Margaret C. Fang, MD, MPH

Resident physician workload has traditionally been measured by patient census.^1,2 However, census and other volume-based metrics such as daily admissions may not accurately reflect workload due to variation in patient complexity. Relative value units (RVUs) are another commonly used marker of workload, but the validity of this metric relies on accurate coding, usually done by the attending physician, and is less directly related to resident physician workload. Because much of hospital-based medicine is mediated through the electronic health record (EHR), which can capture differences in patient complexity,³ electronic records could be harnessed to more comprehensively describe residents’ work. Current government estimates indicate that several hundred companies offer certified EHRs, thanks in large part to the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, which aimed to promote adoption and meaningful use of health information technology.^{4, 5} These systems can collect important data about the usage and operating patterns of physicians, which may provide an insight into workload.^6-8

Accurately measuring workload is important because of the direct link that has been drawn between physician workload and quality metrics. In a study of attending hospitalists, higher workload, as measured by patient census and RVUs, was associated with longer lengths of stay and higher costs of hospitalization.⁹ Another study among medical residents found that as daily admissions increased, length of stay, cost, and inpatient mortality appeared to rise.¹⁰ Although these studies used only volume-based workload metrics, the implication that high workload may negatively impact patient care hints at a possible trade-off between the two that should inform discussions of physician productivity.

In the current study, we examine whether data obtained from the EHR, particularly electronic order volume, could provide valuable information, in addition to patient volume, about resident physician workload. We first tested the feasibility and validity of using electronic order volume as an important component of clinical workload by examining the relationship between electronic order volume and well-established factors that are likely to increase the workload of residents, including patient level of care and severity of illness. Then, using order volume as a marker for workload, we sought to describe whether higher order volumes were associated with two discharge-related quality metrics, completion of a high-quality after-visit summary and timely discharge summary, postulating that quality metrics may suffer when residents are busier.

Study Design and Setting

We performed a single-center retrospective cohort study of patients admitted to the internal medicine service at the University of California, San Francisco (UCSF) Medical Center between May 1, 2015 and July 31, 2016. UCSF is a 600-bed academic medical center, and the inpatient internal medicine teaching service manages an average daily census of 80-90 patients. Medicine teams care for patients on the general acute-care wards, the step-down units (for patients with higher acuity of care), and also patients in the intensive care unit (ICU). ICU patients are comanaged by general medicine teams and intensive care teams; internal medicine teams enter all electronic orders for ICU patients, except for orders for respiratory care or sedating medications. The inpatient internal medicine teaching service comprises eight teams each supervised by an attending physician, a senior resident (in the second or third year of residency training), two interns, and a third- and/or fourth-year medical student. Residents place all clinical orders and complete all clinical documentation through the EHR (Epic Systems, Verona, Wisconsin).¹¹ Typically, the bulk of the orders and documentation, including discharge documentation, is performed by interns; however, the degree of senior resident involvement in these tasks is variable and team-dependent. In addition to the eight resident teams, there are also four attending hospitalist-only internal medicine teams, who manage a service of ~30-40 patients.

Study Population

Our study population comprised all hospitalized adults admitted to the eight resident-run teams on the internal medicine teaching service. Patients cared for by hospitalist-only teams were not included in this analysis. Because the focus of our study was on hospitalizations, individual patients may have been included multiple times over the course of the study. Hospitalizations were excluded if they did not have complete Medicare Severity-Diagnosis Related Group (MS-DRG) data,¹² since this was used as our severity of illness marker. This occurred either because patients were not discharged by the end of the study period or because they had a length of stay of less than one day, because this metric was not assigned to these short-stay (observation) patients.

Data Collection

All electronic orders placed during the study period were obtained by extracting data from Epic’s Clarity database. Our EHR allows for the use of order sets; each order in these sets was counted individually, so that an order set with several orders would not be identified as one order. We identified the time and date that the order was placed, the ordering physician, the identity of the patient for which the order was placed, and the location of the patient when the order was placed, to determine the level of care (ICU, step-down, or general medicine unit). To track the composite volume of orders placed by resident teams, we matched each ordering physician to his or her corresponding resident team using our physician scheduling database, Amion (Spiral Software). We obtained team census by tabulating the total number of patients that a single resident team placed orders on over the course of a given calendar day. From billing data, we identified the MS-DRG weight that was assigned at the end of each hospitalization. Finally, we collected data on adherence to two discharge-related quality metrics to determine whether increased order volume was associated with decreased rates of adherence to these metrics. Using departmental patient-level quality improvement data, we determined whether each metric was met on discharge at the patient level. We also extracted patient-level demographic data, including age, sex, and insurance status, from this departmental quality improvement database.

Discharge Quality Outcome Metrics

We hypothesized that as the total daily electronic orders of a resident team increased, the rate of completion of two discharge-related quality metrics would decline due to the greater time constraints placed on the teams. The first metric we used was the completion of a high-quality after-visit summary (AVS), which has been described by the Centers for Medicare and Medicaid Services as part of its Meaningful Use Initiative.¹³ It was selected by the residents in our program as a particularly high-priority quality metric. Our institution specifically defines a “high-quality” AVS as including the following three components: a principal hospital problem, patient instructions, and follow-up information. The second discharge-related quality metric was the completion of a timely discharge summary, another measure recognized as a critical component in high-quality care.¹⁴ To be considered timely, the discharge summary had to be filed no later than 24 hours after the discharge order was entered into the EHR. This metric was more recently tracked by the internal medicine department and was not selected by the residents as a high-priority metric.

Statistical Analysis

To examine how the order volume per day changed throughout each sequential day of hospital admission, mean orders per hospital day with 95% CIs were plotted. We performed an aggregate analysis of all orders placed for each patient per day across three different levels of care (ICU, step-down, and general medicine). For each day of the study period, we summed all orders for all patients according to their location and divided by the number of total patients in each location to identify the average number of orders written for an ICU, step-down, and general medicine patient that day. We then calculated the mean daily orders for an ICU, step-down, and general medicine patient over the entire study period. We used ANOVA to test for statistically significant differences between the mean daily orders between these locations.

To examine the relationship between severity of illness and order volume, we performed an unadjusted patient-level analysis of orders per patient in the first three days of each hospitalization and stratified the data by the MS-DRG payment weight, which we divided into four quartiles. For each quartile, we calculated the mean number of orders placed in the first three days of admission and used ANOVA to test for statistically significant differences. We restricted the orders to the first three days of hospitalization instead of calculating mean orders per day of hospitalization because we postulated that the majority of orders were entered in these first few days and that with increasing length of stay (which we expected to occur with higher MS-DRG weight), the order volume becomes highly variable, which would tend to skew the mean orders per day.

We used multivariable logistic regression to determine whether the volume of electronic orders on the day of a given patient’s discharge, and also on the day before a given patient’s discharge, was a significant predictor of receiving a high-quality AVS. We adjusted for team census on the day of discharge, MS-DRG weight, age, sex, and insurance status. We then conducted a separate analysis of the association between electronic order volume and likelihood of completing a timely discharge summary among patients where discharge summary data were available. Logistic regression for each case was performed independently, so that team orders on the day prior to a patient’s discharge were not included in the model for the relationship between team orders on the day of a patient’s discharge and the discharge-related quality metric of interest, and vice versa, since including both in the model would be potentially disruptive given that orders on the day before and day of a patient’s discharge are likely correlated.

We also performed a subanalysis in which we restricted orders to only those placed during the daytime hours (7 am-7 pm), since these reflect the work performed by the primary team, and excluded those placed by covering night-shift residents.

IRB Approval

The study was approved by the UCSF Institutional Review Board and was granted a waiver of informed consent.

Population

We identified 7,296 eligible hospitalizations during the study period. After removing hospitalizations according to our exclusion criteria (Figure 1), there were 5,032 hospitalizations that were used in the analysis for which a total of 929,153 orders were written. The vast majority of patients received at least one order per day; fewer than 1% of encounter-days had zero associated orders. The top 10 discharge diagnoses identified in the cohort are listed in Appendix Table 1. A breakdown of orders by order type, across the entire cohort, is displayed in Appendix Table 2. The mean number of orders per patient per day of hospitalization is plotted in the Appendix Figure, which indicates that the number of orders is highest on the day of admission, decreases significantly after the first few days, and becomes increasingly variable with longer lengths of stay.

Patient Level of Care and Severity of Illness Metrics

Patients at a higher level of care had, on average, more orders entered per day. The mean order frequency was 40 orders per day for an ICU patient (standard deviation [SD] 13, range 13-134), 24 for a step-down patient (SD 6, range 11-48), and 19 for a general medicine unit patient (SD 3, range 10-31). The difference in mean daily orders was statistically significant (P < .001, Figure 2a).

Orders also correlated with increasing severity of illness. Patients in the lowest quartile of MS-DRG weight received, on average, 98 orders in the first three days of hospitalization (SD 35, range 2-349), those in the second quartile received 105 orders (SD 38, range 10-380), those in the third quartile received 132 orders (SD 51, range 17-436), and those in the fourth and highest quartile received 149 orders (SD 59, range 32-482). Comparisons between each of these severity of illness categories were significant (P < .001, Figure 2b).

Discharge-Related Quality Metrics

The median number of orders per internal medicine team per day was 343 (IQR 261- 446). Of the 5,032 total discharged patients, 3,657 (73%) received a high-quality AVS on discharge. After controlling for team census, severity of illness, and demographic factors, there was no statistically significant association between total orders on the day of discharge and odds of receiving a high-quality AVS (OR 1.01; 95% CI 0.96-1.06), or between team orders placed the day prior to discharge and odds of receiving a high-quality AVS (OR 0.99; 95% CI 0.95-1.04; Table 1). When we restricted our analysis to orders placed during daytime hours (7 am-7 pm), these findings were largely unchanged (OR 1.05; 95% CI 0.97-1.14 for orders on the day of discharge; OR 1.02; 95% CI 0.95-1.10 for orders on the day before discharge).

There were 3,835 patients for whom data on timing of discharge summary were available. Of these, 3,455 (91.2%) had a discharge summary completed within 24 hours. After controlling for team census, severity of illness, and demographic factors, there was no statistically significant association between total orders placed by the team on a patient’s day of discharge and odds of receiving a timely discharge summary (OR 0.96; 95% CI 0.88-1.05). However, patients were 12% less likely to receive a timely discharge summary for every 100 extra orders the team placed on the day prior to discharge (OR 0.88, 95% CI 0.82-0.95). Patients who received a timely discharge summary were cared for by teams who placed a median of 345 orders the day prior to their discharge, whereas those that did not receive a timely discharge summary were cared for by teams who placed a significantly higher number of orders (375) on the day prior to discharge (Table 2). When we restricted our analysis to only daytime orders, there were no significant changes in the findings (OR 1.00; 95% CI 0.88-1.14 for orders on the day of discharge; OR 0.84; 95% CI 0.75-0.95 for orders on the day prior to discharge).

We found that electronic order volume may be a marker for patient complexity, which encompasses both level of care and severity of illness, and could be a marker of resident physician workload that harnesses readily available data from an EHR. Recent time-motion studies of internal medicine residents indicate that the majority of trainees’ time is spent on computers, engaged in indirect patient care activities such as reading electronic charts, entering electronic orders, and writing computerized notes.^15-18 Capturing these tasks through metrics such as electronic order volume, as we did in this study, can provide valuable insights into resident physician workflow.

We found that ICU patients received more than twice as many orders per day than did general acute care-level patients. Furthermore, we found that patients whose hospitalizations fell into the highest MS-DRG weight quartile received approximately 50% more orders during the first three days of admission compared to that of patients whose hospitalizations fell into the lowest quartile. This strong association indicates that electronic order volume could provide meaningful additional information, in concert with other factors such as census, to describe resident physician workload.

We did not find that our workload measure was significantly associated with high-quality AVS completion. There are several possible explanations for this finding. First, adherence to this quality metric may be independent of workload, possibly because it is highly prioritized by residents at our institution. Second, adherence may only be impacted at levels of workload greater than what was experienced by the residents in our study. Finally, electronic order volume may not encompass enough of total workload to be reliably representative of resident work. However, the tight correlation between electronic order volume with severity of illness and level of care, in conjunction with the finding that patients were less likely to receive a timely discharge summary when workload was high on the day prior to a patient’s discharge, suggests that electronic order volume does indeed encompass a meaningful component of workload, and that with higher workload, adherence to some quality metrics may decline. We found that patients who received a timely discharge summary were discharged by teams who entered 30 fewer orders on the day before discharge compared with patients who did not receive a timely discharge summary. In addition to being statistically significant, it is also likely that this difference is clinically significant, although a determination of clinical significance is outside the scope of this study. Further exploration into the relationship between order volume and other quality metrics that are perhaps more sensitive to workload would be interesting.

The primary strength of our study is in how it demonstrates that EHRs can be harnessed to provide additional insights into clinical workload in a quantifiable and automated manner. Although there are a wide range of EHRs currently in use across the country, the capability to track electronic orders is common and could therefore be used broadly across institutions, with tailoring and standardization specific to each site. This technique is similar to that used by prior investigators who characterized the workload of pediatric residents by orders entered and notes written in the electronic medical record.¹⁹ However, our study is unique, in that we explored the relationship between electronic order volume and patient-level severity metrics as well as discharge-related quality metrics.

Our study is limited by several factors. When conceptualizing resident workload, several other elements that contribute to a sense of “busyness” may be independent of electronic orders and were not measured in our study.²⁰ These include communication factors (such as language discordance, discussion with consulting services, and difficult end-of-life discussions), environmental factors (such as geographic localization), resident physician team factors (such as competing clinical or educational responsibilities), timing (in terms of day of week as well as time of year, since residents in July likely feel “busier” than residents in May), and ultimate discharge destination for patients (those going to a skilled nursing facility may require discharge documentation more urgently). Additionally, we chose to focus on the workload of resident teams, as represented by team orders, as opposed to individual work, which may be more directly correlated to our outcomes of interest, completion of a high-quality AVS, and timely discharge summary, which are usually performed by individuals.

Furthermore, we did not measure the relationship between our objective measure of workload and clinical endpoints. Instead, we chose to focus on process measures because they are less likely to be confounded by clinical factors independent of physician workload.²¹ Future studies should also consider obtaining direct resident-level measures of “busyness” or burnout, or other resident-centered endpoints, such as whether residents left the hospital at times consistent with duty hour regulations or whether they were able to attend educational conferences.

These limitations pose opportunities for further efforts to more comprehensively characterize clinical workload. Additional research is needed to understand and quantify the impact of patient, physician, and environmental factors that are not reflected by electronic order volume. Furthermore, an exploration of other electronic surrogates for clinical workload, such as paging volume and other EHR-derived data points, could also prove valuable in further describing the clinical workload. Future studies should also examine whether there is a relationship between these novel markers of workload and further outcomes, including both process measures and clinical endpoints.

Electronic order volume may provide valuable additional information for estimating the workload of resident physicians caring for hospitalized patients. Further investigation to determine whether the statistically significant differences identified in this study are clinically significant, how the technique used in this work may be applied to different EHRs, an examination of other EHR-derived metrics that may represent workload, and an exploration of additional patient-centered outcomes may be warranted.

Disclosures

Rajkomar reports personal fees from Google LLC, outside the submitted work. Dr. Khanna reports that during the conduct of the study, his salary, and the development of CareWeb (a communication platform that includes a smartphone-based paging application in use in several inpatient clinical units at University of California, San Francisco [UCSF] Medical Center) were supported by funding from the Center for Digital Health Innovation at UCSF. The CareWeb software has been licensed by Voalte.

Disclaimer

The views expressed in the submitted article are of the authors and not an official position of the institution.

Resident physician workload has traditionally been measured by patient census.^1,2 However, census and other volume-based metrics such as daily admissions may not accurately reflect workload due to variation in patient complexity. Relative value units (RVUs) are another commonly used marker of workload, but the validity of this metric relies on accurate coding, usually done by the attending physician, and is less directly related to resident physician workload. Because much of hospital-based medicine is mediated through the electronic health record (EHR), which can capture differences in patient complexity,³ electronic records could be harnessed to more comprehensively describe residents’ work. Current government estimates indicate that several hundred companies offer certified EHRs, thanks in large part to the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, which aimed to promote adoption and meaningful use of health information technology.^{4, 5} These systems can collect important data about the usage and operating patterns of physicians, which may provide an insight into workload.^6-8

Accurately measuring workload is important because of the direct link that has been drawn between physician workload and quality metrics. In a study of attending hospitalists, higher workload, as measured by patient census and RVUs, was associated with longer lengths of stay and higher costs of hospitalization.⁹ Another study among medical residents found that as daily admissions increased, length of stay, cost, and inpatient mortality appeared to rise.¹⁰ Although these studies used only volume-based workload metrics, the implication that high workload may negatively impact patient care hints at a possible trade-off between the two that should inform discussions of physician productivity.

In the current study, we examine whether data obtained from the EHR, particularly electronic order volume, could provide valuable information, in addition to patient volume, about resident physician workload. We first tested the feasibility and validity of using electronic order volume as an important component of clinical workload by examining the relationship between electronic order volume and well-established factors that are likely to increase the workload of residents, including patient level of care and severity of illness. Then, using order volume as a marker for workload, we sought to describe whether higher order volumes were associated with two discharge-related quality metrics, completion of a high-quality after-visit summary and timely discharge summary, postulating that quality metrics may suffer when residents are busier.

Study Design and Setting

We performed a single-center retrospective cohort study of patients admitted to the internal medicine service at the University of California, San Francisco (UCSF) Medical Center between May 1, 2015 and July 31, 2016. UCSF is a 600-bed academic medical center, and the inpatient internal medicine teaching service manages an average daily census of 80-90 patients. Medicine teams care for patients on the general acute-care wards, the step-down units (for patients with higher acuity of care), and also patients in the intensive care unit (ICU). ICU patients are comanaged by general medicine teams and intensive care teams; internal medicine teams enter all electronic orders for ICU patients, except for orders for respiratory care or sedating medications. The inpatient internal medicine teaching service comprises eight teams each supervised by an attending physician, a senior resident (in the second or third year of residency training), two interns, and a third- and/or fourth-year medical student. Residents place all clinical orders and complete all clinical documentation through the EHR (Epic Systems, Verona, Wisconsin).¹¹ Typically, the bulk of the orders and documentation, including discharge documentation, is performed by interns; however, the degree of senior resident involvement in these tasks is variable and team-dependent. In addition to the eight resident teams, there are also four attending hospitalist-only internal medicine teams, who manage a service of ~30-40 patients.

Study Population

Our study population comprised all hospitalized adults admitted to the eight resident-run teams on the internal medicine teaching service. Patients cared for by hospitalist-only teams were not included in this analysis. Because the focus of our study was on hospitalizations, individual patients may have been included multiple times over the course of the study. Hospitalizations were excluded if they did not have complete Medicare Severity-Diagnosis Related Group (MS-DRG) data,¹² since this was used as our severity of illness marker. This occurred either because patients were not discharged by the end of the study period or because they had a length of stay of less than one day, because this metric was not assigned to these short-stay (observation) patients.

Data Collection

All electronic orders placed during the study period were obtained by extracting data from Epic’s Clarity database. Our EHR allows for the use of order sets; each order in these sets was counted individually, so that an order set with several orders would not be identified as one order. We identified the time and date that the order was placed, the ordering physician, the identity of the patient for which the order was placed, and the location of the patient when the order was placed, to determine the level of care (ICU, step-down, or general medicine unit). To track the composite volume of orders placed by resident teams, we matched each ordering physician to his or her corresponding resident team using our physician scheduling database, Amion (Spiral Software). We obtained team census by tabulating the total number of patients that a single resident team placed orders on over the course of a given calendar day. From billing data, we identified the MS-DRG weight that was assigned at the end of each hospitalization. Finally, we collected data on adherence to two discharge-related quality metrics to determine whether increased order volume was associated with decreased rates of adherence to these metrics. Using departmental patient-level quality improvement data, we determined whether each metric was met on discharge at the patient level. We also extracted patient-level demographic data, including age, sex, and insurance status, from this departmental quality improvement database.

Discharge Quality Outcome Metrics

We hypothesized that as the total daily electronic orders of a resident team increased, the rate of completion of two discharge-related quality metrics would decline due to the greater time constraints placed on the teams. The first metric we used was the completion of a high-quality after-visit summary (AVS), which has been described by the Centers for Medicare and Medicaid Services as part of its Meaningful Use Initiative.¹³ It was selected by the residents in our program as a particularly high-priority quality metric. Our institution specifically defines a “high-quality” AVS as including the following three components: a principal hospital problem, patient instructions, and follow-up information. The second discharge-related quality metric was the completion of a timely discharge summary, another measure recognized as a critical component in high-quality care.¹⁴ To be considered timely, the discharge summary had to be filed no later than 24 hours after the discharge order was entered into the EHR. This metric was more recently tracked by the internal medicine department and was not selected by the residents as a high-priority metric.

Statistical Analysis

To examine how the order volume per day changed throughout each sequential day of hospital admission, mean orders per hospital day with 95% CIs were plotted. We performed an aggregate analysis of all orders placed for each patient per day across three different levels of care (ICU, step-down, and general medicine). For each day of the study period, we summed all orders for all patients according to their location and divided by the number of total patients in each location to identify the average number of orders written for an ICU, step-down, and general medicine patient that day. We then calculated the mean daily orders for an ICU, step-down, and general medicine patient over the entire study period. We used ANOVA to test for statistically significant differences between the mean daily orders between these locations.

To examine the relationship between severity of illness and order volume, we performed an unadjusted patient-level analysis of orders per patient in the first three days of each hospitalization and stratified the data by the MS-DRG payment weight, which we divided into four quartiles. For each quartile, we calculated the mean number of orders placed in the first three days of admission and used ANOVA to test for statistically significant differences. We restricted the orders to the first three days of hospitalization instead of calculating mean orders per day of hospitalization because we postulated that the majority of orders were entered in these first few days and that with increasing length of stay (which we expected to occur with higher MS-DRG weight), the order volume becomes highly variable, which would tend to skew the mean orders per day.

