Henry J. Michtalik, MD, MPH, MHS

Hospitalists’ performance is routinely evaluated by third-party payers, employers, and patients. As hospitalist programs mature, there is a need to develop processes to identify, internally measure, and report on individual and group performance. We know from Society of Hospital Medicine (SHM) data that a significant amount of hospitalists’ total compensation is at least partially based on performance. Often this is based at least in part on quality data. In 2006, SHM issued a white paper detailing the key elements of a successful performance monitoring and reporting process.^1,2 Recommendations included the identification of meaningful operational and clinical performance metrics, and the ability to monitor and report both group and individual metrics was highlighted as an essential component. There is evidence that comparison of individual provider performance with that of their peers is a necessary element of successful provider dashboards.³ Additionally, regular feedback and a clear, visual presentation of the data are important components of successful provider feedback dashboards.^3-6

Much of the literature regarding provider feedback dashboards has been based in the outpatient setting. The majority of these dashboards focus on the management of chronic illnesses (eg, diabetes and hypertension), rates of preventative care services (eg, colonoscopy or mammogram), or avoidance of unnecessary care (eg, antibiotics for sinusitis).^4,5 Unlike in the outpatient setting, in which 1 provider often provides a majority of the care for a given episode of care, hospitalized patients are often cared for by multiple providers, challenging the appropriate attribution of patient-level metrics to specific providers. Under the standard approach, an entire hospitalization is attributed to 1 physician, generally the attending of record for the hospitalization, which may be the admitting provider or the discharging provider, depending on the approach used by the hospital. However, assigning responsibility for an entire hospitalization to a provider who may have only seen the patient for a small percentage of a hospitalization may jeopardize the validity of metrics. As provider metrics are increasingly being used for compensation, it is important to ensure that the method for attribution correctly identifies the providers caring for patients. To our knowledge there is no gold standard approach for attributing metrics to providers when patients are cared for by multiple providers, and the standard attending of record–based approach may lack face validity in many cases.

We aimed to develop and operationalize a system to more fairly attribute patient-level data to individual providers across a single hospitalization even when multiple providers cared for the patient. We then compared our methodology to the standard approach, in which the attending of record receives full attribution for each metric, to determine the difference on a provider level between the 2 models.

Clinical Setting

The Johns Hopkins Hospital is a 1145-bed, tertiary-care hospital. Over the years of this project, the Johns Hopkins Hospitalist Program was an approximately 20-physician group providing care in a variety of settings, including a dedicated hospitalist floor, where this metrics program was initiated. Hospitalists in this setting work Monday through Friday, with 1 hospitalist and a moonlighter covering on the weekends. Admissions are performed by an admitter, and overnight care is provided by a nocturnist. Initially 17 beds, this unit expanded to 24 beds in June 2012. For the purposes of this article, we included all general medicine patients admitted to this floor between July 1, 2010, and June 30, 2014, who were cared for by hospitalists. During this period, all patients were inpatients; no patients were admitted under observation status. All of these patients were cared for by hospitalists without housestaff or advanced practitioners. Since 2014, the metrics program has been expanded to other hospitalist-run services in the hospital, but for simplicity, we have not presented these more recent data.

Individual Provider Metrics

Metrics were chosen to reflect institutional quality and efficiency priorities. Our choice of metrics was restricted to those that (1) plausibly reflect provider performance, at least in part, and (2) could be accessed in electronic form (without any manual chart review). Whenever possible, we chose metrics with objective data. Additionally, because funding for this effort was provided by the hospital, we sought to ensure that enough of the metrics were related to cost to justify ongoing hospital support of the project. SAS 9.2 (SAS Institute Inc, Cary, NC) was used to calculate metric weights. Specific metrics included American College of Chest Physicians (ACCP)–compliant venous thromboembolism (VTE) prophylaxis,⁷ observed-to-expected length of stay (LOS) ratio, percentage of discharges per day, discharges before 3 pm, depth of coding, patient satisfaction, readmissions, communication with the primary care provider, and time to signature for discharge summaries (Table 1).

Appropriate prophylaxis for VTE was calculated by using an algorithm embedded within the computerized provider order entry system, which assessed the prescription of ACCP-compliant VTE prophylaxis within 24 hours following admission. This included a risk assessment, and credit was given for no prophylaxis and/or mechanical and/or pharmacologic prophylaxis per the ACCP guidelines.⁷

Observed-to-expected LOS was defined by using the University HealthSystem Consortium (UHC; now Vizient Inc) expected LOS for the given calendar year. This approach incorporates patient diagnoses, demographics, and other administrative variables to define an expected LOS for each patient.

