential to improve productivity and work conditions and promote career satisfaction.

The hospitalist model of care has experienced dramatic growth. In 2003 it was estimated that there were 8000 US hospitalists, a number projected to ultimately reach more than 19 000.1, 2 This rapid growth has largely been driven by improvements in clinical efficiency as a result of hospitalist programs. There is a substantial body of evidence showing that hospitalists reduce length of stay and inpatient costs.3 Despite the rapid growth and proven benefit to clinical efficiency, no studies have evaluated the type and frequency of activities that hospitalists perform during routine work. Although the use of hospitalists improves clinical efficiency for the hospital, relatively little is known about how the hospital can improve efficiency for the hospitalist.

Our institution greatly expanded our hospitalist program in June 2003 to create a resident‐uncovered hospitalist service. The impetus for this change was the need to comply with newly revised Accreditation Council for Graduate Medicine Education (ACGME) program requirements regarding resident duty hours. Many teaching hospitals have implemented similar resident‐uncovered hospitalist services.4 Inefficiencies in their work activities quickly became apparent to our hospitalists. Furthermore, our hospitalists believed that they frequently performed simultaneous activities and that they were excessively interrupted by pages.

To evaluate the type and frequency of activities that the hospitalists performed during routine work, we performed a time‐motion study of hospitalist physicians on the resident‐uncovered hospitalist service. Our goal was to identify areas for systems improvements and activities that were better suited for nonphysician providers and to quantify the time spent multitasking and the frequency of paging interruptions.

METHODS

Northwestern Memorial Hospital (NMH) is a 753‐bed hospital in Chicago, Illinois. NMH is the primary teaching hospital affiliated with the Feinberg School of Medicine of Northwestern University. There are 2 general medicine services at NMH: a traditional resident‐covered ward service and the resident‐uncovered hospitalist service. Patients are admitted to one of these 2 services on the basis of, in order of importance, capacity of the services, preference of the outpatient physician, and potential educational value of the admission. Patients admitted to the hospitalist service are preferentially given beds on specific wards intended for hospitalist service patients. Fourth‐year medical students are frequently paired with hospitalists during their medicine subinternship.

The resident‐uncovered hospitalist service comprises 5 daytime hospitalists on duty at a time. The hospitalists are on service for 7 consecutive days, usually followed by 7 consecutive days off. Hospitalists pick up new patients from the night float hospitalist each morning. Daytime admitting duties rotate on a daily basis. One hospitalist accepts new admissions each morning from 7:00 _AM until noon. Two hospitalists accept admissions from noon until 5:00 _PM. One hospitalist accepts admissions from 5:00 _PM until 9:00 _PM. One hospitalist is free from accepting new admissions each day. All daytime hospitalists begin the workday at 7:00 AM and leave when their duties are completed for the day. One night float hospitalist is on duty each night of the week. The night float hospitalist performs admissions and all cross cover activities from 7:00 _PM until 7:00 _AM.

We first conducted a pilot study to help identify specific activities that our hospitalists routinely perform. Broad categories and subcategories of activities were created based on the results of our pilot study, and a published time‐motion study performed on emergency medicine physicians5 (Table 1). Once activities were defined and codes established, our research assistant unobtrusively shadowed hospitalist physicians for periods lasting 3‐5 hours. The observation periods were distributed in order to sample all activities that a daytime hospitalist would perform throughout a typical week. Observation periods included 2 morning admitting periods, 4 morning nonadmitting periods, 4 afternoon admitting periods, 4 afternoon nonadmitting periods, and 2 admitting periods from 5:00 _PM to 9:00 _PM. Activities were recorded on a standardized data collection form in 1‐minute intervals. When multiple activities were performed at the same time, all activities were recorded in the same 1‐minute interval. Incoming pages were recorded as well. To minimize the possibility that observation would affect hospitalist behavior, the research assistant was instructed not to initiate conversation with the hospitalists.

The data collection forms were manually abstracted and minutes tallied for each category and subcategory, for which summary statistics were converted to percentage of total minutes.

RESULTS

Ten hospitalists were shadowed by a single research assistant for a total of 4467 minutes. Seven hospitalists were male and 3 were female. The hospitalists were a mean age of 31 1.6 years of age and had been practicing as a hospitalist for a mean of 2.1 1.0 years. The hospitalists saw an average of 9.4 4.0 patients on the days they were shadowed by the research assistant. Because simultaneous activities were recorded, a total of 5557 minutes of activities were recorded.

The distribution of total minutes recorded in each activity category is shown in Figure 1. Hospitalists spent 18% of their time doing direct patient care, 69% on indirect patient care, 4% on personal activities, and 3% each on professional development, education, and travel.

Figure 1

Of the time hospitalists directly cared for patients, 18% was spent obtaining histories and performing physical examinations on new patients, 53% seeing patients in follow‐up visits, 16% going over discharge instructions, and 13% in family meetings (Figure 2). Of the time hospitalists spent doing indirect patient care, 37% was taken up by documentation, 21% by reviewing results, 7% by orders, and 35% by communication (Figure 2).

Figure 2

As just explained, communication accounted for 35% of indirect patient care activities; it also accounted for 24% of the total activity minutes. The time spent by hospitalists on communication was further broken down as 23% paging other physicians, 31% returning pages, 34% in face‐to‐face communication, 5% taking report on new admissions, 4% on sign‐out to the night float hospitalist, and 3% receiving sign‐out from the night float hospitalist.