We used multivariable logistic regression to determine whether the volume of electronic orders on the day of a given patient’s discharge, and also on the day before a given patient’s discharge, was a significant predictor of receiving a high-quality AVS. We adjusted for team census on the day of discharge, MS-DRG weight, age, sex, and insurance status. We then conducted a separate analysis of the association between electronic order volume and likelihood of completing a timely discharge summary among patients where discharge summary data were available. Logistic regression for each case was performed independently, so that team orders on the day prior to a patient’s discharge were not included in the model for the relationship between team orders on the day of a patient’s discharge and the discharge-related quality metric of interest, and vice versa, since including both in the model would be potentially disruptive given that orders on the day before and day of a patient’s discharge are likely correlated.

We also performed a subanalysis in which we restricted orders to only those placed during the daytime hours (7 am-7 pm), since these reflect the work performed by the primary team, and excluded those placed by covering night-shift residents.

IRB Approval

The study was approved by the UCSF Institutional Review Board and was granted a waiver of informed consent.

Population

We identified 7,296 eligible hospitalizations during the study period. After removing hospitalizations according to our exclusion criteria (Figure 1), there were 5,032 hospitalizations that were used in the analysis for which a total of 929,153 orders were written. The vast majority of patients received at least one order per day; fewer than 1% of encounter-days had zero associated orders. The top 10 discharge diagnoses identified in the cohort are listed in Appendix Table 1. A breakdown of orders by order type, across the entire cohort, is displayed in Appendix Table 2. The mean number of orders per patient per day of hospitalization is plotted in the Appendix Figure, which indicates that the number of orders is highest on the day of admission, decreases significantly after the first few days, and becomes increasingly variable with longer lengths of stay.

Patient Level of Care and Severity of Illness Metrics

Patients at a higher level of care had, on average, more orders entered per day. The mean order frequency was 40 orders per day for an ICU patient (standard deviation [SD] 13, range 13-134), 24 for a step-down patient (SD 6, range 11-48), and 19 for a general medicine unit patient (SD 3, range 10-31). The difference in mean daily orders was statistically significant (P < .001, Figure 2a).

Orders also correlated with increasing severity of illness. Patients in the lowest quartile of MS-DRG weight received, on average, 98 orders in the first three days of hospitalization (SD 35, range 2-349), those in the second quartile received 105 orders (SD 38, range 10-380), those in the third quartile received 132 orders (SD 51, range 17-436), and those in the fourth and highest quartile received 149 orders (SD 59, range 32-482). Comparisons between each of these severity of illness categories were significant (P < .001, Figure 2b).

Discharge-Related Quality Metrics

The median number of orders per internal medicine team per day was 343 (IQR 261- 446). Of the 5,032 total discharged patients, 3,657 (73%) received a high-quality AVS on discharge. After controlling for team census, severity of illness, and demographic factors, there was no statistically significant association between total orders on the day of discharge and odds of receiving a high-quality AVS (OR 1.01; 95% CI 0.96-1.06), or between team orders placed the day prior to discharge and odds of receiving a high-quality AVS (OR 0.99; 95% CI 0.95-1.04; Table 1). When we restricted our analysis to orders placed during daytime hours (7 am-7 pm), these findings were largely unchanged (OR 1.05; 95% CI 0.97-1.14 for orders on the day of discharge; OR 1.02; 95% CI 0.95-1.10 for orders on the day before discharge).

There were 3,835 patients for whom data on timing of discharge summary were available. Of these, 3,455 (91.2%) had a discharge summary completed within 24 hours. After controlling for team census, severity of illness, and demographic factors, there was no statistically significant association between total orders placed by the team on a patient’s day of discharge and odds of receiving a timely discharge summary (OR 0.96; 95% CI 0.88-1.05). However, patients were 12% less likely to receive a timely discharge summary for every 100 extra orders the team placed on the day prior to discharge (OR 0.88, 95% CI 0.82-0.95). Patients who received a timely discharge summary were cared for by teams who placed a median of 345 orders the day prior to their discharge, whereas those that did not receive a timely discharge summary were cared for by teams who placed a significantly higher number of orders (375) on the day prior to discharge (Table 2). When we restricted our analysis to only daytime orders, there were no significant changes in the findings (OR 1.00; 95% CI 0.88-1.14 for orders on the day of discharge; OR 0.84; 95% CI 0.75-0.95 for orders on the day prior to discharge).

We found that electronic order volume may be a marker for patient complexity, which encompasses both level of care and severity of illness, and could be a marker of resident physician workload that harnesses readily available data from an EHR. Recent time-motion studies of internal medicine residents indicate that the majority of trainees’ time is spent on computers, engaged in indirect patient care activities such as reading electronic charts, entering electronic orders, and writing computerized notes.^15-18 Capturing these tasks through metrics such as electronic order volume, as we did in this study, can provide valuable insights into resident physician workflow.

We found that ICU patients received more than twice as many orders per day than did general acute care-level patients. Furthermore, we found that patients whose hospitalizations fell into the highest MS-DRG weight quartile received approximately 50% more orders during the first three days of admission compared to that of patients whose hospitalizations fell into the lowest quartile. This strong association indicates that electronic order volume could provide meaningful additional information, in concert with other factors such as census, to describe resident physician workload.

We did not find that our workload measure was significantly associated with high-quality AVS completion. There are several possible explanations for this finding. First, adherence to this quality metric may be independent of workload, possibly because it is highly prioritized by residents at our institution. Second, adherence may only be impacted at levels of workload greater than what was experienced by the residents in our study. Finally, electronic order volume may not encompass enough of total workload to be reliably representative of resident work. However, the tight correlation between electronic order volume with severity of illness and level of care, in conjunction with the finding that patients were less likely to receive a timely discharge summary when workload was high on the day prior to a patient’s discharge, suggests that electronic order volume does indeed encompass a meaningful component of workload, and that with higher workload, adherence to some quality metrics may decline. We found that patients who received a timely discharge summary were discharged by teams who entered 30 fewer orders on the day before discharge compared with patients who did not receive a timely discharge summary. In addition to being statistically significant, it is also likely that this difference is clinically significant, although a determination of clinical significance is outside the scope of this study. Further exploration into the relationship between order volume and other quality metrics that are perhaps more sensitive to workload would be interesting.

The primary strength of our study is in how it demonstrates that EHRs can be harnessed to provide additional insights into clinical workload in a quantifiable and automated manner. Although there are a wide range of EHRs currently in use across the country, the capability to track electronic orders is common and could therefore be used broadly across institutions, with tailoring and standardization specific to each site. This technique is similar to that used by prior investigators who characterized the workload of pediatric residents by orders entered and notes written in the electronic medical record.¹⁹ However, our study is unique, in that we explored the relationship between electronic order volume and patient-level severity metrics as well as discharge-related quality metrics.

Our study is limited by several factors. When conceptualizing resident workload, several other elements that contribute to a sense of “busyness” may be independent of electronic orders and were not measured in our study.²⁰ These include communication factors (such as language discordance, discussion with consulting services, and difficult end-of-life discussions), environmental factors (such as geographic localization), resident physician team factors (such as competing clinical or educational responsibilities), timing (in terms of day of week as well as time of year, since residents in July likely feel “busier” than residents in May), and ultimate discharge destination for patients (those going to a skilled nursing facility may require discharge documentation more urgently). Additionally, we chose to focus on the workload of resident teams, as represented by team orders, as opposed to individual work, which may be more directly correlated to our outcomes of interest, completion of a high-quality AVS, and timely discharge summary, which are usually performed by individuals.

Furthermore, we did not measure the relationship between our objective measure of workload and clinical endpoints. Instead, we chose to focus on process measures because they are less likely to be confounded by clinical factors independent of physician workload.²¹ Future studies should also consider obtaining direct resident-level measures of “busyness” or burnout, or other resident-centered endpoints, such as whether residents left the hospital at times consistent with duty hour regulations or whether they were able to attend educational conferences.

These limitations pose opportunities for further efforts to more comprehensively characterize clinical workload. Additional research is needed to understand and quantify the impact of patient, physician, and environmental factors that are not reflected by electronic order volume. Furthermore, an exploration of other electronic surrogates for clinical workload, such as paging volume and other EHR-derived data points, could also prove valuable in further describing the clinical workload. Future studies should also examine whether there is a relationship between these novel markers of workload and further outcomes, including both process measures and clinical endpoints.

Electronic order volume may provide valuable additional information for estimating the workload of resident physicians caring for hospitalized patients. Further investigation to determine whether the statistically significant differences identified in this study are clinically significant, how the technique used in this work may be applied to different EHRs, an examination of other EHR-derived metrics that may represent workload, and an exploration of additional patient-centered outcomes may be warranted.

Disclosures

Rajkomar reports personal fees from Google LLC, outside the submitted work. Dr. Khanna reports that during the conduct of the study, his salary, and the development of CareWeb (a communication platform that includes a smartphone-based paging application in use in several inpatient clinical units at University of California, San Francisco [UCSF] Medical Center) were supported by funding from the Center for Digital Health Innovation at UCSF. The CareWeb software has been licensed by Voalte.

Disclaimer

The views expressed in the submitted article are of the authors and not an official position of the institution.

Resident physician workload has traditionally been measured by patient census.^1,2 However, census and other volume-based metrics such as daily admissions may not accurately reflect workload due to variation in patient complexity. Relative value units (RVUs) are another commonly used marker of workload, but the validity of this metric relies on accurate coding, usually done by the attending physician, and is less directly related to resident physician workload. Because much of hospital-based medicine is mediated through the electronic health record (EHR), which can capture differences in patient complexity,³ electronic records could be harnessed to more comprehensively describe residents’ work. Current government estimates indicate that several hundred companies offer certified EHRs, thanks in large part to the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009, which aimed to promote adoption and meaningful use of health information technology.^{4, 5} These systems can collect important data about the usage and operating patterns of physicians, which may provide an insight into workload.^6-8

Accurately measuring workload is important because of the direct link that has been drawn between physician workload and quality metrics. In a study of attending hospitalists, higher workload, as measured by patient census and RVUs, was associated with longer lengths of stay and higher costs of hospitalization.⁹ Another study among medical residents found that as daily admissions increased, length of stay, cost, and inpatient mortality appeared to rise.¹⁰ Although these studies used only volume-based workload metrics, the implication that high workload may negatively impact patient care hints at a possible trade-off between the two that should inform discussions of physician productivity.

In the current study, we examine whether data obtained from the EHR, particularly electronic order volume, could provide valuable information, in addition to patient volume, about resident physician workload. We first tested the feasibility and validity of using electronic order volume as an important component of clinical workload by examining the relationship between electronic order volume and well-established factors that are likely to increase the workload of residents, including patient level of care and severity of illness. Then, using order volume as a marker for workload, we sought to describe whether higher order volumes were associated with two discharge-related quality metrics, completion of a high-quality after-visit summary and timely discharge summary, postulating that quality metrics may suffer when residents are busier.

Study Design and Setting

We performed a single-center retrospective cohort study of patients admitted to the internal medicine service at the University of California, San Francisco (UCSF) Medical Center between May 1, 2015 and July 31, 2016. UCSF is a 600-bed academic medical center, and the inpatient internal medicine teaching service manages an average daily census of 80-90 patients. Medicine teams care for patients on the general acute-care wards, the step-down units (for patients with higher acuity of care), and also patients in the intensive care unit (ICU). ICU patients are comanaged by general medicine teams and intensive care teams; internal medicine teams enter all electronic orders for ICU patients, except for orders for respiratory care or sedating medications. The inpatient internal medicine teaching service comprises eight teams each supervised by an attending physician, a senior resident (in the second or third year of residency training), two interns, and a third- and/or fourth-year medical student. Residents place all clinical orders and complete all clinical documentation through the EHR (Epic Systems, Verona, Wisconsin).¹¹ Typically, the bulk of the orders and documentation, including discharge documentation, is performed by interns; however, the degree of senior resident involvement in these tasks is variable and team-dependent. In addition to the eight resident teams, there are also four attending hospitalist-only internal medicine teams, who manage a service of ~30-40 patients.

Study Population

Our study population comprised all hospitalized adults admitted to the eight resident-run teams on the internal medicine teaching service. Patients cared for by hospitalist-only teams were not included in this analysis. Because the focus of our study was on hospitalizations, individual patients may have been included multiple times over the course of the study. Hospitalizations were excluded if they did not have complete Medicare Severity-Diagnosis Related Group (MS-DRG) data,¹² since this was used as our severity of illness marker. This occurred either because patients were not discharged by the end of the study period or because they had a length of stay of less than one day, because this metric was not assigned to these short-stay (observation) patients.

Data Collection

All electronic orders placed during the study period were obtained by extracting data from Epic’s Clarity database. Our EHR allows for the use of order sets; each order in these sets was counted individually, so that an order set with several orders would not be identified as one order. We identified the time and date that the order was placed, the ordering physician, the identity of the patient for which the order was placed, and the location of the patient when the order was placed, to determine the level of care (ICU, step-down, or general medicine unit). To track the composite volume of orders placed by resident teams, we matched each ordering physician to his or her corresponding resident team using our physician scheduling database, Amion (Spiral Software). We obtained team census by tabulating the total number of patients that a single resident team placed orders on over the course of a given calendar day. From billing data, we identified the MS-DRG weight that was assigned at the end of each hospitalization. Finally, we collected data on adherence to two discharge-related quality metrics to determine whether increased order volume was associated with decreased rates of adherence to these metrics. Using departmental patient-level quality improvement data, we determined whether each metric was met on discharge at the patient level. We also extracted patient-level demographic data, including age, sex, and insurance status, from this departmental quality improvement database.

Discharge Quality Outcome Metrics

We hypothesized that as the total daily electronic orders of a resident team increased, the rate of completion of two discharge-related quality metrics would decline due to the greater time constraints placed on the teams. The first metric we used was the completion of a high-quality after-visit summary (AVS), which has been described by the Centers for Medicare and Medicaid Services as part of its Meaningful Use Initiative.¹³ It was selected by the residents in our program as a particularly high-priority quality metric. Our institution specifically defines a “high-quality” AVS as including the following three components: a principal hospital problem, patient instructions, and follow-up information. The second discharge-related quality metric was the completion of a timely discharge summary, another measure recognized as a critical component in high-quality care.¹⁴ To be considered timely, the discharge summary had to be filed no later than 24 hours after the discharge order was entered into the EHR. This metric was more recently tracked by the internal medicine department and was not selected by the residents as a high-priority metric.

Statistical Analysis

To examine how the order volume per day changed throughout each sequential day of hospital admission, mean orders per hospital day with 95% CIs were plotted. We performed an aggregate analysis of all orders placed for each patient per day across three different levels of care (ICU, step-down, and general medicine). For each day of the study period, we summed all orders for all patients according to their location and divided by the number of total patients in each location to identify the average number of orders written for an ICU, step-down, and general medicine patient that day. We then calculated the mean daily orders for an ICU, step-down, and general medicine patient over the entire study period. We used ANOVA to test for statistically significant differences between the mean daily orders between these locations.

To examine the relationship between severity of illness and order volume, we performed an unadjusted patient-level analysis of orders per patient in the first three days of each hospitalization and stratified the data by the MS-DRG payment weight, which we divided into four quartiles. For each quartile, we calculated the mean number of orders placed in the first three days of admission and used ANOVA to test for statistically significant differences. We restricted the orders to the first three days of hospitalization instead of calculating mean orders per day of hospitalization because we postulated that the majority of orders were entered in these first few days and that with increasing length of stay (which we expected to occur with higher MS-DRG weight), the order volume becomes highly variable, which would tend to skew the mean orders per day.

We used multivariable logistic regression to determine whether the volume of electronic orders on the day of a given patient’s discharge, and also on the day before a given patient’s discharge, was a significant predictor of receiving a high-quality AVS. We adjusted for team census on the day of discharge, MS-DRG weight, age, sex, and insurance status. We then conducted a separate analysis of the association between electronic order volume and likelihood of completing a timely discharge summary among patients where discharge summary data were available. Logistic regression for each case was performed independently, so that team orders on the day prior to a patient’s discharge were not included in the model for the relationship between team orders on the day of a patient’s discharge and the discharge-related quality metric of interest, and vice versa, since including both in the model would be potentially disruptive given that orders on the day before and day of a patient’s discharge are likely correlated.

We also performed a subanalysis in which we restricted orders to only those placed during the daytime hours (7 am-7 pm), since these reflect the work performed by the primary team, and excluded those placed by covering night-shift residents.

IRB Approval

The study was approved by the UCSF Institutional Review Board and was granted a waiver of informed consent.

Population

We identified 7,296 eligible hospitalizations during the study period. After removing hospitalizations according to our exclusion criteria (Figure 1), there were 5,032 hospitalizations that were used in the analysis for which a total of 929,153 orders were written. The vast majority of patients received at least one order per day; fewer than 1% of encounter-days had zero associated orders. The top 10 discharge diagnoses identified in the cohort are listed in Appendix Table 1. A breakdown of orders by order type, across the entire cohort, is displayed in Appendix Table 2. The mean number of orders per patient per day of hospitalization is plotted in the Appendix Figure, which indicates that the number of orders is highest on the day of admission, decreases significantly after the first few days, and becomes increasingly variable with longer lengths of stay.

Patient Level of Care and Severity of Illness Metrics

Patients at a higher level of care had, on average, more orders entered per day. The mean order frequency was 40 orders per day for an ICU patient (standard deviation [SD] 13, range 13-134), 24 for a step-down patient (SD 6, range 11-48), and 19 for a general medicine unit patient (SD 3, range 10-31). The difference in mean daily orders was statistically significant (P < .001, Figure 2a).

Orders also correlated with increasing severity of illness. Patients in the lowest quartile of MS-DRG weight received, on average, 98 orders in the first three days of hospitalization (SD 35, range 2-349), those in the second quartile received 105 orders (SD 38, range 10-380), those in the third quartile received 132 orders (SD 51, range 17-436), and those in the fourth and highest quartile received 149 orders (SD 59, range 32-482). Comparisons between each of these severity of illness categories were significant (P < .001, Figure 2b).

Discharge-Related Quality Metrics

The median number of orders per internal medicine team per day was 343 (IQR 261- 446). Of the 5,032 total discharged patients, 3,657 (73%) received a high-quality AVS on discharge. After controlling for team census, severity of illness, and demographic factors, there was no statistically significant association between total orders on the day of discharge and odds of receiving a high-quality AVS (OR 1.01; 95% CI 0.96-1.06), or between team orders placed the day prior to discharge and odds of receiving a high-quality AVS (OR 0.99; 95% CI 0.95-1.04; Table 1). When we restricted our analysis to orders placed during daytime hours (7 am-7 pm), these findings were largely unchanged (OR 1.05; 95% CI 0.97-1.14 for orders on the day of discharge; OR 1.02; 95% CI 0.95-1.10 for orders on the day before discharge).

There were 3,835 patients for whom data on timing of discharge summary were available. Of these, 3,455 (91.2%) had a discharge summary completed within 24 hours. After controlling for team census, severity of illness, and demographic factors, there was no statistically significant association between total orders placed by the team on a patient’s day of discharge and odds of receiving a timely discharge summary (OR 0.96; 95% CI 0.88-1.05). However, patients were 12% less likely to receive a timely discharge summary for every 100 extra orders the team placed on the day prior to discharge (OR 0.88, 95% CI 0.82-0.95). Patients who received a timely discharge summary were cared for by teams who placed a median of 345 orders the day prior to their discharge, whereas those that did not receive a timely discharge summary were cared for by teams who placed a significantly higher number of orders (375) on the day prior to discharge (Table 2). When we restricted our analysis to only daytime orders, there were no significant changes in the findings (OR 1.00; 95% CI 0.88-1.14 for orders on the day of discharge; OR 0.84; 95% CI 0.75-0.95 for orders on the day prior to discharge).

We found that electronic order volume may be a marker for patient complexity, which encompasses both level of care and severity of illness, and could be a marker of resident physician workload that harnesses readily available data from an EHR. Recent time-motion studies of internal medicine residents indicate that the majority of trainees’ time is spent on computers, engaged in indirect patient care activities such as reading electronic charts, entering electronic orders, and writing computerized notes.^15-18 Capturing these tasks through metrics such as electronic order volume, as we did in this study, can provide valuable insights into resident physician workflow.

We found that ICU patients received more than twice as many orders per day than did general acute care-level patients. Furthermore, we found that patients whose hospitalizations fell into the highest MS-DRG weight quartile received approximately 50% more orders during the first three days of admission compared to that of patients whose hospitalizations fell into the lowest quartile. This strong association indicates that electronic order volume could provide meaningful additional information, in concert with other factors such as census, to describe resident physician workload.

We did not find that our workload measure was significantly associated with high-quality AVS completion. There are several possible explanations for this finding. First, adherence to this quality metric may be independent of workload, possibly because it is highly prioritized by residents at our institution. Second, adherence may only be impacted at levels of workload greater than what was experienced by the residents in our study. Finally, electronic order volume may not encompass enough of total workload to be reliably representative of resident work. However, the tight correlation between electronic order volume with severity of illness and level of care, in conjunction with the finding that patients were less likely to receive a timely discharge summary when workload was high on the day prior to a patient’s discharge, suggests that electronic order volume does indeed encompass a meaningful component of workload, and that with higher workload, adherence to some quality metrics may decline. We found that patients who received a timely discharge summary were discharged by teams who entered 30 fewer orders on the day before discharge compared with patients who did not receive a timely discharge summary. In addition to being statistically significant, it is also likely that this difference is clinically significant, although a determination of clinical significance is outside the scope of this study. Further exploration into the relationship between order volume and other quality metrics that are perhaps more sensitive to workload would be interesting.

The primary strength of our study is in how it demonstrates that EHRs can be harnessed to provide additional insights into clinical workload in a quantifiable and automated manner. Although there are a wide range of EHRs currently in use across the country, the capability to track electronic orders is common and could therefore be used broadly across institutions, with tailoring and standardization specific to each site. This technique is similar to that used by prior investigators who characterized the workload of pediatric residents by orders entered and notes written in the electronic medical record.¹⁹ However, our study is unique, in that we explored the relationship between electronic order volume and patient-level severity metrics as well as discharge-related quality metrics.