The percent of patients discharged per day was defined from billing data as the percentage of a provider’s evaluation and management charges that were the final charge of a patient’s stay (regardless of whether a discharge day service was coded).

Discharge prior to 3 pm was defined from administrative data as the time a patient was discharged from the electronic medical system.

Depth of coding was defined as the number of coded diagnoses submitted to the Maryland Health Services Cost Review Commission for determining payment and was viewed as an indicator of the thoroughness of provider documentation.

Patient satisfaction was defined at the patient level (for those patients who turned in patient satisfaction surveys) as the pooled value of the 5 provider questions on the hospital’s patient satisfaction survey administered by Press Ganey: “time the physician spent with you,” “did the physician show concern for your questions/worries,” “did the physician keep you informed,” “friendliness/courtesy of the physician,” and “skill of the physician.”⁸

Readmission rates were defined as same-hospital readmissions divided by the total number of patients discharged by a given provider, with exclusions based on the Centers for Medicare and Medicaid Services hospital-wide, all-cause readmission measure.¹ The expected same-hospital readmission rate was defined for each patient as the observed readmission rate in the entire UHC (Vizient) data set for all patients with the same All Patient Refined Diagnosis Related Group and severity of illness, as we have described previously.⁹

Communication with the primary care provider was the only self-reported metric used. It was based on a mandatory prompt on the discharge worksheet in the electronic medical record (EMR). Successful communication with the outpatient provider was defined as verbal or electronic communication by the hospitalist with the outpatient provider. Partial (50%) credit was given for providers who attempted but were unsuccessful in communicating with the outpatient provider, for patients for whom the provider had access to the Johns Hopkins EMR system, and for planned admissions without new or important information to convey. No credit was given for providers who indicated that communication was not indicated, who indicated that a patient and/or family would update the provider, or who indicated that the discharge summary would be sufficient.⁹ Because the discharge worksheet could be initiated at any time during the hospitalization, providers could document communication with the outpatient provider at any point during hospitalization.

Discharge summary turnaround was defined as the average number of days elapsed between the day of discharge and the signing of the discharge summary in the EMR.

Assigning Ownership of Patients to Individual Providers

Using billing data, we assigned ownership of patient care based on the type, timing, and number of charges that occurred during each hospitalization (Figure 1). Eligible charges included all history and physical (codes 99221, 99222, and 99223), subsequent care (codes 99231, 99232, and 99233), and discharge charges (codes 99238 and 99239).

By using a unique identifier assigned for each hospitalization, professional fees submitted by providers were used to identify which provider saw the patient on the admission day, discharge day, as well as subsequent care days. Providers’ productivity, bonus supplements, and policy compliance were determined by using billing data, which encouraged the prompt submittal of charges.

The provider who billed the admission history and physical (codes 99221, 99222, and 99223) within 1 calendar date of the patient’s initial admission was defined as the admitting provider. Patients transferred to the hospitalist service from other services were not assigned an admitting hospitalist. The sole metric assigned to the admitting hospitalist was ACCP-compliant VTE prophylaxis.

The provider who billed the final subsequent care or discharge code (codes 99231, 99232, 99233, 99238, and 99239) within 1 calendar date of discharge was defined as the discharging provider. For hospitalizations characterized by a single provider charge (eg, for patients admitted and discharged on the same day), the provider billing this charge was assigned as both the admitting and discharging physician. Patients upgraded to the intensive care unit (ICU) were not counted as a discharge unless the patient was downgraded and discharged from the hospitalist service. The discharging provider was assigned responsibility for the time of discharge, the percent of patients discharged per day, the discharge summary turnaround time, and hospital readmissions.

Metrics that were assigned to multiple providers for a single hospitalization were termed “provider day–weighted” metrics. The formula for calculating the weight for each provider day–weighted metric was as follows: weight for provider A = [number of daily charges billed by provider A] divided by [LOS +1]. The initial hospital day was counted as day 0. LOS plus 1 was used to recognize that a typical hospitalization will have a charge on the day of admission (day 0) and a charge on the day of discharge such that an LOS of 2 days (eg, a patient admitted on Monday and discharged on Wednesday) will have 3 daily charges. Provider day–weighted metrics included patient satisfaction, communication with the outpatient provider, depth of coding, and observed-to-expected LOS.