Multitasking, performing more than 1 activity at the same time, was done 21% of the time. Hospitalists received an average of 3.4 1.5 pages per hour, and 7% of total activity time was spent returning pages. Other forms of interruption were not evaluated.

DISCUSSION

Our study had several important findings. First, hospitalists spent most of their time on indirect patient care activities and relatively little time on direct patient care. Time‐motion studies of nonhospitalist physicians have reported similar findings.5, 6 A considerable amount of hospitalist time was spent on documentation. This finding also has been reported in studies of nonhospitalist physicians.5, 7

A unique finding in our study was the large amount of time, 24% of total minutes, spent on communication. A study of emergency medicine physicians by Hollingsworth found that 13% of their time was spent on communication activities.5 The large amount of time spent on communication in our study underscores the need for hospitalists to have outstanding communication skills and systems that support efficient communication. Hospitalists spent 6% of their total time paging other physicians and 7% returning pages. Improvements in the efficiency of paging communication could greatly reduce the amount of time communicating by page. Our paging system provides unidirectional alphanumeric paging. In an effort to improve the efficiency of paging, we have asked nurses and consultants to include FYI and callback in the text of the page so it is clear whether the person who has paged the hospitalist needs to be called back. This simple solution to help reduce the number of unnecessary callbacks has previously been proposed by others.8

Another part of solving this problem is adopting the use of 2‐way pagers instead of alphanumeric pagers. Two‐way paging can increase the efficiency of communication even further. For example, a nurse sends a hospitalist a page that asks if the previous diet orders for a patient just returned from a procedure can be resumed. This hospitalist is on another floor in another patient's room. Rather than spending time leaving the other patient's room, finding a phone, calling the floor, waiting for an answer, and then waiting on hold, the hospitalist simply texts a 1‐word answer, Yes, in the 2‐way paging system. In addition to the time occupied by paging activities, hospitalists spent a large amount of time in face‐to‐face communication (8% of total activity time). On the one hand, having hospitalists discuss patient care with consultants and nurses in person on an ongoing basis throughout the day may improve clinical efficiency. On the other hand, the constant potential for interruption may be problematic. Similarly, 2‐way paging could facilitate communication to such a degree that it could actually increase the frequency of interruptions. Research on improvements in communications systems, interventions to improve communication skills, and team‐based care is warranted in order to evaluate the impact on hospitalist workflow.

An important finding in our study was that multitasking and paging interruptions were common. Although this may come as no surprise to practicing hospitalists, the distraction caused by interruptions and multitasking is an important potential cause of medical errors.911 A thorough examination of the types of activities performed simultaneously and whether they contributed to medical error was beyond the scope of our study. Some activities, such as documenting a note on a patient while reviewing the patient's lab results, are concordant (ie, conducted for the same patient) and therefore may be unlikely to contribute to medical error. Other combinations of activities, such as returning a page about one patient while documenting a note on a different patient or having face‐to‐face communication about one patient while entering an order on another patient, are discordant. Discordant activities may contribute to medical error. Further research of the effect of hospitalist multitasking and interruption on medical error is warranted and should be conducted within the framework of concordant versus discordant activities.

We had hoped to find activities that could be performed by non‐physician providers. No high impact activities were discovered that would be better suited for a non‐physician provider in this study. Clerical tasks, such as calling for radiology orders or obtaining medical records, amounted to a small percentage of hospitalist time (less than 1% combined). We did identify several activities in which automation or process improvement would be helpful. Hospitalists spent 5% of time on the combined activity of documenting discharge instructions and writing out prescriptions. Our institution is in the process of implementing an electronic medical record and computerized physician order entry. We are currently working on an automated process to generate printed discharge instructions and prescriptions. This has the potential not only to improve efficiency, but also to eliminate medication errors, as care is transitioned to the outpatient setting.

Our study had several limitations. First, our findings reflect the experience at one institution. Hospitalist practices vary widely in their staffing and scheduling models as well as in their organizational support. The amount of time that hospitalists spend on activities may differ between practices and between individual hospitalists in the same practice. Another limitation to our study pertains to the workflow of our hospitalists and the locations of their patients. As discussed earlier, patients were assigned to a hospitalist according to time of admission, not location of admission. Because of this, the hospitalists were caring for patients on as many as 5 wards. Although travel time amounted to only 3% of total minutes, it is possible that communication time could have been reduced if patients were distributed to hospitalists on the basis of patient location rather than time of admission of patient. For example, physicians and nurses might spend less time communicating in person compared to communicating via unidirectional paging, which frequently requires waiting for a callback. Finally, our study only observed activities performed by the daytime hospitalists at our hospital. The distribution and types of activities performed by nighttime hospitalists may be somewhat different.

Our study may serve as a model for hospitalist time‐motion studies in other settings. Our findings are of particular importance to resident‐uncovered hospitalist programs in academic hospitals, a setting in which operational inefficiencies may be abundant as house staff members have been poorly positioned in the hospital organization to lobby for process change. We hope that our study is a precursor to research evaluating modifications to the environments and systems in which hospitalists work. Such modifications have the potential to improve productivity and work conditions and promote career satisfaction.