Our study is limited by several factors. When conceptualizing resident workload, several other elements that contribute to a sense of “busyness” may be independent of electronic orders and were not measured in our study.²⁰ These include communication factors (such as language discordance, discussion with consulting services, and difficult end-of-life discussions), environmental factors (such as geographic localization), resident physician team factors (such as competing clinical or educational responsibilities), timing (in terms of day of week as well as time of year, since residents in July likely feel “busier” than residents in May), and ultimate discharge destination for patients (those going to a skilled nursing facility may require discharge documentation more urgently). Additionally, we chose to focus on the workload of resident teams, as represented by team orders, as opposed to individual work, which may be more directly correlated to our outcomes of interest, completion of a high-quality AVS, and timely discharge summary, which are usually performed by individuals.

Furthermore, we did not measure the relationship between our objective measure of workload and clinical endpoints. Instead, we chose to focus on process measures because they are less likely to be confounded by clinical factors independent of physician workload.²¹ Future studies should also consider obtaining direct resident-level measures of “busyness” or burnout, or other resident-centered endpoints, such as whether residents left the hospital at times consistent with duty hour regulations or whether they were able to attend educational conferences.

These limitations pose opportunities for further efforts to more comprehensively characterize clinical workload. Additional research is needed to understand and quantify the impact of patient, physician, and environmental factors that are not reflected by electronic order volume. Furthermore, an exploration of other electronic surrogates for clinical workload, such as paging volume and other EHR-derived data points, could also prove valuable in further describing the clinical workload. Future studies should also examine whether there is a relationship between these novel markers of workload and further outcomes, including both process measures and clinical endpoints.

Electronic order volume may provide valuable additional information for estimating the workload of resident physicians caring for hospitalized patients. Further investigation to determine whether the statistically significant differences identified in this study are clinically significant, how the technique used in this work may be applied to different EHRs, an examination of other EHR-derived metrics that may represent workload, and an exploration of additional patient-centered outcomes may be warranted.

Disclosures

Rajkomar reports personal fees from Google LLC, outside the submitted work. Dr. Khanna reports that during the conduct of the study, his salary, and the development of CareWeb (a communication platform that includes a smartphone-based paging application in use in several inpatient clinical units at University of California, San Francisco [UCSF] Medical Center) were supported by funding from the Center for Digital Health Innovation at UCSF. The CareWeb software has been licensed by Voalte.

Disclaimer

The views expressed in the submitted article are of the authors and not an official position of the institution.

The discharge process is a critical bottleneck for efficient patient flow through the hospital. Delayed discharges translate into delays in admissions and other patient transitions, often leading to excess costs, patient dissatisfaction, and even patient harm.^1-3 The emergency department is particularly impacted by these delays; bottlenecks there lead to overcrowding, increased overall hospital length of stay, and increased risks for bad outcomes during hospitalization.²

Academic medical centers in particular may struggle with delayed discharges. In a typical teaching hospital, a team composed of an attending physician and housestaff share responsibility for determining the discharge plan. Additionally, clinical teaching activities may affect the process and quality of discharge.^4-6

The prevalence and causes of delayed discharges vary greatly.^7-9 To improve efficiency around discharge, many hospitals have launched initiatives designed to discharge patients earlier in the day, including goal setting (“discharge by noon”), scheduling discharge appointments, and using quality-improvement methods, such as Lean Methodology (LEAN), to remove inefficiencies within discharge processes.^10-12 However, there are few data on the prevalence and effectiveness of different strategies.

The aim of this study was to survey academic hospitalist and general internal medicine physician leaders to elicit their perspectives on the factors contributing to discharge timing and the relative importance and effectiveness of early-discharge initiatives.

Study Design, Participants, and Oversight

We obtained a list of 115 university-affiliated hospitals associated with a residency program and, in most cases, a medical school from Vizient Inc. (formerly University HealthSystem Consortium), an alliance of academic medical centers and affiliated hospitals. Each member institution submits clinical data to allow for the benchmarking of outcomes to drive transparency and quality improvement.¹³ More than 95% of the nation’s academic medical centers and affiliated hospitals participate in this collaborative. Vizient works with members but does not set nor promote quality metrics, such as discharge timeliness. E-mail addresses for hospital medicine physician leaders (eg, division chief) of major academic medical centers were obtained from each institution via publicly available data (eg, the institution’s website). When an institution did not have a hospital medicine section, we identified the division chief of general internal medicine. The University of California, San Francisco Institutional Review Board approved this study.

Survey Development and Domains

We developed a 30-item survey to evaluate 5 main domains of interest: current discharge practices, degree of prioritization of early discharge on the inpatient service, barriers to timely discharge, prevalence and perceived effectiveness of implemented early-discharge initiatives, and barriers to implementation of early-discharge initiatives.

Respondents were first asked to identify their institutions’ goals for discharge time. They were then asked to compare the priority of early-discharge initiatives to other departmental quality-improvement initiatives, such as reducing 30-day readmissions, improving interpreter use, and improving patient satisfaction. Next, respondents were asked to estimate the degree to which clinical or patient factors contributed to delays in discharge. Respondents were then asked whether specific early-discharge initiatives, such as changes to rounding practices or communication interventions, were implemented at their institutions and, if so, the perceived effectiveness of these initiatives at meeting discharge targets. We piloted the questions locally with physicians and researchers prior to finalizing the survey.

Data Collection

We sent surveys via an online platform (Research Electronic Data Capture).¹⁴ Nonresponders were sent 2 e-mail reminders and then a follow-up telephone call asking them to complete the survey. Only 1 survey per academic medical center was collected. Any respondent who completed the survey within 2 weeks of receiving it was entered to win a Kindle Fire.

Data Analysis

We summarized survey responses using descriptive statistics. Analysis was completed in IBM SPSS version 22 (Armonk, NY).

Survey Respondent and Institutional Characteristics

Of the 115 institutions surveyed, we received 61 responses (response rate of 53%), with 39 (64%) respondents from divisions of hospital medicine and 22 (36%) from divisions of general internal medicine. A majority (n = 53; 87%) stated their medicine services have a combination of teaching (with residents) and nonteaching (without residents) teams. Thirty-nine (64%) reported having daily multidisciplinary rounds.

Early Discharge as a Priority

Forty-seven (77%) institutional representatives strongly agreed or agreed that early discharge was a priority, with discharge by noon being the most common target time (n = 23; 38%). Thirty (50%) respondents rated early discharge as more important than improving interpreter use for non-English-speaking patients and equally important as reducing 30-day readmissions (n = 29; 48%) and improving patient satisfaction (n = 27; 44%).

Factors Delaying Discharge

The most common factors perceived as delaying discharge were considered external to the hospital, such as postacute care bed availability or scheduled (eg, ambulance) transport delays (n = 48; 79%), followed by patient factors such as patient transport issues (n = 44; 72%). Less commonly reported were workflow issues, such as competing primary team priorities or case manager bandwidth (n = 38; 62%; Table 1).

Initiatives to Improve Discharge

The most commonly implemented initiatives perceived as effective at improving discharge times were the preemptive identification of early discharges to plan discharge paperwork (n = 34; 56%), communication with patients about anticipated discharge time on the day prior to discharge (n = 29; 48%), and the implementation of additional rounds between physician teams and case managers specifically around discharge planning (n = 28; 46%). Initiatives not commonly implemented included regular audit of and feedback on discharge times to providers and teams (n = 21; 34%), the use of a discharge readiness checklist (n = 26; 43%), incentives such as bonuses or penalties (n = 37; 61%), the use of a whiteboard to indicate discharge times (n = 23; 38%), and dedicated quality-improvement approaches such as LEAN (n = 37; 61%; Table 2).

Our study suggests early discharge for medicine patients is a priority among academic institutions. Hospitalist and general internal medicine physician leaders in our study generally attributed delayed discharges to external factors, particularly unavailability of postacute care facilities and transportation delays. Having issues with finding postacute care placements is consistent with previous findings by Selker et al.¹⁵ and Carey et al.⁸ This is despite the 20-year difference between Selker et al.’s study and the current study, reflecting a continued opportunity for improvement, including stronger partnerships with local and regional postacute care facilities to expedite care transition and stronger discharge-planning efforts early in the admission process. Efforts in postacute care placement may be particularly important for Medicaid-insured and uninsured patients.

Our responders, hospitalist and internal medicine physician leaders, did not perceive the additional responsibilities of teaching and supervising trainees to be factors that significantly delayed patient discharge. This is in contrast to previous studies, which attributed delays in discharge to prolonged clinical decision-making related to teaching and supervision.^4-6,8 This discrepancy may be due to the fact that we only surveyed single physician leaders at each institution and not residents. Our finding warrants further investigation to understand the degree to which resident skills may impact discharge planning and processes.

Institutions represented in our study have attempted a variety of initiatives promoting earlier discharge, with varying levels of perceived success. Initiatives perceived to be the most effective by hospital leaders centered on 2 main areas: (1) changing individual provider practice and (2) anticipatory discharge preparation. Interestingly, this is in discordance with the main factors labeled as causing delays in discharges, such as obtaining postacute care beds, busy case managers, and competing demands on primary teams. We hypothesize this may be because such changes require organization- or system-level changes and are perceived as more arduous than changes at the individual level. In addition, changes to individual provider behavior may be more cost- and time-effective than more systemic initiatives.

Our findings are consistent with the work published by Wertheimer and colleagues,¹¹ who show that additional afternoon interdisciplinary rounds can help identify patients who may be discharged before noon the next day. In their study, identifying such patients in advance improved the overall early-discharge rate the following day.

Our findings should be interpreted in light of several limitations. Our survey only considers the perspectives of hospitalist and general internal medicine physician leaders at academic medical centers that are part of the Vizient Inc. collaborative. They do not represent all academic or community-based medical centers. Although the perceived effectiveness of some initiatives was high, we did not collect empirical data to support these claims or to determine which initiative had the greatest relative impact on discharge timeliness. Lastly, we did not obtain resident, nursing, or case manager perspectives on discharge practices. Given their roles as frontline providers, we may have missed these alternative perspectives.

Our study shows there is a strong interest in increasing early discharges in an effort to improve hospital throughput and patient flow.

Acknowledgments

The authors thank all participants who completed the survey and Danielle Carrier at Vizient Inc. (formally University HealthSystem Consortium) for her assistance in obtaining data.

Disclosures

Hemali Patel, Margaret Fang, Michelle Mourad, Adrienne Green, Ryan Murphy, and James Harrison report no conflicts of interest. At the time the research was conducted, Robert Wachter reported that he is a member of the Lucian Leape Institute at the National Patient Safety Foundation (no compensation except travel expenses); recently chaired an advisory board to England’s National Health Service (NHS) reviewing the NHS’s digital health strategy (no compensation except travel expenses); has a contract with UCSF from the Agency for Healthcare Research and Quality to edit a patient-safety website; receives compensation from John Wiley & Sons for writing a blog; receives royalties from Lippincott Williams & Wilkins and McGraw-Hill Education for writing and/or editing several books; receives stock options for serving on the board of Acuity Medical Management Systems; receives a yearly stipend for serving on the board of The Doctors Company; serves on the scientific advisory boards for amino.com, PatientSafe Solutions Inc., Twine, and EarlySense (for which he receives stock options); has a small royalty stake in CareWeb, a hospital communication tool developed at UCSF; and holds the Marc and Lynne Benioff Endowed Chair in Hospital Medicine and the Holly Smith Distinguished Professorship in Science and Medicine at UCSF.

The discharge process is a critical bottleneck for efficient patient flow through the hospital. Delayed discharges translate into delays in admissions and other patient transitions, often leading to excess costs, patient dissatisfaction, and even patient harm.^1-3 The emergency department is particularly impacted by these delays; bottlenecks there lead to overcrowding, increased overall hospital length of stay, and increased risks for bad outcomes during hospitalization.²

Academic medical centers in particular may struggle with delayed discharges. In a typical teaching hospital, a team composed of an attending physician and housestaff share responsibility for determining the discharge plan. Additionally, clinical teaching activities may affect the process and quality of discharge.^4-6

The prevalence and causes of delayed discharges vary greatly.^7-9 To improve efficiency around discharge, many hospitals have launched initiatives designed to discharge patients earlier in the day, including goal setting (“discharge by noon”), scheduling discharge appointments, and using quality-improvement methods, such as Lean Methodology (LEAN), to remove inefficiencies within discharge processes.^10-12 However, there are few data on the prevalence and effectiveness of different strategies.

The aim of this study was to survey academic hospitalist and general internal medicine physician leaders to elicit their perspectives on the factors contributing to discharge timing and the relative importance and effectiveness of early-discharge initiatives.

Study Design, Participants, and Oversight

We obtained a list of 115 university-affiliated hospitals associated with a residency program and, in most cases, a medical school from Vizient Inc. (formerly University HealthSystem Consortium), an alliance of academic medical centers and affiliated hospitals. Each member institution submits clinical data to allow for the benchmarking of outcomes to drive transparency and quality improvement.¹³ More than 95% of the nation’s academic medical centers and affiliated hospitals participate in this collaborative. Vizient works with members but does not set nor promote quality metrics, such as discharge timeliness. E-mail addresses for hospital medicine physician leaders (eg, division chief) of major academic medical centers were obtained from each institution via publicly available data (eg, the institution’s website). When an institution did not have a hospital medicine section, we identified the division chief of general internal medicine. The University of California, San Francisco Institutional Review Board approved this study.

Survey Development and Domains

We developed a 30-item survey to evaluate 5 main domains of interest: current discharge practices, degree of prioritization of early discharge on the inpatient service, barriers to timely discharge, prevalence and perceived effectiveness of implemented early-discharge initiatives, and barriers to implementation of early-discharge initiatives.

Respondents were first asked to identify their institutions’ goals for discharge time. They were then asked to compare the priority of early-discharge initiatives to other departmental quality-improvement initiatives, such as reducing 30-day readmissions, improving interpreter use, and improving patient satisfaction. Next, respondents were asked to estimate the degree to which clinical or patient factors contributed to delays in discharge. Respondents were then asked whether specific early-discharge initiatives, such as changes to rounding practices or communication interventions, were implemented at their institutions and, if so, the perceived effectiveness of these initiatives at meeting discharge targets. We piloted the questions locally with physicians and researchers prior to finalizing the survey.

Data Collection

We sent surveys via an online platform (Research Electronic Data Capture).¹⁴ Nonresponders were sent 2 e-mail reminders and then a follow-up telephone call asking them to complete the survey. Only 1 survey per academic medical center was collected. Any respondent who completed the survey within 2 weeks of receiving it was entered to win a Kindle Fire.

Data Analysis

We summarized survey responses using descriptive statistics. Analysis was completed in IBM SPSS version 22 (Armonk, NY).

Survey Respondent and Institutional Characteristics

Of the 115 institutions surveyed, we received 61 responses (response rate of 53%), with 39 (64%) respondents from divisions of hospital medicine and 22 (36%) from divisions of general internal medicine. A majority (n = 53; 87%) stated their medicine services have a combination of teaching (with residents) and nonteaching (without residents) teams. Thirty-nine (64%) reported having daily multidisciplinary rounds.

Early Discharge as a Priority

Forty-seven (77%) institutional representatives strongly agreed or agreed that early discharge was a priority, with discharge by noon being the most common target time (n = 23; 38%). Thirty (50%) respondents rated early discharge as more important than improving interpreter use for non-English-speaking patients and equally important as reducing 30-day readmissions (n = 29; 48%) and improving patient satisfaction (n = 27; 44%).

Factors Delaying Discharge

The most common factors perceived as delaying discharge were considered external to the hospital, such as postacute care bed availability or scheduled (eg, ambulance) transport delays (n = 48; 79%), followed by patient factors such as patient transport issues (n = 44; 72%). Less commonly reported were workflow issues, such as competing primary team priorities or case manager bandwidth (n = 38; 62%; Table 1).

Initiatives to Improve Discharge

The most commonly implemented initiatives perceived as effective at improving discharge times were the preemptive identification of early discharges to plan discharge paperwork (n = 34; 56%), communication with patients about anticipated discharge time on the day prior to discharge (n = 29; 48%), and the implementation of additional rounds between physician teams and case managers specifically around discharge planning (n = 28; 46%). Initiatives not commonly implemented included regular audit of and feedback on discharge times to providers and teams (n = 21; 34%), the use of a discharge readiness checklist (n = 26; 43%), incentives such as bonuses or penalties (n = 37; 61%), the use of a whiteboard to indicate discharge times (n = 23; 38%), and dedicated quality-improvement approaches such as LEAN (n = 37; 61%; Table 2).

Our study suggests early discharge for medicine patients is a priority among academic institutions. Hospitalist and general internal medicine physician leaders in our study generally attributed delayed discharges to external factors, particularly unavailability of postacute care facilities and transportation delays. Having issues with finding postacute care placements is consistent with previous findings by Selker et al.¹⁵ and Carey et al.⁸ This is despite the 20-year difference between Selker et al.’s study and the current study, reflecting a continued opportunity for improvement, including stronger partnerships with local and regional postacute care facilities to expedite care transition and stronger discharge-planning efforts early in the admission process. Efforts in postacute care placement may be particularly important for Medicaid-insured and uninsured patients.

Our responders, hospitalist and internal medicine physician leaders, did not perceive the additional responsibilities of teaching and supervising trainees to be factors that significantly delayed patient discharge. This is in contrast to previous studies, which attributed delays in discharge to prolonged clinical decision-making related to teaching and supervision.^4-6,8 This discrepancy may be due to the fact that we only surveyed single physician leaders at each institution and not residents. Our finding warrants further investigation to understand the degree to which resident skills may impact discharge planning and processes.

Institutions represented in our study have attempted a variety of initiatives promoting earlier discharge, with varying levels of perceived success. Initiatives perceived to be the most effective by hospital leaders centered on 2 main areas: (1) changing individual provider practice and (2) anticipatory discharge preparation. Interestingly, this is in discordance with the main factors labeled as causing delays in discharges, such as obtaining postacute care beds, busy case managers, and competing demands on primary teams. We hypothesize this may be because such changes require organization- or system-level changes and are perceived as more arduous than changes at the individual level. In addition, changes to individual provider behavior may be more cost- and time-effective than more systemic initiatives.

Our findings are consistent with the work published by Wertheimer and colleagues,¹¹ who show that additional afternoon interdisciplinary rounds can help identify patients who may be discharged before noon the next day. In their study, identifying such patients in advance improved the overall early-discharge rate the following day.

Our findings should be interpreted in light of several limitations. Our survey only considers the perspectives of hospitalist and general internal medicine physician leaders at academic medical centers that are part of the Vizient Inc. collaborative. They do not represent all academic or community-based medical centers. Although the perceived effectiveness of some initiatives was high, we did not collect empirical data to support these claims or to determine which initiative had the greatest relative impact on discharge timeliness. Lastly, we did not obtain resident, nursing, or case manager perspectives on discharge practices. Given their roles as frontline providers, we may have missed these alternative perspectives.

Our study shows there is a strong interest in increasing early discharges in an effort to improve hospital throughput and patient flow.

Acknowledgments

The authors thank all participants who completed the survey and Danielle Carrier at Vizient Inc. (formally University HealthSystem Consortium) for her assistance in obtaining data.

Disclosures

Hemali Patel, Margaret Fang, Michelle Mourad, Adrienne Green, Ryan Murphy, and James Harrison report no conflicts of interest. At the time the research was conducted, Robert Wachter reported that he is a member of the Lucian Leape Institute at the National Patient Safety Foundation (no compensation except travel expenses); recently chaired an advisory board to England’s National Health Service (NHS) reviewing the NHS’s digital health strategy (no compensation except travel expenses); has a contract with UCSF from the Agency for Healthcare Research and Quality to edit a patient-safety website; receives compensation from John Wiley & Sons for writing a blog; receives royalties from Lippincott Williams & Wilkins and McGraw-Hill Education for writing and/or editing several books; receives stock options for serving on the board of Acuity Medical Management Systems; receives a yearly stipend for serving on the board of The Doctors Company; serves on the scientific advisory boards for amino.com, PatientSafe Solutions Inc., Twine, and EarlySense (for which he receives stock options); has a small royalty stake in CareWeb, a hospital communication tool developed at UCSF; and holds the Marc and Lynne Benioff Endowed Chair in Hospital Medicine and the Holly Smith Distinguished Professorship in Science and Medicine at UCSF.

The discharge process is a critical bottleneck for efficient patient flow through the hospital. Delayed discharges translate into delays in admissions and other patient transitions, often leading to excess costs, patient dissatisfaction, and even patient harm.^1-3 The emergency department is particularly impacted by these delays; bottlenecks there lead to overcrowding, increased overall hospital length of stay, and increased risks for bad outcomes during hospitalization.²

Academic medical centers in particular may struggle with delayed discharges. In a typical teaching hospital, a team composed of an attending physician and housestaff share responsibility for determining the discharge plan. Additionally, clinical teaching activities may affect the process and quality of discharge.^4-6

The prevalence and causes of delayed discharges vary greatly.^7-9 To improve efficiency around discharge, many hospitals have launched initiatives designed to discharge patients earlier in the day, including goal setting (“discharge by noon”), scheduling discharge appointments, and using quality-improvement methods, such as Lean Methodology (LEAN), to remove inefficiencies within discharge processes.^10-12 However, there are few data on the prevalence and effectiveness of different strategies.

The aim of this study was to survey academic hospitalist and general internal medicine physician leaders to elicit their perspectives on the factors contributing to discharge timing and the relative importance and effectiveness of early-discharge initiatives.

Study Design, Participants, and Oversight

We obtained a list of 115 university-affiliated hospitals associated with a residency program and, in most cases, a medical school from Vizient Inc. (formerly University HealthSystem Consortium), an alliance of academic medical centers and affiliated hospitals. Each member institution submits clinical data to allow for the benchmarking of outcomes to drive transparency and quality improvement.¹³ More than 95% of the nation’s academic medical centers and affiliated hospitals participate in this collaborative. Vizient works with members but does not set nor promote quality metrics, such as discharge timeliness. E-mail addresses for hospital medicine physician leaders (eg, division chief) of major academic medical centers were obtained from each institution via publicly available data (eg, the institution’s website). When an institution did not have a hospital medicine section, we identified the division chief of general internal medicine. The University of California, San Francisco Institutional Review Board approved this study.