Our billing software prevented providers from the same group from billing multiple daily charges, thus ensuring that there were no duplicated charges submitted for a given day.

Presenting Results

Providers were only shown data from the day-weighted approach. For ease of visual interpretation, scores for each metric were scaled ordinally from 1 (worst performance) to 9 (best performance; Table 1). Data were displayed in a dashboard format on a password-protected website for each provider to view his or her own data relative to that of the hospitalist peer group. The dashboard was implemented in this format on July 1, 2011. Data were updated quarterly (Figure 2).

Results were displayed in a polyhedral or spider-web graph (Figure 2). Provider and group metrics were scaled according to predefined benchmarks established for each metric and standardized to a scale ranging from 1 to 9. The scale for each metric was set based on examining historical data and group median performance on the metrics to ensure that there was a range of performance (ie, to avoid having most hospitalists scoring a 1 or 9). Scaling thresholds were periodically adjusted as appropriate to maintain good visual discrimination. Higher scores (creating a larger-volume polygon) are desirable even for metrics such as LOS, for which a low value is desirable. Both a spider-web graph and trends over time were available to the provider (Figure 2). These graphs display a comparison of the individual provider scores for each metric to the hospitalist group average for that metric.

Comparison with the Standard (Attending of Record) Method of Attribution

For the purposes of this report, we sought to determine whether there were meaningful differences between our day-weighted approach versus the standard method of attribution, in which the attending of record is assigned responsibility for each metric that would not have been attributed to the discharging attending under both methods. Our goal was to determine where and whether there was a meaningful difference between the 2 methodologies, recognizing that the degree of difference between these 2 methodologies might vary in other institutions and settings. In our hospital, the attending of record is generally the discharging attending. In order to compare the 2 methodologies, we arbitrarily picked 2015 to retrospectively evaluate the differences between these 2 methods of attribution. We did not display or provide data using the standard methodology to providers at any point; this approach was used only for the purposes of this report. Because these metrics are intended to evaluate relative provider performance, we assigned a percentile to each provider for his or her performance on the given metric using our attribution methodology and then, similarly, assigned a percentile to each provider using the standard methodology. This yielded 2 percentile scores for each provider and each metric. We then compared these percentile ranks for providers in 2 ways: (1) we determined how often providers who scored in the top half of the group for a given metric (above the 50th percentile) also scored in the top half of the group for that metric by using the other calculation method, and (2) we calculated the absolute value of the difference in percentiles between the 2 methods to characterize the impact on a provider’s ranking for that metric that might result from switching to the other method. For instance, if a provider scored at the 20th percentile for the group in patient satisfaction with 1 attribution method and scored at the 40th percentile for the group in patient satisfaction using the other method, the absolute change in percentile would be 20 percentile points. But, this provider would still be below the 50th percentile by both methods (concordant bottom half performance). We did not perform this comparison for metrics assigned to the discharging provider (such as discharge summary turnaround time or readmissions) because the attending of record designation is assigned to the discharging provider at our hospital.

The dashboard was successfully operationalized on July 1, 2011, with displays visible to providers as shown in Figure 2. Consistent with the principles of providing effective performance feedback to providers, the display simultaneously showed providers their individual performance as well as the performance of their peers. Providers were able to view their spider-web plot for prior quarters. Not shown are additional views that allowed providers to see quarterly trends in their data versus their peers across several fiscal years. Also available to providers was their ranking relative to their peers for each metric; specific peers were deidentified in the display.

There was notable discordance between provider rankings between the 2 methodologies, as shown in Table 2. Provider performance above or below the median was concordant 56% to 75% of the time (depending on the particular metric), indicating substantial discordance because top-half or bottom-half concordance would be expected to occur by chance 50% of the time. Although the provider percentile differences between the 2 methods tended to be modest for most providers (the median difference between the methods was 13 to 22 percentile points for the various metrics), there were some providers for whom the method of calculation dramatically impacted their rankings. For 5 of the 6 metrics we examined, at least 1 provider had a 50-percentile or greater change in his or her ranking based on the method used. This indicates that at least some providers would have had markedly different scores relative to their peers had we used the alternative methodology (Table 2). In VTE prophylaxis, for example, at least 1 provider had a 94-percentile change in his or her ranking; similarly, a provider had an 88-perentile change in his or her LOS ranking between the 2 methodologies.