Survey Development and Domains

We developed a 30-item survey to evaluate 5 main domains of interest: current discharge practices, degree of prioritization of early discharge on the inpatient service, barriers to timely discharge, prevalence and perceived effectiveness of implemented early-discharge initiatives, and barriers to implementation of early-discharge initiatives.

Respondents were first asked to identify their institutions’ goals for discharge time. They were then asked to compare the priority of early-discharge initiatives to other departmental quality-improvement initiatives, such as reducing 30-day readmissions, improving interpreter use, and improving patient satisfaction. Next, respondents were asked to estimate the degree to which clinical or patient factors contributed to delays in discharge. Respondents were then asked whether specific early-discharge initiatives, such as changes to rounding practices or communication interventions, were implemented at their institutions and, if so, the perceived effectiveness of these initiatives at meeting discharge targets. We piloted the questions locally with physicians and researchers prior to finalizing the survey.

Data Collection

We sent surveys via an online platform (Research Electronic Data Capture).¹⁴ Nonresponders were sent 2 e-mail reminders and then a follow-up telephone call asking them to complete the survey. Only 1 survey per academic medical center was collected. Any respondent who completed the survey within 2 weeks of receiving it was entered to win a Kindle Fire.

Data Analysis

We summarized survey responses using descriptive statistics. Analysis was completed in IBM SPSS version 22 (Armonk, NY).

Survey Respondent and Institutional Characteristics

Of the 115 institutions surveyed, we received 61 responses (response rate of 53%), with 39 (64%) respondents from divisions of hospital medicine and 22 (36%) from divisions of general internal medicine. A majority (n = 53; 87%) stated their medicine services have a combination of teaching (with residents) and nonteaching (without residents) teams. Thirty-nine (64%) reported having daily multidisciplinary rounds.

Early Discharge as a Priority

Forty-seven (77%) institutional representatives strongly agreed or agreed that early discharge was a priority, with discharge by noon being the most common target time (n = 23; 38%). Thirty (50%) respondents rated early discharge as more important than improving interpreter use for non-English-speaking patients and equally important as reducing 30-day readmissions (n = 29; 48%) and improving patient satisfaction (n = 27; 44%).

Factors Delaying Discharge

The most common factors perceived as delaying discharge were considered external to the hospital, such as postacute care bed availability or scheduled (eg, ambulance) transport delays (n = 48; 79%), followed by patient factors such as patient transport issues (n = 44; 72%). Less commonly reported were workflow issues, such as competing primary team priorities or case manager bandwidth (n = 38; 62%; Table 1).

Initiatives to Improve Discharge

The most commonly implemented initiatives perceived as effective at improving discharge times were the preemptive identification of early discharges to plan discharge paperwork (n = 34; 56%), communication with patients about anticipated discharge time on the day prior to discharge (n = 29; 48%), and the implementation of additional rounds between physician teams and case managers specifically around discharge planning (n = 28; 46%). Initiatives not commonly implemented included regular audit of and feedback on discharge times to providers and teams (n = 21; 34%), the use of a discharge readiness checklist (n = 26; 43%), incentives such as bonuses or penalties (n = 37; 61%), the use of a whiteboard to indicate discharge times (n = 23; 38%), and dedicated quality-improvement approaches such as LEAN (n = 37; 61%; Table 2).

Our study suggests early discharge for medicine patients is a priority among academic institutions. Hospitalist and general internal medicine physician leaders in our study generally attributed delayed discharges to external factors, particularly unavailability of postacute care facilities and transportation delays. Having issues with finding postacute care placements is consistent with previous findings by Selker et al.¹⁵ and Carey et al.⁸ This is despite the 20-year difference between Selker et al.’s study and the current study, reflecting a continued opportunity for improvement, including stronger partnerships with local and regional postacute care facilities to expedite care transition and stronger discharge-planning efforts early in the admission process. Efforts in postacute care placement may be particularly important for Medicaid-insured and uninsured patients.

Our responders, hospitalist and internal medicine physician leaders, did not perceive the additional responsibilities of teaching and supervising trainees to be factors that significantly delayed patient discharge. This is in contrast to previous studies, which attributed delays in discharge to prolonged clinical decision-making related to teaching and supervision.^4-6,8 This discrepancy may be due to the fact that we only surveyed single physician leaders at each institution and not residents. Our finding warrants further investigation to understand the degree to which resident skills may impact discharge planning and processes.

Institutions represented in our study have attempted a variety of initiatives promoting earlier discharge, with varying levels of perceived success. Initiatives perceived to be the most effective by hospital leaders centered on 2 main areas: (1) changing individual provider practice and (2) anticipatory discharge preparation. Interestingly, this is in discordance with the main factors labeled as causing delays in discharges, such as obtaining postacute care beds, busy case managers, and competing demands on primary teams. We hypothesize this may be because such changes require organization- or system-level changes and are perceived as more arduous than changes at the individual level. In addition, changes to individual provider behavior may be more cost- and time-effective than more systemic initiatives.

Our findings are consistent with the work published by Wertheimer and colleagues,¹¹ who show that additional afternoon interdisciplinary rounds can help identify patients who may be discharged before noon the next day. In their study, identifying such patients in advance improved the overall early-discharge rate the following day.

Our findings should be interpreted in light of several limitations. Our survey only considers the perspectives of hospitalist and general internal medicine physician leaders at academic medical centers that are part of the Vizient Inc. collaborative. They do not represent all academic or community-based medical centers. Although the perceived effectiveness of some initiatives was high, we did not collect empirical data to support these claims or to determine which initiative had the greatest relative impact on discharge timeliness. Lastly, we did not obtain resident, nursing, or case manager perspectives on discharge practices. Given their roles as frontline providers, we may have missed these alternative perspectives.

Our study shows there is a strong interest in increasing early discharges in an effort to improve hospital throughput and patient flow.

Acknowledgments

The authors thank all participants who completed the survey and Danielle Carrier at Vizient Inc. (formally University HealthSystem Consortium) for her assistance in obtaining data.

Disclosures

Hemali Patel, Margaret Fang, Michelle Mourad, Adrienne Green, Ryan Murphy, and James Harrison report no conflicts of interest. At the time the research was conducted, Robert Wachter reported that he is a member of the Lucian Leape Institute at the National Patient Safety Foundation (no compensation except travel expenses); recently chaired an advisory board to England’s National Health Service (NHS) reviewing the NHS’s digital health strategy (no compensation except travel expenses); has a contract with UCSF from the Agency for Healthcare Research and Quality to edit a patient-safety website; receives compensation from John Wiley & Sons for writing a blog; receives royalties from Lippincott Williams & Wilkins and McGraw-Hill Education for writing and/or editing several books; receives stock options for serving on the board of Acuity Medical Management Systems; receives a yearly stipend for serving on the board of The Doctors Company; serves on the scientific advisory boards for amino.com, PatientSafe Solutions Inc., Twine, and EarlySense (for which he receives stock options); has a small royalty stake in CareWeb, a hospital communication tool developed at UCSF; and holds the Marc and Lynne Benioff Endowed Chair in Hospital Medicine and the Holly Smith Distinguished Professorship in Science and Medicine at UCSF.

Admission to a hospital can be a stressful event,^1,2 and patients report having many concerns at the time of hospital admission.³ Over the last 20 years, the United States has widely adopted the hospitalist model of inpatient care. Although this model has clear benefits, it also has the potential to contribute to patient stress, as hospitalized patients generally lack preexisting relationships with their inpatient physicians.^4,5 In this changing hospital environment, defining and promoting effective medical communication has become an essential goal of both individual practitioners and medical centers.

Successful communication and strong therapeutic relationships with physicians support patients’ coping with illness-associated stress^6,7 as well as promote adherence to medical treatment plans.⁸ Empathy serves as an important building block of patient-centered communication and encourages a strong therapeutic alliance.⁹ Studies from primary care, oncology, and intensive care unit (ICU) settings indicate that physician empathy is associated with decreased emotional distress,^10,11 improved ratings of communication,¹² and even better medical outcomes.¹³

Prior work has shown that hospitalists, like other clinicians, underutilize empathy as a tool in their daily interactions with patients.^14-16 Our prior qualitative analysis of audio-recorded hospitalist-patient admission encounters indicated that how hospitalists respond to patient expressions of negative emotion influences relationships with patients and alignment around care plans.¹⁷ To determine whether empathic communication is associated with patient-reported outcomes in the hospitalist model, we quantitatively analyzed coded admission encounters and survey data to examine the association between hospitalists’ responses to patient expressions of negative emotion (anxiety, sadness, and anger) and patient anxiety and ratings of communication. Given the often-limited time hospitalists have to complete admission encounters, we also examined the association between response to emotion and encounter length.

We analyzed data collected as part of an observational study of hospitalist-patient communication during hospital admission encounters¹⁴ to assess the association between the way physicians responded to patient expressions of negative emotion and patient anxiety, ratings of communication in the encounter, and encounter length. We collected data between August 2008 and March 2009 on the general medical service at 2 urban hospitals that are part of an academic medical center. Participants were attending hospitalists (not physician trainees), and patients admitted under participating hospitalists’ care who were able to communicate verbally in English and provide informed consent for the study. The institutional review board at the University of California, San Francisco approved the study; physician and patient participants provided written informed consent.

Enrollment and data collection has been described previously.¹⁷ Our cohort for this analysis included 76 patients of 27 physicians who completed encounter audio recordings and pre- and postencounter surveys. Following enrollment, patients completed a preencounter survey to collect demographic information and to measure their baseline anxiety via the State Anxiety Scale (STAI-S), which assesses transient anxious mood using 20 items answered on a 4-point scale for a final score range of 20 to 80.^10,18,19 We timed and audio-recorded admission encounters. Encounter recordings were obtained solely from patient interactions with attending hospitalists and did not take into account the time patients may have spent with other physicians, including trainees. After the encounter, patients completed postencounter surveys, which included the STAI-S and patients’ ratings of communication during the encounter. To rate communication, patients responded to 7 items on a 0- to 10-point scale that were derived from previous work (Table 1)^12,20,21; the anchors were “not at all” and “completely.” To identify patients with serious illness, which we used as a covariate in regression models, we asked physicians on a postencounter survey whether or not they “would be surprised by this patient’s death or admission to the ICU in the next year.”²²

We analyzed data from admission encounters with 76 patients (consent rate 63%) and 27 hospitalists (consent rate 91%). Their characteristics are shown in Table 3. Median encounter length was 19 minutes (mean 21 minutes, range 3-68). Patients expressed negative emotion in 190 instances across all encounters; median number of expressions per encounter was 1 (range 0-14). Hospitalists responded empathically to 32% (n = 61) of the patient expressions, neutrally to 43% (n = 81), and nonempathically to 25% (n = 48).

The STAI-S was normally distributed. The mean preencounter STAI-S score was 39 (standard deviation [SD] 8.9). Mean postencounter STAI-S score was 38 (SD 10.7). Mean change in anxiety over the course of the encounter, calculated as the postencounter minus preencounter mean was −1.2 (SD 7.6). Table 1 shows summary statistics for the patient ratings of communication items. All items were rated highly. Across the items, between 51% and 78% of patients rated the highest score of 10.

Across the range of frequencies of emotional expressions per encounter in our data set (0-14 expressions), each additional empathic hospitalist response was associated with a 1.65-point decrease in the STAI-S (95% confidence interval [CI], 0.48-2.82). We did not find significant associations between changes in the STAI-S and the number of neutral hospitalist responses (−0.65 per response; 95% CI, −1.67-0.37) or nonempathic hospitalist responses (0.61 per response; 95% CI, −0.88-2.10).

Our study set out to measure how hospitalists responded to expressions of negative emotion during admission encounters with patients and how those responses correlated with patient anxiety, ratings of communication, and encounter length. We found that empathic responses were associated with diminishing patient anxiety after the visit, as well as with better ratings of several domains of hospitalist communication. Moreover, nonempathic responses to negative emotion were associated with more strongly negative ratings of hospitalist communication. Finally, while clinicians may worry that encouraging patients to speak further about emotion will result in excessive visit lengths, we did not find a statistical association between empathic responses and encounter duration. To our knowledge, this is the first study to indicate an association between empathy and patient anxiety and communication ratings within the hospitalist model, which is rapidly becoming the predominant model for providing inpatient care in the United States.^4,5

As in oncologic care, anxiety is an emotion commonly confronted by clinicians meeting admitted medical patients for the first time. Studies show that not only do patient anxiety levels remain high throughout a hospital course, patients who experience higher levels of anxiety tend to stay longer in the hospital.^1,2,27-30 But unlike oncologic care or other therapy provided in an outpatient setting, the hospitalist model does not facilitate “continuity” of care, or the ability to care for the same patients over a long period of time. This reality of inpatient care makes rapid, effective rapport-building critical to establishing strong physician-patient relationships. In this setting, a simple communication tool that is potentially able to reduce inpatients’ anxiety could have a meaningful impact on hospitalist-provided care and patient outcomes.

In terms of the magnitude of the effect of empathic responses, the clinical significance of a 1.65-point decrease in the STAI-S anxiety score is not precisely clear. A prior study that examined the effect of music therapy on anxiety levels in patients with cancer found an average anxiety reduction of approximately 9.5 units on the STAIS-S scale after sensitivity analysis, suggesting a rather large meaningful effect size.³¹ Given we found a reduction of 1.65 points for each empathic response, however, with a range of 0-14 negative emotions expressed over a median 19-minute encounter, there is opportunity for hospitalists to achieve a clinically significant decrease in patient anxiety during an admission encounter. The potential to reduce anxiety is extended further when we consider that the impact of an empathic response may apply not just to the admission encounter alone but also to numerous other patient-clinician interactions over the course of a hospitalization.

A healthy body of communication research supports the associations we found in our study between empathy and patient ratings of communication and physicians. Families in ICU conferences rate communication more positively when physicians express empathy,¹² and a number of studies indicate an association between empathy and patient satisfaction in outpatient settings.⁸ Given the associations we found with negative ratings on the items in our study, promoting empathic responses to expressions of emotion and, more importantly, stressing avoidance of nonempathic responses may be relevant efforts in working to improve patient satisfaction scores on surveys reporting “top box” percentages, such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). More notably, evidence indicates that empathy has positive impacts beyond satisfaction surveys, such as adherence, better diagnostic and clinical outcomes, and strengthening of patient enablement.⁸Not all hospitalist responses to emotion were associated with patient ratings across the 7 communication items we assessed. For example, we did not find an association between how physicians responded to patient expressions of negative emotion and patient perception that enough time was spent in the visit or the degree to which talking with the doctor met a patient’s overall needs. It follows logically, and other research supports, that empathy would influence patient ratings of physician caring and trust,³² whereas other communication factors we were unable to measure (eg, physician body language, tone, and use of jargon and patient health literacy and primary language) may have a more significant association with patient ratings of the other items we assessed.

In considering the clinical application of our results, it is important to note that communication skills, including responding empathically to patient expressions of negative emotion, can be imparted through training in the same way as abdominal examination or electrocardiogram interpretation skills.^33-35 However, training of hospitalists in communication skills requires time and some financial investment on the part of the physician, their hospital or group, or, ideally, both. Effective training methods, like those for other skill acquisition, involve learner-centered teaching and practicing skills with role-play and feedback.³⁶ Given the importance of a learner-centered approach, learning would likely be better received and more effective if it was tailored to the specific needs and patient scenarios commonly encountered by hospitalist physicians. As these programs are developed, it will be important to assess the impact of any training on the patient-reported outcomes we assessed in this observational study, along with clinical outcomes.

Our study has several limitations. First, we were only able to evaluate whether hospitalists verbally responded to patient emotion and were thus not able to account for nonverbal empathy such as facial expressions, body language, or voice tone. Second, given our patient consent rate of 63%, patients who agreed to participate in the study may have had different opinions than those who declined to participate. Also, hospitalists and patients may have behaved differently as a result of being audio recorded. We only included patients who spoke English, and our patient population was predominately non-Hispanic white. Patients who spoke other languages or came from other cultural backgrounds may have had different responses. Third, we did not use a single validated scale for patient ratings of communication, and multiple analyses increase our risk of finding statistically significant associations by chance. The skewing of the communication rating items toward high scores may also have led to our results being driven by outliers, although the model we chose for analysis does penalize for this. Furthermore, our sample size was small, leading to wide CIs and potential for lack of statistical associations due to insufficient power. Our findings warrant replication in larger studies. Fourth, the setting of our study in an academic center may affect generalizability. Finally, the age of our data (collected between 2008 and 2009) is also a limitation. Given a recent focus on communication and patient experience since the initiation of HCAHPS feedback, a similar analysis of empathy and communication methods now may result in different outcomes.

In conclusion, our results suggest that enhancing hospitalists’ empathic responses to patient expressions of negative emotion could decrease patient anxiety and improve patients’ perceptions of (and thus possibly their relationships with) hospitalists, without sacrificing efficiency. Future work should focus on tailoring and implementing communication skills training programs for hospitalists and evaluating the impact of training on patient outcomes.

Acknowledgments

The authors extend their sincere thanks to the patients and physicians who participated in this study. Dr. Anderson was funded by the National Palliative Care Research Center and the University of California, San Francisco Clinical and Translational Science Institute Career Development Program, National Institutes of Health (NIH) grant number 5 KL2 RR024130-04. Project costs were funded by a grant from the University of California, San Francisco Academic Senate.

Disclosure

 All coauthors have seen and agree with the contents of this manuscript. This submission is not under review by any other publication. Wendy Anderson received funding for this project from the National Palliative Care Research Center, University of California San Francisco Clinical and Translational Science Institute (NIH grant number 5KL2RR024130-04), and the University of San Francisco Academic Senate [From Section 2 of Author Disclosure Form]. Andy Auerbach has a Patient-Centered Outcomes Research Institute research grant in development [From Section 3 of the Author Disclosure Form].

Admission to a hospital can be a stressful event,^1,2 and patients report having many concerns at the time of hospital admission.³ Over the last 20 years, the United States has widely adopted the hospitalist model of inpatient care. Although this model has clear benefits, it also has the potential to contribute to patient stress, as hospitalized patients generally lack preexisting relationships with their inpatient physicians.^4,5 In this changing hospital environment, defining and promoting effective medical communication has become an essential goal of both individual practitioners and medical centers.

Successful communication and strong therapeutic relationships with physicians support patients’ coping with illness-associated stress^6,7 as well as promote adherence to medical treatment plans.⁸ Empathy serves as an important building block of patient-centered communication and encourages a strong therapeutic alliance.⁹ Studies from primary care, oncology, and intensive care unit (ICU) settings indicate that physician empathy is associated with decreased emotional distress,^10,11 improved ratings of communication,¹² and even better medical outcomes.¹³

Prior work has shown that hospitalists, like other clinicians, underutilize empathy as a tool in their daily interactions with patients.^14-16 Our prior qualitative analysis of audio-recorded hospitalist-patient admission encounters indicated that how hospitalists respond to patient expressions of negative emotion influences relationships with patients and alignment around care plans.¹⁷ To determine whether empathic communication is associated with patient-reported outcomes in the hospitalist model, we quantitatively analyzed coded admission encounters and survey data to examine the association between hospitalists’ responses to patient expressions of negative emotion (anxiety, sadness, and anger) and patient anxiety and ratings of communication. Given the often-limited time hospitalists have to complete admission encounters, we also examined the association between response to emotion and encounter length.

We analyzed data collected as part of an observational study of hospitalist-patient communication during hospital admission encounters¹⁴ to assess the association between the way physicians responded to patient expressions of negative emotion and patient anxiety, ratings of communication in the encounter, and encounter length. We collected data between August 2008 and March 2009 on the general medical service at 2 urban hospitals that are part of an academic medical center. Participants were attending hospitalists (not physician trainees), and patients admitted under participating hospitalists’ care who were able to communicate verbally in English and provide informed consent for the study. The institutional review board at the University of California, San Francisco approved the study; physician and patient participants provided written informed consent.

Enrollment and data collection has been described previously.¹⁷ Our cohort for this analysis included 76 patients of 27 physicians who completed encounter audio recordings and pre- and postencounter surveys. Following enrollment, patients completed a preencounter survey to collect demographic information and to measure their baseline anxiety via the State Anxiety Scale (STAI-S), which assesses transient anxious mood using 20 items answered on a 4-point scale for a final score range of 20 to 80.^10,18,19 We timed and audio-recorded admission encounters. Encounter recordings were obtained solely from patient interactions with attending hospitalists and did not take into account the time patients may have spent with other physicians, including trainees. After the encounter, patients completed postencounter surveys, which included the STAI-S and patients’ ratings of communication during the encounter. To rate communication, patients responded to 7 items on a 0- to 10-point scale that were derived from previous work (Table 1)^12,20,21; the anchors were “not at all” and “completely.” To identify patients with serious illness, which we used as a covariate in regression models, we asked physicians on a postencounter survey whether or not they “would be surprised by this patient’s death or admission to the ICU in the next year.”²²

We analyzed data from admission encounters with 76 patients (consent rate 63%) and 27 hospitalists (consent rate 91%). Their characteristics are shown in Table 3. Median encounter length was 19 minutes (mean 21 minutes, range 3-68). Patients expressed negative emotion in 190 instances across all encounters; median number of expressions per encounter was 1 (range 0-14). Hospitalists responded empathically to 32% (n = 61) of the patient expressions, neutrally to 43% (n = 81), and nonempathically to 25% (n = 48).

The STAI-S was normally distributed. The mean preencounter STAI-S score was 39 (standard deviation [SD] 8.9). Mean postencounter STAI-S score was 38 (SD 10.7). Mean change in anxiety over the course of the encounter, calculated as the postencounter minus preencounter mean was −1.2 (SD 7.6). Table 1 shows summary statistics for the patient ratings of communication items. All items were rated highly. Across the items, between 51% and 78% of patients rated the highest score of 10.

Across the range of frequencies of emotional expressions per encounter in our data set (0-14 expressions), each additional empathic hospitalist response was associated with a 1.65-point decrease in the STAI-S (95% confidence interval [CI], 0.48-2.82). We did not find significant associations between changes in the STAI-S and the number of neutral hospitalist responses (−0.65 per response; 95% CI, −1.67-0.37) or nonempathic hospitalist responses (0.61 per response; 95% CI, −0.88-2.10).

Our study set out to measure how hospitalists responded to expressions of negative emotion during admission encounters with patients and how those responses correlated with patient anxiety, ratings of communication, and encounter length. We found that empathic responses were associated with diminishing patient anxiety after the visit, as well as with better ratings of several domains of hospitalist communication. Moreover, nonempathic responses to negative emotion were associated with more strongly negative ratings of hospitalist communication. Finally, while clinicians may worry that encouraging patients to speak further about emotion will result in excessive visit lengths, we did not find a statistical association between empathic responses and encounter duration. To our knowledge, this is the first study to indicate an association between empathy and patient anxiety and communication ratings within the hospitalist model, which is rapidly becoming the predominant model for providing inpatient care in the United States.^4,5

As in oncologic care, anxiety is an emotion commonly confronted by clinicians meeting admitted medical patients for the first time. Studies show that not only do patient anxiety levels remain high throughout a hospital course, patients who experience higher levels of anxiety tend to stay longer in the hospital.^1,2,27-30 But unlike oncologic care or other therapy provided in an outpatient setting, the hospitalist model does not facilitate “continuity” of care, or the ability to care for the same patients over a long period of time. This reality of inpatient care makes rapid, effective rapport-building critical to establishing strong physician-patient relationships. In this setting, a simple communication tool that is potentially able to reduce inpatients’ anxiety could have a meaningful impact on hospitalist-provided care and patient outcomes.

In terms of the magnitude of the effect of empathic responses, the clinical significance of a 1.65-point decrease in the STAI-S anxiety score is not precisely clear. A prior study that examined the effect of music therapy on anxiety levels in patients with cancer found an average anxiety reduction of approximately 9.5 units on the STAIS-S scale after sensitivity analysis, suggesting a rather large meaningful effect size.³¹ Given we found a reduction of 1.65 points for each empathic response, however, with a range of 0-14 negative emotions expressed over a median 19-minute encounter, there is opportunity for hospitalists to achieve a clinically significant decrease in patient anxiety during an admission encounter. The potential to reduce anxiety is extended further when we consider that the impact of an empathic response may apply not just to the admission encounter alone but also to numerous other patient-clinician interactions over the course of a hospitalization.

A healthy body of communication research supports the associations we found in our study between empathy and patient ratings of communication and physicians. Families in ICU conferences rate communication more positively when physicians express empathy,¹² and a number of studies indicate an association between empathy and patient satisfaction in outpatient settings.⁸ Given the associations we found with negative ratings on the items in our study, promoting empathic responses to expressions of emotion and, more importantly, stressing avoidance of nonempathic responses may be relevant efforts in working to improve patient satisfaction scores on surveys reporting “top box” percentages, such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). More notably, evidence indicates that empathy has positive impacts beyond satisfaction surveys, such as adherence, better diagnostic and clinical outcomes, and strengthening of patient enablement.⁸Not all hospitalist responses to emotion were associated with patient ratings across the 7 communication items we assessed. For example, we did not find an association between how physicians responded to patient expressions of negative emotion and patient perception that enough time was spent in the visit or the degree to which talking with the doctor met a patient’s overall needs. It follows logically, and other research supports, that empathy would influence patient ratings of physician caring and trust,³² whereas other communication factors we were unable to measure (eg, physician body language, tone, and use of jargon and patient health literacy and primary language) may have a more significant association with patient ratings of the other items we assessed.

In considering the clinical application of our results, it is important to note that communication skills, including responding empathically to patient expressions of negative emotion, can be imparted through training in the same way as abdominal examination or electrocardiogram interpretation skills.^33-35 However, training of hospitalists in communication skills requires time and some financial investment on the part of the physician, their hospital or group, or, ideally, both. Effective training methods, like those for other skill acquisition, involve learner-centered teaching and practicing skills with role-play and feedback.³⁶ Given the importance of a learner-centered approach, learning would likely be better received and more effective if it was tailored to the specific needs and patient scenarios commonly encountered by hospitalist physicians. As these programs are developed, it will be important to assess the impact of any training on the patient-reported outcomes we assessed in this observational study, along with clinical outcomes.

Our study has several limitations. First, we were only able to evaluate whether hospitalists verbally responded to patient emotion and were thus not able to account for nonverbal empathy such as facial expressions, body language, or voice tone. Second, given our patient consent rate of 63%, patients who agreed to participate in the study may have had different opinions than those who declined to participate. Also, hospitalists and patients may have behaved differently as a result of being audio recorded. We only included patients who spoke English, and our patient population was predominately non-Hispanic white. Patients who spoke other languages or came from other cultural backgrounds may have had different responses. Third, we did not use a single validated scale for patient ratings of communication, and multiple analyses increase our risk of finding statistically significant associations by chance. The skewing of the communication rating items toward high scores may also have led to our results being driven by outliers, although the model we chose for analysis does penalize for this. Furthermore, our sample size was small, leading to wide CIs and potential for lack of statistical associations due to insufficient power. Our findings warrant replication in larger studies. Fourth, the setting of our study in an academic center may affect generalizability. Finally, the age of our data (collected between 2008 and 2009) is also a limitation. Given a recent focus on communication and patient experience since the initiation of HCAHPS feedback, a similar analysis of empathy and communication methods now may result in different outcomes.

In conclusion, our results suggest that enhancing hospitalists’ empathic responses to patient expressions of negative emotion could decrease patient anxiety and improve patients’ perceptions of (and thus possibly their relationships with) hospitalists, without sacrificing efficiency. Future work should focus on tailoring and implementing communication skills training programs for hospitalists and evaluating the impact of training on patient outcomes.

Acknowledgments

The authors extend their sincere thanks to the patients and physicians who participated in this study. Dr. Anderson was funded by the National Palliative Care Research Center and the University of California, San Francisco Clinical and Translational Science Institute Career Development Program, National Institutes of Health (NIH) grant number 5 KL2 RR024130-04. Project costs were funded by a grant from the University of California, San Francisco Academic Senate.

Disclosure

 All coauthors have seen and agree with the contents of this manuscript. This submission is not under review by any other publication. Wendy Anderson received funding for this project from the National Palliative Care Research Center, University of California San Francisco Clinical and Translational Science Institute (NIH grant number 5KL2RR024130-04), and the University of San Francisco Academic Senate [From Section 2 of Author Disclosure Form]. Andy Auerbach has a Patient-Centered Outcomes Research Institute research grant in development [From Section 3 of the Author Disclosure Form].

Admission to a hospital can be a stressful event,^1,2 and patients report having many concerns at the time of hospital admission.³ Over the last 20 years, the United States has widely adopted the hospitalist model of inpatient care. Although this model has clear benefits, it also has the potential to contribute to patient stress, as hospitalized patients generally lack preexisting relationships with their inpatient physicians.^4,5 In this changing hospital environment, defining and promoting effective medical communication has become an essential goal of both individual practitioners and medical centers.

Successful communication and strong therapeutic relationships with physicians support patients’ coping with illness-associated stress^6,7 as well as promote adherence to medical treatment plans.⁸ Empathy serves as an important building block of patient-centered communication and encourages a strong therapeutic alliance.⁹ Studies from primary care, oncology, and intensive care unit (ICU) settings indicate that physician empathy is associated with decreased emotional distress,^10,11 improved ratings of communication,¹² and even better medical outcomes.¹³

Prior work has shown that hospitalists, like other clinicians, underutilize empathy as a tool in their daily interactions with patients.^14-16 Our prior qualitative analysis of audio-recorded hospitalist-patient admission encounters indicated that how hospitalists respond to patient expressions of negative emotion influences relationships with patients and alignment around care plans.¹⁷ To determine whether empathic communication is associated with patient-reported outcomes in the hospitalist model, we quantitatively analyzed coded admission encounters and survey data to examine the association between hospitalists’ responses to patient expressions of negative emotion (anxiety, sadness, and anger) and patient anxiety and ratings of communication. Given the often-limited time hospitalists have to complete admission encounters, we also examined the association between response to emotion and encounter length.

We analyzed data collected as part of an observational study of hospitalist-patient communication during hospital admission encounters¹⁴ to assess the association between the way physicians responded to patient expressions of negative emotion and patient anxiety, ratings of communication in the encounter, and encounter length. We collected data between August 2008 and March 2009 on the general medical service at 2 urban hospitals that are part of an academic medical center. Participants were attending hospitalists (not physician trainees), and patients admitted under participating hospitalists’ care who were able to communicate verbally in English and provide informed consent for the study. The institutional review board at the University of California, San Francisco approved the study; physician and patient participants provided written informed consent.

Enrollment and data collection has been described previously.¹⁷ Our cohort for this analysis included 76 patients of 27 physicians who completed encounter audio recordings and pre- and postencounter surveys. Following enrollment, patients completed a preencounter survey to collect demographic information and to measure their baseline anxiety via the State Anxiety Scale (STAI-S), which assesses transient anxious mood using 20 items answered on a 4-point scale for a final score range of 20 to 80.^10,18,19 We timed and audio-recorded admission encounters. Encounter recordings were obtained solely from patient interactions with attending hospitalists and did not take into account the time patients may have spent with other physicians, including trainees. After the encounter, patients completed postencounter surveys, which included the STAI-S and patients’ ratings of communication during the encounter. To rate communication, patients responded to 7 items on a 0- to 10-point scale that were derived from previous work (Table 1)^12,20,21; the anchors were “not at all” and “completely.” To identify patients with serious illness, which we used as a covariate in regression models, we asked physicians on a postencounter survey whether or not they “would be surprised by this patient’s death or admission to the ICU in the next year.”²²

We analyzed data from admission encounters with 76 patients (consent rate 63%) and 27 hospitalists (consent rate 91%). Their characteristics are shown in Table 3. Median encounter length was 19 minutes (mean 21 minutes, range 3-68). Patients expressed negative emotion in 190 instances across all encounters; median number of expressions per encounter was 1 (range 0-14). Hospitalists responded empathically to 32% (n = 61) of the patient expressions, neutrally to 43% (n = 81), and nonempathically to 25% (n = 48).

The STAI-S was normally distributed. The mean preencounter STAI-S score was 39 (standard deviation [SD] 8.9). Mean postencounter STAI-S score was 38 (SD 10.7). Mean change in anxiety over the course of the encounter, calculated as the postencounter minus preencounter mean was −1.2 (SD 7.6). Table 1 shows summary statistics for the patient ratings of communication items. All items were rated highly. Across the items, between 51% and 78% of patients rated the highest score of 10.

Across the range of frequencies of emotional expressions per encounter in our data set (0-14 expressions), each additional empathic hospitalist response was associated with a 1.65-point decrease in the STAI-S (95% confidence interval [CI], 0.48-2.82). We did not find significant associations between changes in the STAI-S and the number of neutral hospitalist responses (−0.65 per response; 95% CI, −1.67-0.37) or nonempathic hospitalist responses (0.61 per response; 95% CI, −0.88-2.10).

Our study set out to measure how hospitalists responded to expressions of negative emotion during admission encounters with patients and how those responses correlated with patient anxiety, ratings of communication, and encounter length. We found that empathic responses were associated with diminishing patient anxiety after the visit, as well as with better ratings of several domains of hospitalist communication. Moreover, nonempathic responses to negative emotion were associated with more strongly negative ratings of hospitalist communication. Finally, while clinicians may worry that encouraging patients to speak further about emotion will result in excessive visit lengths, we did not find a statistical association between empathic responses and encounter duration. To our knowledge, this is the first study to indicate an association between empathy and patient anxiety and communication ratings within the hospitalist model, which is rapidly becoming the predominant model for providing inpatient care in the United States.^4,5

As in oncologic care, anxiety is an emotion commonly confronted by clinicians meeting admitted medical patients for the first time. Studies show that not only do patient anxiety levels remain high throughout a hospital course, patients who experience higher levels of anxiety tend to stay longer in the hospital.^1,2,27-30 But unlike oncologic care or other therapy provided in an outpatient setting, the hospitalist model does not facilitate “continuity” of care, or the ability to care for the same patients over a long period of time. This reality of inpatient care makes rapid, effective rapport-building critical to establishing strong physician-patient relationships. In this setting, a simple communication tool that is potentially able to reduce inpatients’ anxiety could have a meaningful impact on hospitalist-provided care and patient outcomes.

In terms of the magnitude of the effect of empathic responses, the clinical significance of a 1.65-point decrease in the STAI-S anxiety score is not precisely clear. A prior study that examined the effect of music therapy on anxiety levels in patients with cancer found an average anxiety reduction of approximately 9.5 units on the STAIS-S scale after sensitivity analysis, suggesting a rather large meaningful effect size.³¹ Given we found a reduction of 1.65 points for each empathic response, however, with a range of 0-14 negative emotions expressed over a median 19-minute encounter, there is opportunity for hospitalists to achieve a clinically significant decrease in patient anxiety during an admission encounter. The potential to reduce anxiety is extended further when we consider that the impact of an empathic response may apply not just to the admission encounter alone but also to numerous other patient-clinician interactions over the course of a hospitalization.

A healthy body of communication research supports the associations we found in our study between empathy and patient ratings of communication and physicians. Families in ICU conferences rate communication more positively when physicians express empathy,¹² and a number of studies indicate an association between empathy and patient satisfaction in outpatient settings.⁸ Given the associations we found with negative ratings on the items in our study, promoting empathic responses to expressions of emotion and, more importantly, stressing avoidance of nonempathic responses may be relevant efforts in working to improve patient satisfaction scores on surveys reporting “top box” percentages, such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). More notably, evidence indicates that empathy has positive impacts beyond satisfaction surveys, such as adherence, better diagnostic and clinical outcomes, and strengthening of patient enablement.⁸Not all hospitalist responses to emotion were associated with patient ratings across the 7 communication items we assessed. For example, we did not find an association between how physicians responded to patient expressions of negative emotion and patient perception that enough time was spent in the visit or the degree to which talking with the doctor met a patient’s overall needs. It follows logically, and other research supports, that empathy would influence patient ratings of physician caring and trust,³² whereas other communication factors we were unable to measure (eg, physician body language, tone, and use of jargon and patient health literacy and primary language) may have a more significant association with patient ratings of the other items we assessed.

In considering the clinical application of our results, it is important to note that communication skills, including responding empathically to patient expressions of negative emotion, can be imparted through training in the same way as abdominal examination or electrocardiogram interpretation skills.^33-35 However, training of hospitalists in communication skills requires time and some financial investment on the part of the physician, their hospital or group, or, ideally, both. Effective training methods, like those for other skill acquisition, involve learner-centered teaching and practicing skills with role-play and feedback.³⁶ Given the importance of a learner-centered approach, learning would likely be better received and more effective if it was tailored to the specific needs and patient scenarios commonly encountered by hospitalist physicians. As these programs are developed, it will be important to assess the impact of any training on the patient-reported outcomes we assessed in this observational study, along with clinical outcomes.

Our study has several limitations. First, we were only able to evaluate whether hospitalists verbally responded to patient emotion and were thus not able to account for nonverbal empathy such as facial expressions, body language, or voice tone. Second, given our patient consent rate of 63%, patients who agreed to participate in the study may have had different opinions than those who declined to participate. Also, hospitalists and patients may have behaved differently as a result of being audio recorded. We only included patients who spoke English, and our patient population was predominately non-Hispanic white. Patients who spoke other languages or came from other cultural backgrounds may have had different responses. Third, we did not use a single validated scale for patient ratings of communication, and multiple analyses increase our risk of finding statistically significant associations by chance. The skewing of the communication rating items toward high scores may also have led to our results being driven by outliers, although the model we chose for analysis does penalize for this. Furthermore, our sample size was small, leading to wide CIs and potential for lack of statistical associations due to insufficient power. Our findings warrant replication in larger studies. Fourth, the setting of our study in an academic center may affect generalizability. Finally, the age of our data (collected between 2008 and 2009) is also a limitation. Given a recent focus on communication and patient experience since the initiation of HCAHPS feedback, a similar analysis of empathy and communication methods now may result in different outcomes.

In conclusion, our results suggest that enhancing hospitalists’ empathic responses to patient expressions of negative emotion could decrease patient anxiety and improve patients’ perceptions of (and thus possibly their relationships with) hospitalists, without sacrificing efficiency. Future work should focus on tailoring and implementing communication skills training programs for hospitalists and evaluating the impact of training on patient outcomes.

Acknowledgments

The authors extend their sincere thanks to the patients and physicians who participated in this study. Dr. Anderson was funded by the National Palliative Care Research Center and the University of California, San Francisco Clinical and Translational Science Institute Career Development Program, National Institutes of Health (NIH) grant number 5 KL2 RR024130-04. Project costs were funded by a grant from the University of California, San Francisco Academic Senate.

Disclosure

 All coauthors have seen and agree with the contents of this manuscript. This submission is not under review by any other publication. Wendy Anderson received funding for this project from the National Palliative Care Research Center, University of California San Francisco Clinical and Translational Science Institute (NIH grant number 5KL2RR024130-04), and the University of San Francisco Academic Senate [From Section 2 of Author Disclosure Form]. Andy Auerbach has a Patient-Centered Outcomes Research Institute research grant in development [From Section 3 of the Author Disclosure Form].

We thank Talari et al. for their comments in response to our randomized controlled trial evaluating the impact of standardized rounds on patient, attending, and trainee satisfaction. We agree that many factors beyond rounding structure contribute to resident satisfaction, including those highlighted by the authors, and would enthusiastically welcome additional research in this realm.

Because our study intervention addressed rounding structure, we elected to specifically focus on satisfaction with rounds, both from the physician and patient perspectives. We chose to ask about patient satisfaction with attending rounds, as opposed to more generic measures of patient satisfaction, to allow for more direct comparison between attending/resident responses and patient responses. Certainly, there are many other factors that affect overall patient experience. Surveys such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) and Press Ganey do not specifically address rounds, are often completed several weeks following hospitalization, and may have low response rates. Relying on such global assessments of patient experience may also reduce the power of the study. Although patient responses to our survey may be higher than scores seen with HCAHPS and Press Ganey, the randomized nature of our study helps control for other differences in the hospitalization experience unrelated to rounding structure. Similarly, because physician teams were randomly assigned, differences in census were not a major factor in the study. Physician blinding was not possible due to the nature of the intervention, which may have affected the satisfaction reports from attendings and residents. For our primary outcome (patient satisfaction with rounds), patients were blinded to the nature of our intervention, and all study team members involved in data collection and statistical analyses were blinded to study arm allocation.

In summary, we feel that evaluating the trade-offs and consequences of interventions should be examined from multiple perspectives, and we welcome additional investigations in this area.

We thank Talari et al. for their comments in response to our randomized controlled trial evaluating the impact of standardized rounds on patient, attending, and trainee satisfaction. We agree that many factors beyond rounding structure contribute to resident satisfaction, including those highlighted by the authors, and would enthusiastically welcome additional research in this realm.

Because our study intervention addressed rounding structure, we elected to specifically focus on satisfaction with rounds, both from the physician and patient perspectives. We chose to ask about patient satisfaction with attending rounds, as opposed to more generic measures of patient satisfaction, to allow for more direct comparison between attending/resident responses and patient responses. Certainly, there are many other factors that affect overall patient experience. Surveys such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) and Press Ganey do not specifically address rounds, are often completed several weeks following hospitalization, and may have low response rates. Relying on such global assessments of patient experience may also reduce the power of the study. Although patient responses to our survey may be higher than scores seen with HCAHPS and Press Ganey, the randomized nature of our study helps control for other differences in the hospitalization experience unrelated to rounding structure. Similarly, because physician teams were randomly assigned, differences in census were not a major factor in the study. Physician blinding was not possible due to the nature of the intervention, which may have affected the satisfaction reports from attendings and residents. For our primary outcome (patient satisfaction with rounds), patients were blinded to the nature of our intervention, and all study team members involved in data collection and statistical analyses were blinded to study arm allocation.

In summary, we feel that evaluating the trade-offs and consequences of interventions should be examined from multiple perspectives, and we welcome additional investigations in this area.

We thank Talari et al. for their comments in response to our randomized controlled trial evaluating the impact of standardized rounds on patient, attending, and trainee satisfaction. We agree that many factors beyond rounding structure contribute to resident satisfaction, including those highlighted by the authors, and would enthusiastically welcome additional research in this realm.

Because our study intervention addressed rounding structure, we elected to specifically focus on satisfaction with rounds, both from the physician and patient perspectives. We chose to ask about patient satisfaction with attending rounds, as opposed to more generic measures of patient satisfaction, to allow for more direct comparison between attending/resident responses and patient responses. Certainly, there are many other factors that affect overall patient experience. Surveys such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) and Press Ganey do not specifically address rounds, are often completed several weeks following hospitalization, and may have low response rates. Relying on such global assessments of patient experience may also reduce the power of the study. Although patient responses to our survey may be higher than scores seen with HCAHPS and Press Ganey, the randomized nature of our study helps control for other differences in the hospitalization experience unrelated to rounding structure. Similarly, because physician teams were randomly assigned, differences in census were not a major factor in the study. Physician blinding was not possible due to the nature of the intervention, which may have affected the satisfaction reports from attendings and residents. For our primary outcome (patient satisfaction with rounds), patients were blinded to the nature of our intervention, and all study team members involved in data collection and statistical analyses were blinded to study arm allocation.

In summary, we feel that evaluating the trade-offs and consequences of interventions should be examined from multiple perspectives, and we welcome additional investigations in this area.