We found that it is possible to assign metrics across 1 hospital stay to multiple providers by using billing data. We also found a meaningful discrepancy in how well providers scored (relative to their peers) based on the method used for attribution. These results imply that hospitals should consider attributing performance metrics based on ascribed ownership from billing data and not just from attending of record status.

As hospitalist programs and providers in general are increasingly being asked to develop dashboards to monitor individual and group performance, correctly attributing care to providers is likely to become increasingly important. Experts agree that principles of effective provider performance dashboards include ranking individual provider performance relative to peers, clearly displaying data in an easily accessible format, and ensuring that data can be credibly attributed to the individual provider.^3,4,6 However, there appears to be no gold standard method for attribution, especially in the inpatient setting. Our results imply that hospitals should consider attributing performance metrics based on ascribed ownership from billing data and not just from attending of record status.

Several limitations of our findings are important to consider. First, our program is a relatively small, academic group with handoffs that typically occur every 1 to 2 weeks and sometimes with additional handoffs on weekends. Different care patterns and settings might impact the utility of our attribution methodology relative to the standard methodology. Additionally, it is important to note that the relative merits of the different methodologies cannot be ascertained from our comparison. We can demonstrate discordance between the attribution methodologies, but we cannot say that 1 method is correct and the other is flawed. Although we believe that our day-weighted approach feels fairer to providers based on group input and feedback, we did not conduct a formal survey to examine providers’ preferences for the standard versus day-weighted approaches. The appropriateness of a particular attribution method needs to be assessed locally and may vary based on the clinical setting. For instance, on a service in which patients are admitted for procedures, it may make more sense to attribute the outcome of the case to the proceduralist even if that provider did not bill for the patient’s care on a daily basis. Finally, the computational requirements of our methodology are not trivial and require linking billing data with administrative patient-level data, which may be challenging to operationalize in some institutions.

These limitations aside, we believe that our attribution methodology has face validity. For example, a provider might be justifiably frustrated if, using the standard methodology, he or she is charged with the LOS of a patient who had been hospitalized for months, particularly if that patient is discharged shortly after the provider assumes care. Our method addresses this type of misattribution. Particularly when individual provider compensation is based on performance on metrics (as is the case at our institution), optimizing provider attribution to particular patients may be important, and face validity may be required for group buy-in.

In summary, we have demonstrated that it is possible to use billing data to assign ownership of patients to multiple providers over 1 hospital stay. This could be applied to other hospitalist programs as well as other healthcare settings in which multiple providers care for patients during 1 healthcare encounter (eg, ICUs).

Disclosure

The authors declare they have no relevant conflicts of interest.

Hospitalists’ performance is routinely evaluated by third-party payers, employers, and patients. As hospitalist programs mature, there is a need to develop processes to identify, internally measure, and report on individual and group performance. We know from Society of Hospital Medicine (SHM) data that a significant amount of hospitalists’ total compensation is at least partially based on performance. Often this is based at least in part on quality data. In 2006, SHM issued a white paper detailing the key elements of a successful performance monitoring and reporting process.^1,2 Recommendations included the identification of meaningful operational and clinical performance metrics, and the ability to monitor and report both group and individual metrics was highlighted as an essential component. There is evidence that comparison of individual provider performance with that of their peers is a necessary element of successful provider dashboards.³ Additionally, regular feedback and a clear, visual presentation of the data are important components of successful provider feedback dashboards.^3-6

Much of the literature regarding provider feedback dashboards has been based in the outpatient setting. The majority of these dashboards focus on the management of chronic illnesses (eg, diabetes and hypertension), rates of preventative care services (eg, colonoscopy or mammogram), or avoidance of unnecessary care (eg, antibiotics for sinusitis).^4,5 Unlike in the outpatient setting, in which 1 provider often provides a majority of the care for a given episode of care, hospitalized patients are often cared for by multiple providers, challenging the appropriate attribution of patient-level metrics to specific providers. Under the standard approach, an entire hospitalization is attributed to 1 physician, generally the attending of record for the hospitalization, which may be the admitting provider or the discharging provider, depending on the approach used by the hospital. However, assigning responsibility for an entire hospitalization to a provider who may have only seen the patient for a small percentage of a hospitalization may jeopardize the validity of metrics. As provider metrics are increasingly being used for compensation, it is important to ensure that the method for attribution correctly identifies the providers caring for patients. To our knowledge there is no gold standard approach for attributing metrics to providers when patients are cared for by multiple providers, and the standard attending of record–based approach may lack face validity in many cases.