Direct-acting oral anticoagulants (DOACs) have been introduced into clinical use for stroke prevention in patients with nonvalvular atrial fibrillation (NVAF), prevention of venous thrombosis after hip or knee surgery, and treatment of deep vein thrombosis (DVT) and pulmonary embolism (PE).^1-7 Advantages of DOACs over warfarin are often stated as fixed dosing, minor drug and food interactions, wider therapeutic index, and no need for laboratory test monitoring.^1,8 Yet, recommended DOAC dosages vary by renal function and therapeutic indications. Dosing recommendations for prevention of stroke in patients with NVAF are based on estimated creatinine clearance (dabigatran, rivaroxaban, edoxaban), age (apixaban), weight (apixaban, edoxaban), serum creatinine level (apixaban, edoxaban), and presence of cirrhosis by Child-Pugh class^9,10 (apixaban, edoxaban).^4-6,11,12 Dosing recommendations based on coadministration of strong CYP34A and P-glycoprotein inhibitors or inducers vary by DOAC. In addition, dabigatran cannot be crushed and must be stored in its original packaging, and rivaroxaban should be taken with food when the dose is over 10 mg.

We studied DOAC prescribing in adults admitted to a large academic medical center by comparing initial prescribed dosing with FDA-approved prescribing information. We hypothesized that the complexity of DOAC dosing may not be recognized by prescribers.

Our study protocol was approved by the Committee on Human Research (Institutional Review Board) of the University of California San Francisco.

Data Collection

We used electronic medical records (EMRs) to identify adult inpatients who were prescribed a DOAC (apixaban, dabigatran, edoxaban, or rivaroxaban) at the University of California San Francisco Medical Center, a large academic hospital, between July 1, 2014 and June 30, 2015. Demographic and medical information related to therapeutic indications, contraindications, and indications for dose adjustments were collected and included diagnoses classified by International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) for venous thromboses; phlebitis or thrombophlebitis; PE or venous embolism; atrial arrhythmias; surgical procedures; cirrhosis and/or ascites or liver disease; coagulopathies; artificial heart valves or implanted devices; prior use of medications including parenteral anticoagulants; and laboratory data obtained before the first DOAC order (serum creatinine level, estimated glomerular filtration rate [eGFR] determined by Chronic Kidney Disease Epidemiology Collaboration,¹³ international normalized ratio, or, if available, activated partial thromboplastin time and bilirubin level). Creatinine clearance was calculated with the Cockcroft-Gault method¹⁴ using total body weight, per drug label recommendation. Child-Pugh class was calculated if cirrhosis was diagnosed.¹⁰ DOAC dose, frequency, dosing directions, and prescriber medical specialty were determined.

Accuracy of search results was confirmed by review of the first 200 patients’ records. Records were manually reviewed for encounters lacking ICD-9/10 codes and approved DOAC indications (30%) and encounters having multiple coded diagnostic indications (to identify the indication). ICD-9 codes for venous thrombosis were reviewed to differentiate acute from chronic events.

Data Analysis

The main outcome was concordance or discordance between the first DOAC prescribing order and the FDA-approved prescribing information at the time. Initial classification, performed by 2 independent reviewers (a pharmacist and a physician, or 2 pharmacists), was followed by adjudication and individual record review (by 2 independent reviewers) of all initial prescribing orders classified as discordant. A third reviewer adjudicated any disagreement. Records and notes were reviewed to identify stated or potential reasons for dosing variation and pre-admission prescriptions. Data are presented as means and standard deviations (SDs) and as raw numbers and percentages. Differences in patient characteristics by DOAC or therapeutic indication were determined by analysis of variance (ANOVA) with Bonferroni correction for post hoc comparisons. Dosing information was categorized as the same as recommended, lower than recommended, higher than recommended, or avoid drug use (drug–drug or drug–disease interaction), per FDA-approved prescribing information, and χ² tests were used to determine whether variation in dosing occurred by individual DOAC, therapeutic indication, or prescriber specialty. Relationships between dosing variation and age or renal function were tested by ANOVA with Bonferroni correction for post hoc comparisons.

Patients with AF were older with lower creatinine clearance compared to patients with other diagnoses. Mean (SD) patient age was 72.1 (12.7) years for AF, 53.1 (10.9) years for chronic PE, 55.5 (14) years for acute PE, 56.4 (15.9) years for chronic DVT, 57.9 (18.4) years for acute DVT, and 61.4 (11.6) years for DVT prevention after hip or knee surgery (P < 0.0001 for all comparisons). Mean (SD) estimated creatinine clearance was 76.8 (43.5) mL/min for AF, 92.4 (44.4) mL/min for DVT prevention after hip or knee surgery, 111 (53) mL/min for chronic DVT, 118 (55) mL/min for acute DVT, 126 (60) mL/min for chronic PE, and 127 (54) mL/min for acute PE (P < 0.0001 for all comparisons). Differences between patient groups by therapeutic indication were not detected for weight, body mass index, or serum creatinine level.

The most frequent deviation from prescribing recommendations was omission of directions to administer rivaroxaban with food—93% (248/268) of orders—but not for DVT prevention after hip or knee surgery, for which the 10-mg dose is appropriately administered without food. Doses were the same as recommended for 82% of apixaban orders, 84% of rivaroxaban orders, and 93% of initial dabigatran orders (P < 0.05 for differences; Table 3). Dosages not concordant with FDA recommendations were prescribed in 44 (18.1%) of 243 apixaban orders, 41 (14.3%) of 286 rivaroxaban orders, and 7 (7.2%) of 89 initial dabigatran orders. Lower than recommended doses were more common than higher than recommended doses (Table 3, Figure 1): 15.2% versus 2.1% of apixaban orders, 9.4% versus 3.5% of rivaroxaban orders, and 4.2% versus 1.0% of initial dabigatran orders (P < 0.05). Failure to avoid drug use (for potential drug–drug or drug–disease interactions) was uncommon (1%-2%). There were more deviations from recommended doses for patients with AF or DVT prevention after hip or knee surgery than for patients with acute or chronic PE or acute DVT (Table 3). No significant differences were detected between prescribed and recommended doses by prescriber specialty.

For apixaban, patients who were prescribed lower than recommended doses were older than those prescribed recommended doses: mean (SD), 78.1 (12.2) years versus 71 (13.6) years (P = 0.003). Seventy-six percent of those prescribed lower than recommended doses were older than 75. Prescriptions for apixaban at lower than recommended doses were continuations of prior outpatient prescriptions in 20 of 37 cases (almost half), and in 12 cases (one-fourth) antiplatelet drugs were coprescribed (aspirin in 10 cases, clopidogrel in 1, prasugrel in 1). For rivaroxaban, older age was associated with both lower than recommended dosing (P = 0.003) and higher than recommended dosing (P < 0.001). Variations from prescribing recommendations were continuations of outpatient rivaroxaban doses in about two-thirds (26 of 41; 63.4 %) with 13 receiving antiplatelet drugs. For dabigatran, 6 of 7 orders not in agreement with recommendations were continuations of outpatient dosing.

The specific equation used to estimate renal function also had the potential to lead to dosing errors. Among the 41 rivaroxaban patients categorized as receiving doses discordant with recommendations, 8 would have had an inappropriate DOAC dose if eGFR were used instead of eCrCL as recommended. No relationships were detected for other patient variables/measures and dosing deviations from recommendations.

We examined initial hospital orders for DOACs in adults admitted to a single academic medical center during 2014-2015. Dabigatran, apixaban and rivaroxaban were prescribed for prevention of stroke in patients with atrial fibrillation/flutter (AF) in three quarters of the encounters similar to national patterns. (15) Prescribing departures from FDA-approved recommendations ranged from failure to prescribe rivaroxaban with food to failure to recognize drug-drug interactions in 1% to 2%. Unexpectedly, lower than recommended dosing was more common than higher than recommended dosing of the three DOACs.

Rivaroxaban bioavailability is dose dependent with the presence of food required to enhance absorption for doses over 10 mg that are used for prevention of stroke in patients with non-valvular AF or treatment of DVT or PE.^5,16 Peak rivaroxaban concentrations are 75% higher and the total area under the concentration vs. time curve after dosing is 40% higher when rivaroxaban is administered with high fat high calorie meals compared to the fasting state.¹⁶ If rivaroxaban is not administered with food, drug concentrations and pharmacologic effects may be less than in clinical trials that specified co-administration with food.^17-19 A small survey of outpatients receiving rivaroxaban found that 23% reported taking it without food.²⁰ With electronic pharmacy systems in almost all hospitals and electronic prescriber order entry in most, automated addition of directions for rivaroxaban administration with food for doses over 10 mg to labels or dispensing instructions could easily correct this deviation from recommended practice.

Lower than recommended doses were prescribed in 9.4% of orders for rivaroxaban and 15.2% of orders for apixaban, with dose-deviations often appearing to be a continuation of outpatient doses. Patients 75 years or older were more likely to receive lower than recommended dosing of apixaban. Reductions in apixaban doses from 5 mg twice daily to 2.5 mg twice daily are recommended in patients with non-valvular AF with two of the following criteria: age ≥80 y, weight ≤60 kg, serum creatinine ≥1.5 mg/dL or co-administration of a strong PgP inhibitor to a patient without 2 of the 3 dose reduction criteria. Our study was not designed to determine reasons for under-dosing, but we speculate that clinicians may have considered patients aged 75-79 years to be similar to those 80 years of age or older, or, older and not as healthy as those enrolled in randomized trials.^21-25 The median age of our patients with AF receiving apixaban was 75y (interquartile range of 16) vs 70y ( interquartile range 63-76) in the pivotal trial comparing warfarin to apixaban.²¹ Renal function was also lower with 37% having eCrCL below 50 mL/min compared to 17% in ARISTOTLE. (21). Twenty-six percent of our apixaban-treated AF patients qualified for the lower 2.5 mg twice daily compared to only 5% of ARISTOTLE participants,²¹ further suggesting differences between patients in our sample compared to randomized trial participants.

Concerns regarding bleeding or falls in older patients, may also have contributed to lower than recommended doses. Recent analyses of patients at risk for falls confirmed that increased risk of falling was associated with more bone fractures, bleeding and all-cause death but not stroke or systemic emboli, and with less severe bleeding with the DOAC edoxaban compared to warfarin.²⁶ While a rationale for personalized or lower than recommended dosing of apixaban may exist in very old patients and those at risk of falls and bleeding, more data are needed to determine outcomes of lower than recommended doses of DOACs before such an approach can be endorsed. Monitoring of anticoagulant effect in patients who receive doses lower than those investigated in clinical trials could provide important information. The assays that measure DOAC effects are likely to be more available because of the use of reversal agents in the setting of bleeding with DOACs.²⁷

We had anticipated higher than recommended dosing for rivaroxaban as recommendations are based on creatinine clearance while laboratories routinely report estimated glomerular filtration rate (eGFR) that can provide higher estimates of renal clearance and estimated DOAC doses in older and smaller individuals.²⁸ Higher than recommended dosing was found in only 3.5% of our sample. In half, eGFR estimates were higher than creatinine clearance estimates. An international postmarketing registry of rivaroxaban use for the prevention of stroke in patients with NVAF, which included outpatients, found that 36% of those with creatinine clearances below 50 mL/min received a dose higher than recommended, and 15% received a dose lower than expected.²⁹ A more recent outpatient registry report on patients with NVAF, in which apixaban, dabigatran, or rivaroxaban was administered, found that overall 9.4% received a dose lower than recommended, and 3.4% were overdosed, with a similar percentage (34%) of rivaroxaban patients with creatinine clearance of 15 to 50 mL/min receiving higher than recommended dosing.³⁰ The lower rate of higher-than-recommended doses that we observed may have been related to the routine measurement of serum creatinine and attention to dosing adjustments for renal function in the inpatient setting compared to the outpatient setting. In addition, renal function data may not be available to outpatient pharmacies, limiting potential input on dosing recommendations. At least one cardiac society recommends monitoring of renal function in patients treated with DOACs, annually in patients with normal estimated creatinine clearance and more frequently (at intervals in months equal to the creatinine clearance divided by 10) in patients with abnormal creatinine clearance.¹¹ A hospital encounter provides an opportunity to assess or reassess renal status to optimize DOAC dosing.

Dabigatran was the first DOAC introduced into use in the United States with the same dose recommended for prevention of stroke in patients with AF or venous thromboembolic disease with reductions for creatinine clearance below 30 mL/min or creatinine clearance between 30 and 50 mL/min and concomitant use of the potent P-glycoprotein inhibitor dronedarone or systemic ketoconazole. The relative simplicity of dosing may have been responsible for the lowest rate of prescribing outside of recommendations observed in this study, but the low dabigatran use limits analyses of contributing factors.

Failure to avoid drug use in combination with use of strong P-glycoprotein inducers or inhibitors was infrequent but should be preventable. Current prescribing recommendations refer to “strong” P-glycoprotein inhibitors and list different specific agents that interact with each DOAC without a standardized definition or classification. Standardized classifications or reference sources would be helpful.

Our primary goal in this study was to compare initial prescribed dosing of DOACs with FDA-approved prescribing directions. However, therapeutic indication data warrant discussion. In our sample, 7.5% of patients with AF had bioprosthetic valves or recent mitral valve repair or replacement. Using the NVAF definition found in the 2014 AHA/ACC/HRS (American Heart Association, American College of Cardiology, Heart Rhythm Society) AF guidelines¹—“absence of rheumatic mitral valve disease, a prosthetic heart valve, or mitral valve repair”—these patients would not appear to be candidates for DOACs. However, arguments have been made that a bioprosthetic heart valve or native valve after valve repair does not have a risk profile for thromboembolism that differs from other forms of NVAF and would be equally responsive to DOAC therapy.³¹ Data are sparse, but retrospective subanalyses of limited numbers of patients with valvular disease (including bioprosthesis and mitral repair patients but excluding mechanical valve patients) enrolled in the pivotal DOAC studies support this conclusion.³² For the first months after biological valve replacement (including catheter-based valve replacement), recent European guidelines recommend vitamin K antagonists but also state, “NOACs probably deliver the same protection.”⁸ DOACs were also used for management of venous thromboembolic disease (both acute and chronic) in patients with active cancer. Our data predate the most recent American College of Chest Physician guidelines on treatment of venous thromboembolism in patients with cancer, which provide grade 2B recommendations for use of low-molecular-weight heparin (LMWH) over vitamin K antagonists and grade 2C recommendations for use of LMWH over dabigatran, rivaroxaban, apixaban, or edoxaban.³³

Our study had several limitations. First, data were from a single US academic medical center, though similar rates of prescribing deviation from recommendations have been reported for rivaroxaban and dabigatran in NVAF patients in other countries.^29,34 Second, therapeutic indications may have been misclassified because of errors, incomplete EMR data, or multiple indications. Third, we analyzed the first DOAC order and not dispensing information or subsequent corrections. Therefore, deviations from recommendations should not be interpreted as errors that reached patients. We evaluated dosing based on the measures used at the time of hospital admission, noting that, in a significant fraction of deviations from recommended doses, they represented continuations of outpatient doses when renal function or weight may have differed, and it is unknown whether patients were counseled to take rivaroxaban with food in the outpatient setting. Fourth, the number of patients with acute DVT was small, so firm conclusions cannot be drawn for this specific population. Fifth, our estimates of off-label dosing may have been underestimates, as data on cancer and cancer activity or cardiac valvular disease may not have been complete.

Healthcare professionals are prescribing DOACs in ways that differ from recommendations. These differences may reflect the older ages and reduced renal function of clinical populations relative to randomized clinical trial groups, but they could also potentially alter clinical efficacy. Our findings support the need to evaluate the appropriateness and dosing of DOACs at each encounter and to determine the outcomes of patients treated with lower than recommended doses of DOACs and the outcomes of DOAC-treated patients with bioprostheses or active malignancies.

Acknowledgment

The authors thank Tobias Schmelzinger for electronic data extraction and compilation and University of California San Francisco students Eduardo De La Torre Cruz (School of Pharmacy) and Carlos Mikell (School of Medicine) for assistance with data review.

Disclosure

Dr. Schwartz reports receiving personal fees from Bristol-Myers Squibb and Amgen and grants from Bristol-Myers Squibb and Pfizer, outside the submitted work. The other authors have nothing to report.

Direct-acting oral anticoagulants (DOACs) have been introduced into clinical use for stroke prevention in patients with nonvalvular atrial fibrillation (NVAF), prevention of venous thrombosis after hip or knee surgery, and treatment of deep vein thrombosis (DVT) and pulmonary embolism (PE).^1-7 Advantages of DOACs over warfarin are often stated as fixed dosing, minor drug and food interactions, wider therapeutic index, and no need for laboratory test monitoring.^1,8 Yet, recommended DOAC dosages vary by renal function and therapeutic indications. Dosing recommendations for prevention of stroke in patients with NVAF are based on estimated creatinine clearance (dabigatran, rivaroxaban, edoxaban), age (apixaban), weight (apixaban, edoxaban), serum creatinine level (apixaban, edoxaban), and presence of cirrhosis by Child-Pugh class^9,10 (apixaban, edoxaban).^4-6,11,12 Dosing recommendations based on coadministration of strong CYP34A and P-glycoprotein inhibitors or inducers vary by DOAC. In addition, dabigatran cannot be crushed and must be stored in its original packaging, and rivaroxaban should be taken with food when the dose is over 10 mg.

We studied DOAC prescribing in adults admitted to a large academic medical center by comparing initial prescribed dosing with FDA-approved prescribing information. We hypothesized that the complexity of DOAC dosing may not be recognized by prescribers.

Our study protocol was approved by the Committee on Human Research (Institutional Review Board) of the University of California San Francisco.

Data Collection

We used electronic medical records (EMRs) to identify adult inpatients who were prescribed a DOAC (apixaban, dabigatran, edoxaban, or rivaroxaban) at the University of California San Francisco Medical Center, a large academic hospital, between July 1, 2014 and June 30, 2015. Demographic and medical information related to therapeutic indications, contraindications, and indications for dose adjustments were collected and included diagnoses classified by International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) for venous thromboses; phlebitis or thrombophlebitis; PE or venous embolism; atrial arrhythmias; surgical procedures; cirrhosis and/or ascites or liver disease; coagulopathies; artificial heart valves or implanted devices; prior use of medications including parenteral anticoagulants; and laboratory data obtained before the first DOAC order (serum creatinine level, estimated glomerular filtration rate [eGFR] determined by Chronic Kidney Disease Epidemiology Collaboration,¹³ international normalized ratio, or, if available, activated partial thromboplastin time and bilirubin level). Creatinine clearance was calculated with the Cockcroft-Gault method¹⁴ using total body weight, per drug label recommendation. Child-Pugh class was calculated if cirrhosis was diagnosed.¹⁰ DOAC dose, frequency, dosing directions, and prescriber medical specialty were determined.

Accuracy of search results was confirmed by review of the first 200 patients’ records. Records were manually reviewed for encounters lacking ICD-9/10 codes and approved DOAC indications (30%) and encounters having multiple coded diagnostic indications (to identify the indication). ICD-9 codes for venous thrombosis were reviewed to differentiate acute from chronic events.

Data Analysis

The main outcome was concordance or discordance between the first DOAC prescribing order and the FDA-approved prescribing information at the time. Initial classification, performed by 2 independent reviewers (a pharmacist and a physician, or 2 pharmacists), was followed by adjudication and individual record review (by 2 independent reviewers) of all initial prescribing orders classified as discordant. A third reviewer adjudicated any disagreement. Records and notes were reviewed to identify stated or potential reasons for dosing variation and pre-admission prescriptions. Data are presented as means and standard deviations (SDs) and as raw numbers and percentages. Differences in patient characteristics by DOAC or therapeutic indication were determined by analysis of variance (ANOVA) with Bonferroni correction for post hoc comparisons. Dosing information was categorized as the same as recommended, lower than recommended, higher than recommended, or avoid drug use (drug–drug or drug–disease interaction), per FDA-approved prescribing information, and χ² tests were used to determine whether variation in dosing occurred by individual DOAC, therapeutic indication, or prescriber specialty. Relationships between dosing variation and age or renal function were tested by ANOVA with Bonferroni correction for post hoc comparisons.

Patients with AF were older with lower creatinine clearance compared to patients with other diagnoses. Mean (SD) patient age was 72.1 (12.7) years for AF, 53.1 (10.9) years for chronic PE, 55.5 (14) years for acute PE, 56.4 (15.9) years for chronic DVT, 57.9 (18.4) years for acute DVT, and 61.4 (11.6) years for DVT prevention after hip or knee surgery (P < 0.0001 for all comparisons). Mean (SD) estimated creatinine clearance was 76.8 (43.5) mL/min for AF, 92.4 (44.4) mL/min for DVT prevention after hip or knee surgery, 111 (53) mL/min for chronic DVT, 118 (55) mL/min for acute DVT, 126 (60) mL/min for chronic PE, and 127 (54) mL/min for acute PE (P < 0.0001 for all comparisons). Differences between patient groups by therapeutic indication were not detected for weight, body mass index, or serum creatinine level.

The most frequent deviation from prescribing recommendations was omission of directions to administer rivaroxaban with food—93% (248/268) of orders—but not for DVT prevention after hip or knee surgery, for which the 10-mg dose is appropriately administered without food. Doses were the same as recommended for 82% of apixaban orders, 84% of rivaroxaban orders, and 93% of initial dabigatran orders (P < 0.05 for differences; Table 3). Dosages not concordant with FDA recommendations were prescribed in 44 (18.1%) of 243 apixaban orders, 41 (14.3%) of 286 rivaroxaban orders, and 7 (7.2%) of 89 initial dabigatran orders. Lower than recommended doses were more common than higher than recommended doses (Table 3, Figure 1): 15.2% versus 2.1% of apixaban orders, 9.4% versus 3.5% of rivaroxaban orders, and 4.2% versus 1.0% of initial dabigatran orders (P < 0.05). Failure to avoid drug use (for potential drug–drug or drug–disease interactions) was uncommon (1%-2%). There were more deviations from recommended doses for patients with AF or DVT prevention after hip or knee surgery than for patients with acute or chronic PE or acute DVT (Table 3). No significant differences were detected between prescribed and recommended doses by prescriber specialty.