We aimed to develop and operationalize a system to more fairly attribute patient-level data to individual providers across a single hospitalization even when multiple providers cared for the patient. We then compared our methodology to the standard approach, in which the attending of record receives full attribution for each metric, to determine the difference on a provider level between the 2 models.

Clinical Setting

The Johns Hopkins Hospital is a 1145-bed, tertiary-care hospital. Over the years of this project, the Johns Hopkins Hospitalist Program was an approximately 20-physician group providing care in a variety of settings, including a dedicated hospitalist floor, where this metrics program was initiated. Hospitalists in this setting work Monday through Friday, with 1 hospitalist and a moonlighter covering on the weekends. Admissions are performed by an admitter, and overnight care is provided by a nocturnist. Initially 17 beds, this unit expanded to 24 beds in June 2012. For the purposes of this article, we included all general medicine patients admitted to this floor between July 1, 2010, and June 30, 2014, who were cared for by hospitalists. During this period, all patients were inpatients; no patients were admitted under observation status. All of these patients were cared for by hospitalists without housestaff or advanced practitioners. Since 2014, the metrics program has been expanded to other hospitalist-run services in the hospital, but for simplicity, we have not presented these more recent data.

Individual Provider Metrics

Metrics were chosen to reflect institutional quality and efficiency priorities. Our choice of metrics was restricted to those that (1) plausibly reflect provider performance, at least in part, and (2) could be accessed in electronic form (without any manual chart review). Whenever possible, we chose metrics with objective data. Additionally, because funding for this effort was provided by the hospital, we sought to ensure that enough of the metrics were related to cost to justify ongoing hospital support of the project. SAS 9.2 (SAS Institute Inc, Cary, NC) was used to calculate metric weights. Specific metrics included American College of Chest Physicians (ACCP)–compliant venous thromboembolism (VTE) prophylaxis,⁷ observed-to-expected length of stay (LOS) ratio, percentage of discharges per day, discharges before 3 pm, depth of coding, patient satisfaction, readmissions, communication with the primary care provider, and time to signature for discharge summaries (Table 1).

Appropriate prophylaxis for VTE was calculated by using an algorithm embedded within the computerized provider order entry system, which assessed the prescription of ACCP-compliant VTE prophylaxis within 24 hours following admission. This included a risk assessment, and credit was given for no prophylaxis and/or mechanical and/or pharmacologic prophylaxis per the ACCP guidelines.⁷

Observed-to-expected LOS was defined by using the University HealthSystem Consortium (UHC; now Vizient Inc) expected LOS for the given calendar year. This approach incorporates patient diagnoses, demographics, and other administrative variables to define an expected LOS for each patient.

The percent of patients discharged per day was defined from billing data as the percentage of a provider’s evaluation and management charges that were the final charge of a patient’s stay (regardless of whether a discharge day service was coded).

Discharge prior to 3 pm was defined from administrative data as the time a patient was discharged from the electronic medical system.

Depth of coding was defined as the number of coded diagnoses submitted to the Maryland Health Services Cost Review Commission for determining payment and was viewed as an indicator of the thoroughness of provider documentation.

Patient satisfaction was defined at the patient level (for those patients who turned in patient satisfaction surveys) as the pooled value of the 5 provider questions on the hospital’s patient satisfaction survey administered by Press Ganey: “time the physician spent with you,” “did the physician show concern for your questions/worries,” “did the physician keep you informed,” “friendliness/courtesy of the physician,” and “skill of the physician.”⁸

Readmission rates were defined as same-hospital readmissions divided by the total number of patients discharged by a given provider, with exclusions based on the Centers for Medicare and Medicaid Services hospital-wide, all-cause readmission measure.¹ The expected same-hospital readmission rate was defined for each patient as the observed readmission rate in the entire UHC (Vizient) data set for all patients with the same All Patient Refined Diagnosis Related Group and severity of illness, as we have described previously.⁹

Communication with the primary care provider was the only self-reported metric used. It was based on a mandatory prompt on the discharge worksheet in the electronic medical record (EMR). Successful communication with the outpatient provider was defined as verbal or electronic communication by the hospitalist with the outpatient provider. Partial (50%) credit was given for providers who attempted but were unsuccessful in communicating with the outpatient provider, for patients for whom the provider had access to the Johns Hopkins EMR system, and for planned admissions without new or important information to convey. No credit was given for providers who indicated that communication was not indicated, who indicated that a patient and/or family would update the provider, or who indicated that the discharge summary would be sufficient.⁹ Because the discharge worksheet could be initiated at any time during the hospitalization, providers could document communication with the outpatient provider at any point during hospitalization.