For apixaban, patients who were prescribed lower than recommended doses were older than those prescribed recommended doses: mean (SD), 78.1 (12.2) years versus 71 (13.6) years (P = 0.003). Seventy-six percent of those prescribed lower than recommended doses were older than 75. Prescriptions for apixaban at lower than recommended doses were continuations of prior outpatient prescriptions in 20 of 37 cases (almost half), and in 12 cases (one-fourth) antiplatelet drugs were coprescribed (aspirin in 10 cases, clopidogrel in 1, prasugrel in 1). For rivaroxaban, older age was associated with both lower than recommended dosing (P = 0.003) and higher than recommended dosing (P < 0.001). Variations from prescribing recommendations were continuations of outpatient rivaroxaban doses in about two-thirds (26 of 41; 63.4 %) with 13 receiving antiplatelet drugs. For dabigatran, 6 of 7 orders not in agreement with recommendations were continuations of outpatient dosing.

The specific equation used to estimate renal function also had the potential to lead to dosing errors. Among the 41 rivaroxaban patients categorized as receiving doses discordant with recommendations, 8 would have had an inappropriate DOAC dose if eGFR were used instead of eCrCL as recommended. No relationships were detected for other patient variables/measures and dosing deviations from recommendations.

We examined initial hospital orders for DOACs in adults admitted to a single academic medical center during 2014-2015. Dabigatran, apixaban and rivaroxaban were prescribed for prevention of stroke in patients with atrial fibrillation/flutter (AF) in three quarters of the encounters similar to national patterns. (15) Prescribing departures from FDA-approved recommendations ranged from failure to prescribe rivaroxaban with food to failure to recognize drug-drug interactions in 1% to 2%. Unexpectedly, lower than recommended dosing was more common than higher than recommended dosing of the three DOACs.

Rivaroxaban bioavailability is dose dependent with the presence of food required to enhance absorption for doses over 10 mg that are used for prevention of stroke in patients with non-valvular AF or treatment of DVT or PE.^5,16 Peak rivaroxaban concentrations are 75% higher and the total area under the concentration vs. time curve after dosing is 40% higher when rivaroxaban is administered with high fat high calorie meals compared to the fasting state.¹⁶ If rivaroxaban is not administered with food, drug concentrations and pharmacologic effects may be less than in clinical trials that specified co-administration with food.^17-19 A small survey of outpatients receiving rivaroxaban found that 23% reported taking it without food.²⁰ With electronic pharmacy systems in almost all hospitals and electronic prescriber order entry in most, automated addition of directions for rivaroxaban administration with food for doses over 10 mg to labels or dispensing instructions could easily correct this deviation from recommended practice.

Lower than recommended doses were prescribed in 9.4% of orders for rivaroxaban and 15.2% of orders for apixaban, with dose-deviations often appearing to be a continuation of outpatient doses. Patients 75 years or older were more likely to receive lower than recommended dosing of apixaban. Reductions in apixaban doses from 5 mg twice daily to 2.5 mg twice daily are recommended in patients with non-valvular AF with two of the following criteria: age ≥80 y, weight ≤60 kg, serum creatinine ≥1.5 mg/dL or co-administration of a strong PgP inhibitor to a patient without 2 of the 3 dose reduction criteria. Our study was not designed to determine reasons for under-dosing, but we speculate that clinicians may have considered patients aged 75-79 years to be similar to those 80 years of age or older, or, older and not as healthy as those enrolled in randomized trials.^21-25 The median age of our patients with AF receiving apixaban was 75y (interquartile range of 16) vs 70y ( interquartile range 63-76) in the pivotal trial comparing warfarin to apixaban.²¹ Renal function was also lower with 37% having eCrCL below 50 mL/min compared to 17% in ARISTOTLE. (21). Twenty-six percent of our apixaban-treated AF patients qualified for the lower 2.5 mg twice daily compared to only 5% of ARISTOTLE participants,²¹ further suggesting differences between patients in our sample compared to randomized trial participants.

Concerns regarding bleeding or falls in older patients, may also have contributed to lower than recommended doses. Recent analyses of patients at risk for falls confirmed that increased risk of falling was associated with more bone fractures, bleeding and all-cause death but not stroke or systemic emboli, and with less severe bleeding with the DOAC edoxaban compared to warfarin.²⁶ While a rationale for personalized or lower than recommended dosing of apixaban may exist in very old patients and those at risk of falls and bleeding, more data are needed to determine outcomes of lower than recommended doses of DOACs before such an approach can be endorsed. Monitoring of anticoagulant effect in patients who receive doses lower than those investigated in clinical trials could provide important information. The assays that measure DOAC effects are likely to be more available because of the use of reversal agents in the setting of bleeding with DOACs.²⁷

We had anticipated higher than recommended dosing for rivaroxaban as recommendations are based on creatinine clearance while laboratories routinely report estimated glomerular filtration rate (eGFR) that can provide higher estimates of renal clearance and estimated DOAC doses in older and smaller individuals.²⁸ Higher than recommended dosing was found in only 3.5% of our sample. In half, eGFR estimates were higher than creatinine clearance estimates. An international postmarketing registry of rivaroxaban use for the prevention of stroke in patients with NVAF, which included outpatients, found that 36% of those with creatinine clearances below 50 mL/min received a dose higher than recommended, and 15% received a dose lower than expected.²⁹ A more recent outpatient registry report on patients with NVAF, in which apixaban, dabigatran, or rivaroxaban was administered, found that overall 9.4% received a dose lower than recommended, and 3.4% were overdosed, with a similar percentage (34%) of rivaroxaban patients with creatinine clearance of 15 to 50 mL/min receiving higher than recommended dosing.³⁰ The lower rate of higher-than-recommended doses that we observed may have been related to the routine measurement of serum creatinine and attention to dosing adjustments for renal function in the inpatient setting compared to the outpatient setting. In addition, renal function data may not be available to outpatient pharmacies, limiting potential input on dosing recommendations. At least one cardiac society recommends monitoring of renal function in patients treated with DOACs, annually in patients with normal estimated creatinine clearance and more frequently (at intervals in months equal to the creatinine clearance divided by 10) in patients with abnormal creatinine clearance.¹¹ A hospital encounter provides an opportunity to assess or reassess renal status to optimize DOAC dosing.

Dabigatran was the first DOAC introduced into use in the United States with the same dose recommended for prevention of stroke in patients with AF or venous thromboembolic disease with reductions for creatinine clearance below 30 mL/min or creatinine clearance between 30 and 50 mL/min and concomitant use of the potent P-glycoprotein inhibitor dronedarone or systemic ketoconazole. The relative simplicity of dosing may have been responsible for the lowest rate of prescribing outside of recommendations observed in this study, but the low dabigatran use limits analyses of contributing factors.

Failure to avoid drug use in combination with use of strong P-glycoprotein inducers or inhibitors was infrequent but should be preventable. Current prescribing recommendations refer to “strong” P-glycoprotein inhibitors and list different specific agents that interact with each DOAC without a standardized definition or classification. Standardized classifications or reference sources would be helpful.

Our primary goal in this study was to compare initial prescribed dosing of DOACs with FDA-approved prescribing directions. However, therapeutic indication data warrant discussion. In our sample, 7.5% of patients with AF had bioprosthetic valves or recent mitral valve repair or replacement. Using the NVAF definition found in the 2014 AHA/ACC/HRS (American Heart Association, American College of Cardiology, Heart Rhythm Society) AF guidelines¹—“absence of rheumatic mitral valve disease, a prosthetic heart valve, or mitral valve repair”—these patients would not appear to be candidates for DOACs. However, arguments have been made that a bioprosthetic heart valve or native valve after valve repair does not have a risk profile for thromboembolism that differs from other forms of NVAF and would be equally responsive to DOAC therapy.³¹ Data are sparse, but retrospective subanalyses of limited numbers of patients with valvular disease (including bioprosthesis and mitral repair patients but excluding mechanical valve patients) enrolled in the pivotal DOAC studies support this conclusion.³² For the first months after biological valve replacement (including catheter-based valve replacement), recent European guidelines recommend vitamin K antagonists but also state, “NOACs probably deliver the same protection.”⁸ DOACs were also used for management of venous thromboembolic disease (both acute and chronic) in patients with active cancer. Our data predate the most recent American College of Chest Physician guidelines on treatment of venous thromboembolism in patients with cancer, which provide grade 2B recommendations for use of low-molecular-weight heparin (LMWH) over vitamin K antagonists and grade 2C recommendations for use of LMWH over dabigatran, rivaroxaban, apixaban, or edoxaban.³³

Our study had several limitations. First, data were from a single US academic medical center, though similar rates of prescribing deviation from recommendations have been reported for rivaroxaban and dabigatran in NVAF patients in other countries.^29,34 Second, therapeutic indications may have been misclassified because of errors, incomplete EMR data, or multiple indications. Third, we analyzed the first DOAC order and not dispensing information or subsequent corrections. Therefore, deviations from recommendations should not be interpreted as errors that reached patients. We evaluated dosing based on the measures used at the time of hospital admission, noting that, in a significant fraction of deviations from recommended doses, they represented continuations of outpatient doses when renal function or weight may have differed, and it is unknown whether patients were counseled to take rivaroxaban with food in the outpatient setting. Fourth, the number of patients with acute DVT was small, so firm conclusions cannot be drawn for this specific population. Fifth, our estimates of off-label dosing may have been underestimates, as data on cancer and cancer activity or cardiac valvular disease may not have been complete.

Healthcare professionals are prescribing DOACs in ways that differ from recommendations. These differences may reflect the older ages and reduced renal function of clinical populations relative to randomized clinical trial groups, but they could also potentially alter clinical efficacy. Our findings support the need to evaluate the appropriateness and dosing of DOACs at each encounter and to determine the outcomes of patients treated with lower than recommended doses of DOACs and the outcomes of DOAC-treated patients with bioprostheses or active malignancies.

Acknowledgment

The authors thank Tobias Schmelzinger for electronic data extraction and compilation and University of California San Francisco students Eduardo De La Torre Cruz (School of Pharmacy) and Carlos Mikell (School of Medicine) for assistance with data review.

Disclosure

Dr. Schwartz reports receiving personal fees from Bristol-Myers Squibb and Amgen and grants from Bristol-Myers Squibb and Pfizer, outside the submitted work. The other authors have nothing to report.

Direct-acting oral anticoagulants (DOACs) have been introduced into clinical use for stroke prevention in patients with nonvalvular atrial fibrillation (NVAF), prevention of venous thrombosis after hip or knee surgery, and treatment of deep vein thrombosis (DVT) and pulmonary embolism (PE).^1-7 Advantages of DOACs over warfarin are often stated as fixed dosing, minor drug and food interactions, wider therapeutic index, and no need for laboratory test monitoring.^1,8 Yet, recommended DOAC dosages vary by renal function and therapeutic indications. Dosing recommendations for prevention of stroke in patients with NVAF are based on estimated creatinine clearance (dabigatran, rivaroxaban, edoxaban), age (apixaban), weight (apixaban, edoxaban), serum creatinine level (apixaban, edoxaban), and presence of cirrhosis by Child-Pugh class^9,10 (apixaban, edoxaban).^4-6,11,12 Dosing recommendations based on coadministration of strong CYP34A and P-glycoprotein inhibitors or inducers vary by DOAC. In addition, dabigatran cannot be crushed and must be stored in its original packaging, and rivaroxaban should be taken with food when the dose is over 10 mg.

We studied DOAC prescribing in adults admitted to a large academic medical center by comparing initial prescribed dosing with FDA-approved prescribing information. We hypothesized that the complexity of DOAC dosing may not be recognized by prescribers.

Our study protocol was approved by the Committee on Human Research (Institutional Review Board) of the University of California San Francisco.

Data Collection

We used electronic medical records (EMRs) to identify adult inpatients who were prescribed a DOAC (apixaban, dabigatran, edoxaban, or rivaroxaban) at the University of California San Francisco Medical Center, a large academic hospital, between July 1, 2014 and June 30, 2015. Demographic and medical information related to therapeutic indications, contraindications, and indications for dose adjustments were collected and included diagnoses classified by International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) for venous thromboses; phlebitis or thrombophlebitis; PE or venous embolism; atrial arrhythmias; surgical procedures; cirrhosis and/or ascites or liver disease; coagulopathies; artificial heart valves or implanted devices; prior use of medications including parenteral anticoagulants; and laboratory data obtained before the first DOAC order (serum creatinine level, estimated glomerular filtration rate [eGFR] determined by Chronic Kidney Disease Epidemiology Collaboration,¹³ international normalized ratio, or, if available, activated partial thromboplastin time and bilirubin level). Creatinine clearance was calculated with the Cockcroft-Gault method¹⁴ using total body weight, per drug label recommendation. Child-Pugh class was calculated if cirrhosis was diagnosed.¹⁰ DOAC dose, frequency, dosing directions, and prescriber medical specialty were determined.

Accuracy of search results was confirmed by review of the first 200 patients’ records. Records were manually reviewed for encounters lacking ICD-9/10 codes and approved DOAC indications (30%) and encounters having multiple coded diagnostic indications (to identify the indication). ICD-9 codes for venous thrombosis were reviewed to differentiate acute from chronic events.

Data Analysis

The main outcome was concordance or discordance between the first DOAC prescribing order and the FDA-approved prescribing information at the time. Initial classification, performed by 2 independent reviewers (a pharmacist and a physician, or 2 pharmacists), was followed by adjudication and individual record review (by 2 independent reviewers) of all initial prescribing orders classified as discordant. A third reviewer adjudicated any disagreement. Records and notes were reviewed to identify stated or potential reasons for dosing variation and pre-admission prescriptions. Data are presented as means and standard deviations (SDs) and as raw numbers and percentages. Differences in patient characteristics by DOAC or therapeutic indication were determined by analysis of variance (ANOVA) with Bonferroni correction for post hoc comparisons. Dosing information was categorized as the same as recommended, lower than recommended, higher than recommended, or avoid drug use (drug–drug or drug–disease interaction), per FDA-approved prescribing information, and χ² tests were used to determine whether variation in dosing occurred by individual DOAC, therapeutic indication, or prescriber specialty. Relationships between dosing variation and age or renal function were tested by ANOVA with Bonferroni correction for post hoc comparisons.

Patients with AF were older with lower creatinine clearance compared to patients with other diagnoses. Mean (SD) patient age was 72.1 (12.7) years for AF, 53.1 (10.9) years for chronic PE, 55.5 (14) years for acute PE, 56.4 (15.9) years for chronic DVT, 57.9 (18.4) years for acute DVT, and 61.4 (11.6) years for DVT prevention after hip or knee surgery (P < 0.0001 for all comparisons). Mean (SD) estimated creatinine clearance was 76.8 (43.5) mL/min for AF, 92.4 (44.4) mL/min for DVT prevention after hip or knee surgery, 111 (53) mL/min for chronic DVT, 118 (55) mL/min for acute DVT, 126 (60) mL/min for chronic PE, and 127 (54) mL/min for acute PE (P < 0.0001 for all comparisons). Differences between patient groups by therapeutic indication were not detected for weight, body mass index, or serum creatinine level.

The most frequent deviation from prescribing recommendations was omission of directions to administer rivaroxaban with food—93% (248/268) of orders—but not for DVT prevention after hip or knee surgery, for which the 10-mg dose is appropriately administered without food. Doses were the same as recommended for 82% of apixaban orders, 84% of rivaroxaban orders, and 93% of initial dabigatran orders (P < 0.05 for differences; Table 3). Dosages not concordant with FDA recommendations were prescribed in 44 (18.1%) of 243 apixaban orders, 41 (14.3%) of 286 rivaroxaban orders, and 7 (7.2%) of 89 initial dabigatran orders. Lower than recommended doses were more common than higher than recommended doses (Table 3, Figure 1): 15.2% versus 2.1% of apixaban orders, 9.4% versus 3.5% of rivaroxaban orders, and 4.2% versus 1.0% of initial dabigatran orders (P < 0.05). Failure to avoid drug use (for potential drug–drug or drug–disease interactions) was uncommon (1%-2%). There were more deviations from recommended doses for patients with AF or DVT prevention after hip or knee surgery than for patients with acute or chronic PE or acute DVT (Table 3). No significant differences were detected between prescribed and recommended doses by prescriber specialty.

For apixaban, patients who were prescribed lower than recommended doses were older than those prescribed recommended doses: mean (SD), 78.1 (12.2) years versus 71 (13.6) years (P = 0.003). Seventy-six percent of those prescribed lower than recommended doses were older than 75. Prescriptions for apixaban at lower than recommended doses were continuations of prior outpatient prescriptions in 20 of 37 cases (almost half), and in 12 cases (one-fourth) antiplatelet drugs were coprescribed (aspirin in 10 cases, clopidogrel in 1, prasugrel in 1). For rivaroxaban, older age was associated with both lower than recommended dosing (P = 0.003) and higher than recommended dosing (P < 0.001). Variations from prescribing recommendations were continuations of outpatient rivaroxaban doses in about two-thirds (26 of 41; 63.4 %) with 13 receiving antiplatelet drugs. For dabigatran, 6 of 7 orders not in agreement with recommendations were continuations of outpatient dosing.

The specific equation used to estimate renal function also had the potential to lead to dosing errors. Among the 41 rivaroxaban patients categorized as receiving doses discordant with recommendations, 8 would have had an inappropriate DOAC dose if eGFR were used instead of eCrCL as recommended. No relationships were detected for other patient variables/measures and dosing deviations from recommendations.

We examined initial hospital orders for DOACs in adults admitted to a single academic medical center during 2014-2015. Dabigatran, apixaban and rivaroxaban were prescribed for prevention of stroke in patients with atrial fibrillation/flutter (AF) in three quarters of the encounters similar to national patterns. (15) Prescribing departures from FDA-approved recommendations ranged from failure to prescribe rivaroxaban with food to failure to recognize drug-drug interactions in 1% to 2%. Unexpectedly, lower than recommended dosing was more common than higher than recommended dosing of the three DOACs.

Rivaroxaban bioavailability is dose dependent with the presence of food required to enhance absorption for doses over 10 mg that are used for prevention of stroke in patients with non-valvular AF or treatment of DVT or PE.^5,16 Peak rivaroxaban concentrations are 75% higher and the total area under the concentration vs. time curve after dosing is 40% higher when rivaroxaban is administered with high fat high calorie meals compared to the fasting state.¹⁶ If rivaroxaban is not administered with food, drug concentrations and pharmacologic effects may be less than in clinical trials that specified co-administration with food.^17-19 A small survey of outpatients receiving rivaroxaban found that 23% reported taking it without food.²⁰ With electronic pharmacy systems in almost all hospitals and electronic prescriber order entry in most, automated addition of directions for rivaroxaban administration with food for doses over 10 mg to labels or dispensing instructions could easily correct this deviation from recommended practice.

Lower than recommended doses were prescribed in 9.4% of orders for rivaroxaban and 15.2% of orders for apixaban, with dose-deviations often appearing to be a continuation of outpatient doses. Patients 75 years or older were more likely to receive lower than recommended dosing of apixaban. Reductions in apixaban doses from 5 mg twice daily to 2.5 mg twice daily are recommended in patients with non-valvular AF with two of the following criteria: age ≥80 y, weight ≤60 kg, serum creatinine ≥1.5 mg/dL or co-administration of a strong PgP inhibitor to a patient without 2 of the 3 dose reduction criteria. Our study was not designed to determine reasons for under-dosing, but we speculate that clinicians may have considered patients aged 75-79 years to be similar to those 80 years of age or older, or, older and not as healthy as those enrolled in randomized trials.^21-25 The median age of our patients with AF receiving apixaban was 75y (interquartile range of 16) vs 70y ( interquartile range 63-76) in the pivotal trial comparing warfarin to apixaban.²¹ Renal function was also lower with 37% having eCrCL below 50 mL/min compared to 17% in ARISTOTLE. (21). Twenty-six percent of our apixaban-treated AF patients qualified for the lower 2.5 mg twice daily compared to only 5% of ARISTOTLE participants,²¹ further suggesting differences between patients in our sample compared to randomized trial participants.

Concerns regarding bleeding or falls in older patients, may also have contributed to lower than recommended doses. Recent analyses of patients at risk for falls confirmed that increased risk of falling was associated with more bone fractures, bleeding and all-cause death but not stroke or systemic emboli, and with less severe bleeding with the DOAC edoxaban compared to warfarin.²⁶ While a rationale for personalized or lower than recommended dosing of apixaban may exist in very old patients and those at risk of falls and bleeding, more data are needed to determine outcomes of lower than recommended doses of DOACs before such an approach can be endorsed. Monitoring of anticoagulant effect in patients who receive doses lower than those investigated in clinical trials could provide important information. The assays that measure DOAC effects are likely to be more available because of the use of reversal agents in the setting of bleeding with DOACs.²⁷

We had anticipated higher than recommended dosing for rivaroxaban as recommendations are based on creatinine clearance while laboratories routinely report estimated glomerular filtration rate (eGFR) that can provide higher estimates of renal clearance and estimated DOAC doses in older and smaller individuals.²⁸ Higher than recommended dosing was found in only 3.5% of our sample. In half, eGFR estimates were higher than creatinine clearance estimates. An international postmarketing registry of rivaroxaban use for the prevention of stroke in patients with NVAF, which included outpatients, found that 36% of those with creatinine clearances below 50 mL/min received a dose higher than recommended, and 15% received a dose lower than expected.²⁹ A more recent outpatient registry report on patients with NVAF, in which apixaban, dabigatran, or rivaroxaban was administered, found that overall 9.4% received a dose lower than recommended, and 3.4% were overdosed, with a similar percentage (34%) of rivaroxaban patients with creatinine clearance of 15 to 50 mL/min receiving higher than recommended dosing.³⁰ The lower rate of higher-than-recommended doses that we observed may have been related to the routine measurement of serum creatinine and attention to dosing adjustments for renal function in the inpatient setting compared to the outpatient setting. In addition, renal function data may not be available to outpatient pharmacies, limiting potential input on dosing recommendations. At least one cardiac society recommends monitoring of renal function in patients treated with DOACs, annually in patients with normal estimated creatinine clearance and more frequently (at intervals in months equal to the creatinine clearance divided by 10) in patients with abnormal creatinine clearance.¹¹ A hospital encounter provides an opportunity to assess or reassess renal status to optimize DOAC dosing.

Dabigatran was the first DOAC introduced into use in the United States with the same dose recommended for prevention of stroke in patients with AF or venous thromboembolic disease with reductions for creatinine clearance below 30 mL/min or creatinine clearance between 30 and 50 mL/min and concomitant use of the potent P-glycoprotein inhibitor dronedarone or systemic ketoconazole. The relative simplicity of dosing may have been responsible for the lowest rate of prescribing outside of recommendations observed in this study, but the low dabigatran use limits analyses of contributing factors.

Failure to avoid drug use in combination with use of strong P-glycoprotein inducers or inhibitors was infrequent but should be preventable. Current prescribing recommendations refer to “strong” P-glycoprotein inhibitors and list different specific agents that interact with each DOAC without a standardized definition or classification. Standardized classifications or reference sources would be helpful.

Our primary goal in this study was to compare initial prescribed dosing of DOACs with FDA-approved prescribing directions. However, therapeutic indication data warrant discussion. In our sample, 7.5% of patients with AF had bioprosthetic valves or recent mitral valve repair or replacement. Using the NVAF definition found in the 2014 AHA/ACC/HRS (American Heart Association, American College of Cardiology, Heart Rhythm Society) AF guidelines¹—“absence of rheumatic mitral valve disease, a prosthetic heart valve, or mitral valve repair”—these patients would not appear to be candidates for DOACs. However, arguments have been made that a bioprosthetic heart valve or native valve after valve repair does not have a risk profile for thromboembolism that differs from other forms of NVAF and would be equally responsive to DOAC therapy.³¹ Data are sparse, but retrospective subanalyses of limited numbers of patients with valvular disease (including bioprosthesis and mitral repair patients but excluding mechanical valve patients) enrolled in the pivotal DOAC studies support this conclusion.³² For the first months after biological valve replacement (including catheter-based valve replacement), recent European guidelines recommend vitamin K antagonists but also state, “NOACs probably deliver the same protection.”⁸ DOACs were also used for management of venous thromboembolic disease (both acute and chronic) in patients with active cancer. Our data predate the most recent American College of Chest Physician guidelines on treatment of venous thromboembolism in patients with cancer, which provide grade 2B recommendations for use of low-molecular-weight heparin (LMWH) over vitamin K antagonists and grade 2C recommendations for use of LMWH over dabigatran, rivaroxaban, apixaban, or edoxaban.³³

Our study had several limitations. First, data were from a single US academic medical center, though similar rates of prescribing deviation from recommendations have been reported for rivaroxaban and dabigatran in NVAF patients in other countries.^29,34 Second, therapeutic indications may have been misclassified because of errors, incomplete EMR data, or multiple indications. Third, we analyzed the first DOAC order and not dispensing information or subsequent corrections. Therefore, deviations from recommendations should not be interpreted as errors that reached patients. We evaluated dosing based on the measures used at the time of hospital admission, noting that, in a significant fraction of deviations from recommended doses, they represented continuations of outpatient doses when renal function or weight may have differed, and it is unknown whether patients were counseled to take rivaroxaban with food in the outpatient setting. Fourth, the number of patients with acute DVT was small, so firm conclusions cannot be drawn for this specific population. Fifth, our estimates of off-label dosing may have been underestimates, as data on cancer and cancer activity or cardiac valvular disease may not have been complete.

Healthcare professionals are prescribing DOACs in ways that differ from recommendations. These differences may reflect the older ages and reduced renal function of clinical populations relative to randomized clinical trial groups, but they could also potentially alter clinical efficacy. Our findings support the need to evaluate the appropriateness and dosing of DOACs at each encounter and to determine the outcomes of patients treated with lower than recommended doses of DOACs and the outcomes of DOAC-treated patients with bioprostheses or active malignancies.

Acknowledgment

The authors thank Tobias Schmelzinger for electronic data extraction and compilation and University of California San Francisco students Eduardo De La Torre Cruz (School of Pharmacy) and Carlos Mikell (School of Medicine) for assistance with data review.

Disclosure

Dr. Schwartz reports receiving personal fees from Bristol-Myers Squibb and Amgen and grants from Bristol-Myers Squibb and Pfizer, outside the submitted work. The other authors have nothing to report.

We have tools to predict bleeding risk, but their predictive value is modest, and the estimated risk of bleeding is often outweighed by the benefits of anticoagulant therapy.

Anticoagulant therapy is commonly prescribed for conditions that disproportionately affect the elderly, including atrial fibrillation, venous thromboembolism, and valvular heart disease. Though anticoagulants are highly effective in preventing clots, they also significantly increase the risk of bleeding. Since older age is a risk factor for bleeding as well as thrombosis, it is essential to weigh the risks and benefits of anticoagulants for each patient.

WHAT KINDS OF BLEEDING DEVELOP IN PATIENTS ON ANTICOAGULANTS?

Patients taking anticoagulants have roughly double the risk of bleeding compared with patients not on anticoagulants.¹ Bleeding rates tend to be slightly higher in patients taking anticoagulants for venous thromboembolism than in those taking them for atrial fibrillation. The average yearly risk of a “major” anticoagulant-associated bleeding event (eg, requiring transfusion or intervention or occurring in a critical anatomic site) is about 2% to 3%, with most of the bleeding being gastrointestinal.²

Intracranial hemorrhage is by far the most deadly complication of anticoagulant therapy: it causes 90% of deaths and disability from warfarin-associated hemorrhage and is associated with a death rate over 50%; however, it is much less common than gastrointestinal bleeding.³ Anticoagulant therapy increases the risk of intracranial hemorrhage by only 0.2% per year.¹

RISK-PREDICTION TOOLS HAVE LIMITATIONS

Not all patients have the same risk of bleeding when taking anticoagulants. Many factors in addition to advanced age have been associated with increased bleeding risk, including coexisting medical conditions (such as malignancy, prior stroke or bleeding event, and renal insufficiency), medications (particularly aspirin, nonsteroidal anti-inflammatory drugs, and other antiplatelet drugs), and the timing and intensity of anticoagulation therapy.⁴

Scoring tools have been developed to identify patients at higher risk of bleeding (Table 1).^4–9 The various schemes incorporate many of the same variables, such as older age, renal impairment, and history of bleeding, but some include additional risk factors while others are more parsimonious. They also differ in how individual risk factors are weighted to generate a final risk score.

In terms of predictive ability, none of the available risk schemes appears to be vastly superior, and their ability to predict hemorrhage is modest at best. There is also no universal or well-established threshold at which the risk of bleeding is so high that one would not consider anticoagulants. In fact, a “high-risk” patient may have an aggregate bleeding rate of only 4% to 6% per year. Using risk schemes such as ATRIA,⁵ HEMORR₂HAGES,⁶ and HAS-BLED⁷ may be more useful because they provide an estimate of bleeding risk for each point on the scale.

Moreover, the current tools to predict bleeding risk have several other limitations. They were developed in patients already taking anticoagulants and so probably underestimate the actual risk of hemorrhage, as people who could not take anticoagulants were excluded, most likely because they were at high risk of bleeding. Therefore, bleeding risk tools probably apply best to a patient for whom anticoagulation can be considered.

Some clinical variables are necessarily broad. For example, “prior bleeding” is a risk factor included in several risk scores, but does not distinguish between massive variceal bleeding and minor hemorrhoidal bleeding.

Risk scores do not effectively predict intracranial hemorrhage.

Finally, these risk tools were developed in patients taking vitamin K antagonists, and it is not yet established that they can effectively predict hemorrhage related to other, newer anticoagulants.

WHEN DOES BLEEDING RISK OUTWEIGH ANTICOAGULATION BENEFIT?

For patients with atrial fibrillation, the net clinical benefit of anticoagulation (strokes prevented minus bleeding events induced) increases as the risk of stroke rises. Updated guidelines for managing atrial fibrillation now recommend anticoagulation for most patients.¹⁰

For most older patients with atrial fibrillation, the decision to anticoagulate may not change even if a bleeding risk tool indicates a high bleeding risk.¹¹ For example, a patient with a history of ischemic stroke will generally derive more benefit than harm from anticoagulants. The primary exception is in patients with prior lobar intracranial hemorrhage, because of the high risk of rebleeding and the worse outcomes associated with intracranial hemorrhage.¹² As a general rule, most patients with atrial fibrillation and an additional risk factor for stroke should be considered for anticoagulant therapy unless they have a history of lobar intracranial hemorrhage.

Anticoagulation may be deferred if the patient is at the lower end of the stroke risk spectrum and if the bleeding risk is calculated to be high. However, as noted before, current bleeding risk tools probably do not capture the experiences of patients at the extremes of high bleeding risk, so clinical judgment continues to be important. In addition, forgoing anticoagulation could be reasonable even in patients at high risk for recurrent stroke if their life expectancy is limited, if anticoagulation is unacceptably burdensome, or if it is not within their goals and preferences.

WHAT ABOUT FALL RISK?

Fall risk commonly deters clinicians from prescribing anticoagulants because of the fear of causing intracranial hemorrhage. In particular, falls increase the risk for subdural hematoma, which has a death rate comparable to that of ischemic stroke.¹³

Studies have had difficulty quantifying the exact risk associated with falls because these patients are less likely to be prescribed anticoagulants. One decision analysis estimated that a person would have to fall about 300 times per year before the risk of intracranial hemorrhage outweighed the benefits from stroke reduction.¹⁴ Studies have found that patients at high risk of falls have a higher risk of intracranial hemorrhage, but that this risk is counterbalanced by an even greater risk of ischemic stroke.¹⁵

Therefore, if the baseline risk of ischemic stroke is high, anticoagulation is still favored.

WHEN SHOULD I USE A BLEEDING RISK TOOL?

Despite their limitations, bleeding risk tools are useful in clinical practice when estimates of bleeding risk affect clinical behavior. They are most helpful for patients at the lower end of the stroke or thromboembolism risk spectrum, where the decision to anticoagulate is strongly influenced by bleeding risk. Risk tools may also be helpful when counseling patients about their bleeding risk off and on anticoagulants.

Finally, recognizing that a patient is at high bleeding risk may lead the clinician to consider closer monitoring of anticoagulants or to implement strategies to reduce the risk.

We have tools to predict bleeding risk, but their predictive value is modest, and the estimated risk of bleeding is often outweighed by the benefits of anticoagulant therapy.

Anticoagulant therapy is commonly prescribed for conditions that disproportionately affect the elderly, including atrial fibrillation, venous thromboembolism, and valvular heart disease. Though anticoagulants are highly effective in preventing clots, they also significantly increase the risk of bleeding. Since older age is a risk factor for bleeding as well as thrombosis, it is essential to weigh the risks and benefits of anticoagulants for each patient.

WHAT KINDS OF BLEEDING DEVELOP IN PATIENTS ON ANTICOAGULANTS?

Patients taking anticoagulants have roughly double the risk of bleeding compared with patients not on anticoagulants.¹ Bleeding rates tend to be slightly higher in patients taking anticoagulants for venous thromboembolism than in those taking them for atrial fibrillation. The average yearly risk of a “major” anticoagulant-associated bleeding event (eg, requiring transfusion or intervention or occurring in a critical anatomic site) is about 2% to 3%, with most of the bleeding being gastrointestinal.²

Intracranial hemorrhage is by far the most deadly complication of anticoagulant therapy: it causes 90% of deaths and disability from warfarin-associated hemorrhage and is associated with a death rate over 50%; however, it is much less common than gastrointestinal bleeding.³ Anticoagulant therapy increases the risk of intracranial hemorrhage by only 0.2% per year.¹

RISK-PREDICTION TOOLS HAVE LIMITATIONS

Not all patients have the same risk of bleeding when taking anticoagulants. Many factors in addition to advanced age have been associated with increased bleeding risk, including coexisting medical conditions (such as malignancy, prior stroke or bleeding event, and renal insufficiency), medications (particularly aspirin, nonsteroidal anti-inflammatory drugs, and other antiplatelet drugs), and the timing and intensity of anticoagulation therapy.⁴

Scoring tools have been developed to identify patients at higher risk of bleeding (Table 1).^4–9 The various schemes incorporate many of the same variables, such as older age, renal impairment, and history of bleeding, but some include additional risk factors while others are more parsimonious. They also differ in how individual risk factors are weighted to generate a final risk score.

In terms of predictive ability, none of the available risk schemes appears to be vastly superior, and their ability to predict hemorrhage is modest at best. There is also no universal or well-established threshold at which the risk of bleeding is so high that one would not consider anticoagulants. In fact, a “high-risk” patient may have an aggregate bleeding rate of only 4% to 6% per year. Using risk schemes such as ATRIA,⁵ HEMORR₂HAGES,⁶ and HAS-BLED⁷ may be more useful because they provide an estimate of bleeding risk for each point on the scale.

Moreover, the current tools to predict bleeding risk have several other limitations. They were developed in patients already taking anticoagulants and so probably underestimate the actual risk of hemorrhage, as people who could not take anticoagulants were excluded, most likely because they were at high risk of bleeding. Therefore, bleeding risk tools probably apply best to a patient for whom anticoagulation can be considered.

Some clinical variables are necessarily broad. For example, “prior bleeding” is a risk factor included in several risk scores, but does not distinguish between massive variceal bleeding and minor hemorrhoidal bleeding.

Risk scores do not effectively predict intracranial hemorrhage.

Finally, these risk tools were developed in patients taking vitamin K antagonists, and it is not yet established that they can effectively predict hemorrhage related to other, newer anticoagulants.

WHEN DOES BLEEDING RISK OUTWEIGH ANTICOAGULATION BENEFIT?

For patients with atrial fibrillation, the net clinical benefit of anticoagulation (strokes prevented minus bleeding events induced) increases as the risk of stroke rises. Updated guidelines for managing atrial fibrillation now recommend anticoagulation for most patients.¹⁰

For most older patients with atrial fibrillation, the decision to anticoagulate may not change even if a bleeding risk tool indicates a high bleeding risk.¹¹ For example, a patient with a history of ischemic stroke will generally derive more benefit than harm from anticoagulants. The primary exception is in patients with prior lobar intracranial hemorrhage, because of the high risk of rebleeding and the worse outcomes associated with intracranial hemorrhage.¹² As a general rule, most patients with atrial fibrillation and an additional risk factor for stroke should be considered for anticoagulant therapy unless they have a history of lobar intracranial hemorrhage.

Anticoagulation may be deferred if the patient is at the lower end of the stroke risk spectrum and if the bleeding risk is calculated to be high. However, as noted before, current bleeding risk tools probably do not capture the experiences of patients at the extremes of high bleeding risk, so clinical judgment continues to be important. In addition, forgoing anticoagulation could be reasonable even in patients at high risk for recurrent stroke if their life expectancy is limited, if anticoagulation is unacceptably burdensome, or if it is not within their goals and preferences.

WHAT ABOUT FALL RISK?

Fall risk commonly deters clinicians from prescribing anticoagulants because of the fear of causing intracranial hemorrhage. In particular, falls increase the risk for subdural hematoma, which has a death rate comparable to that of ischemic stroke.¹³

Studies have had difficulty quantifying the exact risk associated with falls because these patients are less likely to be prescribed anticoagulants. One decision analysis estimated that a person would have to fall about 300 times per year before the risk of intracranial hemorrhage outweighed the benefits from stroke reduction.¹⁴ Studies have found that patients at high risk of falls have a higher risk of intracranial hemorrhage, but that this risk is counterbalanced by an even greater risk of ischemic stroke.¹⁵

Therefore, if the baseline risk of ischemic stroke is high, anticoagulation is still favored.

WHEN SHOULD I USE A BLEEDING RISK TOOL?

Despite their limitations, bleeding risk tools are useful in clinical practice when estimates of bleeding risk affect clinical behavior. They are most helpful for patients at the lower end of the stroke or thromboembolism risk spectrum, where the decision to anticoagulate is strongly influenced by bleeding risk. Risk tools may also be helpful when counseling patients about their bleeding risk off and on anticoagulants.

Finally, recognizing that a patient is at high bleeding risk may lead the clinician to consider closer monitoring of anticoagulants or to implement strategies to reduce the risk.

We have tools to predict bleeding risk, but their predictive value is modest, and the estimated risk of bleeding is often outweighed by the benefits of anticoagulant therapy.

Anticoagulant therapy is commonly prescribed for conditions that disproportionately affect the elderly, including atrial fibrillation, venous thromboembolism, and valvular heart disease. Though anticoagulants are highly effective in preventing clots, they also significantly increase the risk of bleeding. Since older age is a risk factor for bleeding as well as thrombosis, it is essential to weigh the risks and benefits of anticoagulants for each patient.

WHAT KINDS OF BLEEDING DEVELOP IN PATIENTS ON ANTICOAGULANTS?

Patients taking anticoagulants have roughly double the risk of bleeding compared with patients not on anticoagulants.¹ Bleeding rates tend to be slightly higher in patients taking anticoagulants for venous thromboembolism than in those taking them for atrial fibrillation. The average yearly risk of a “major” anticoagulant-associated bleeding event (eg, requiring transfusion or intervention or occurring in a critical anatomic site) is about 2% to 3%, with most of the bleeding being gastrointestinal.²

Intracranial hemorrhage is by far the most deadly complication of anticoagulant therapy: it causes 90% of deaths and disability from warfarin-associated hemorrhage and is associated with a death rate over 50%; however, it is much less common than gastrointestinal bleeding.³ Anticoagulant therapy increases the risk of intracranial hemorrhage by only 0.2% per year.¹

RISK-PREDICTION TOOLS HAVE LIMITATIONS

Not all patients have the same risk of bleeding when taking anticoagulants. Many factors in addition to advanced age have been associated with increased bleeding risk, including coexisting medical conditions (such as malignancy, prior stroke or bleeding event, and renal insufficiency), medications (particularly aspirin, nonsteroidal anti-inflammatory drugs, and other antiplatelet drugs), and the timing and intensity of anticoagulation therapy.⁴

Scoring tools have been developed to identify patients at higher risk of bleeding (Table 1).^4–9 The various schemes incorporate many of the same variables, such as older age, renal impairment, and history of bleeding, but some include additional risk factors while others are more parsimonious. They also differ in how individual risk factors are weighted to generate a final risk score.

In terms of predictive ability, none of the available risk schemes appears to be vastly superior, and their ability to predict hemorrhage is modest at best. There is also no universal or well-established threshold at which the risk of bleeding is so high that one would not consider anticoagulants. In fact, a “high-risk” patient may have an aggregate bleeding rate of only 4% to 6% per year. Using risk schemes such as ATRIA,⁵ HEMORR₂HAGES,⁶ and HAS-BLED⁷ may be more useful because they provide an estimate of bleeding risk for each point on the scale.

Moreover, the current tools to predict bleeding risk have several other limitations. They were developed in patients already taking anticoagulants and so probably underestimate the actual risk of hemorrhage, as people who could not take anticoagulants were excluded, most likely because they were at high risk of bleeding. Therefore, bleeding risk tools probably apply best to a patient for whom anticoagulation can be considered.

Some clinical variables are necessarily broad. For example, “prior bleeding” is a risk factor included in several risk scores, but does not distinguish between massive variceal bleeding and minor hemorrhoidal bleeding.

Risk scores do not effectively predict intracranial hemorrhage.

Finally, these risk tools were developed in patients taking vitamin K antagonists, and it is not yet established that they can effectively predict hemorrhage related to other, newer anticoagulants.

WHEN DOES BLEEDING RISK OUTWEIGH ANTICOAGULATION BENEFIT?

For patients with atrial fibrillation, the net clinical benefit of anticoagulation (strokes prevented minus bleeding events induced) increases as the risk of stroke rises. Updated guidelines for managing atrial fibrillation now recommend anticoagulation for most patients.¹⁰

For most older patients with atrial fibrillation, the decision to anticoagulate may not change even if a bleeding risk tool indicates a high bleeding risk.¹¹ For example, a patient with a history of ischemic stroke will generally derive more benefit than harm from anticoagulants. The primary exception is in patients with prior lobar intracranial hemorrhage, because of the high risk of rebleeding and the worse outcomes associated with intracranial hemorrhage.¹² As a general rule, most patients with atrial fibrillation and an additional risk factor for stroke should be considered for anticoagulant therapy unless they have a history of lobar intracranial hemorrhage.

Anticoagulation may be deferred if the patient is at the lower end of the stroke risk spectrum and if the bleeding risk is calculated to be high. However, as noted before, current bleeding risk tools probably do not capture the experiences of patients at the extremes of high bleeding risk, so clinical judgment continues to be important. In addition, forgoing anticoagulation could be reasonable even in patients at high risk for recurrent stroke if their life expectancy is limited, if anticoagulation is unacceptably burdensome, or if it is not within their goals and preferences.

WHAT ABOUT FALL RISK?

Fall risk commonly deters clinicians from prescribing anticoagulants because of the fear of causing intracranial hemorrhage. In particular, falls increase the risk for subdural hematoma, which has a death rate comparable to that of ischemic stroke.¹³

Studies have had difficulty quantifying the exact risk associated with falls because these patients are less likely to be prescribed anticoagulants. One decision analysis estimated that a person would have to fall about 300 times per year before the risk of intracranial hemorrhage outweighed the benefits from stroke reduction.¹⁴ Studies have found that patients at high risk of falls have a higher risk of intracranial hemorrhage, but that this risk is counterbalanced by an even greater risk of ischemic stroke.¹⁵

Therefore, if the baseline risk of ischemic stroke is high, anticoagulation is still favored.

WHEN SHOULD I USE A BLEEDING RISK TOOL?

Despite their limitations, bleeding risk tools are useful in clinical practice when estimates of bleeding risk affect clinical behavior. They are most helpful for patients at the lower end of the stroke or thromboembolism risk spectrum, where the decision to anticoagulate is strongly influenced by bleeding risk. Risk tools may also be helpful when counseling patients about their bleeding risk off and on anticoagulants.

Finally, recognizing that a patient is at high bleeding risk may lead the clinician to consider closer monitoring of anticoagulants or to implement strategies to reduce the risk.