Discharge summary turnaround was defined as the average number of days elapsed between the day of discharge and the signing of the discharge summary in the EMR.

Assigning Ownership of Patients to Individual Providers

Using billing data, we assigned ownership of patient care based on the type, timing, and number of charges that occurred during each hospitalization (Figure 1). Eligible charges included all history and physical (codes 99221, 99222, and 99223), subsequent care (codes 99231, 99232, and 99233), and discharge charges (codes 99238 and 99239).

By using a unique identifier assigned for each hospitalization, professional fees submitted by providers were used to identify which provider saw the patient on the admission day, discharge day, as well as subsequent care days. Providers’ productivity, bonus supplements, and policy compliance were determined by using billing data, which encouraged the prompt submittal of charges.

The provider who billed the admission history and physical (codes 99221, 99222, and 99223) within 1 calendar date of the patient’s initial admission was defined as the admitting provider. Patients transferred to the hospitalist service from other services were not assigned an admitting hospitalist. The sole metric assigned to the admitting hospitalist was ACCP-compliant VTE prophylaxis.

The provider who billed the final subsequent care or discharge code (codes 99231, 99232, 99233, 99238, and 99239) within 1 calendar date of discharge was defined as the discharging provider. For hospitalizations characterized by a single provider charge (eg, for patients admitted and discharged on the same day), the provider billing this charge was assigned as both the admitting and discharging physician. Patients upgraded to the intensive care unit (ICU) were not counted as a discharge unless the patient was downgraded and discharged from the hospitalist service. The discharging provider was assigned responsibility for the time of discharge, the percent of patients discharged per day, the discharge summary turnaround time, and hospital readmissions.

Metrics that were assigned to multiple providers for a single hospitalization were termed “provider day–weighted” metrics. The formula for calculating the weight for each provider day–weighted metric was as follows: weight for provider A = [number of daily charges billed by provider A] divided by [LOS +1]. The initial hospital day was counted as day 0. LOS plus 1 was used to recognize that a typical hospitalization will have a charge on the day of admission (day 0) and a charge on the day of discharge such that an LOS of 2 days (eg, a patient admitted on Monday and discharged on Wednesday) will have 3 daily charges. Provider day–weighted metrics included patient satisfaction, communication with the outpatient provider, depth of coding, and observed-to-expected LOS.

Our billing software prevented providers from the same group from billing multiple daily charges, thus ensuring that there were no duplicated charges submitted for a given day.

Presenting Results

Providers were only shown data from the day-weighted approach. For ease of visual interpretation, scores for each metric were scaled ordinally from 1 (worst performance) to 9 (best performance; Table 1). Data were displayed in a dashboard format on a password-protected website for each provider to view his or her own data relative to that of the hospitalist peer group. The dashboard was implemented in this format on July 1, 2011. Data were updated quarterly (Figure 2).

Results were displayed in a polyhedral or spider-web graph (Figure 2). Provider and group metrics were scaled according to predefined benchmarks established for each metric and standardized to a scale ranging from 1 to 9. The scale for each metric was set based on examining historical data and group median performance on the metrics to ensure that there was a range of performance (ie, to avoid having most hospitalists scoring a 1 or 9). Scaling thresholds were periodically adjusted as appropriate to maintain good visual discrimination. Higher scores (creating a larger-volume polygon) are desirable even for metrics such as LOS, for which a low value is desirable. Both a spider-web graph and trends over time were available to the provider (Figure 2). These graphs display a comparison of the individual provider scores for each metric to the hospitalist group average for that metric.

Comparison with the Standard (Attending of Record) Method of Attribution

For the purposes of this report, we sought to determine whether there were meaningful differences between our day-weighted approach versus the standard method of attribution, in which the attending of record is assigned responsibility for each metric that would not have been attributed to the discharging attending under both methods. Our goal was to determine where and whether there was a meaningful difference between the 2 methodologies, recognizing that the degree of difference between these 2 methodologies might vary in other institutions and settings. In our hospital, the attending of record is generally the discharging attending. In order to compare the 2 methodologies, we arbitrarily picked 2015 to retrospectively evaluate the differences between these 2 methods of attribution. We did not display or provide data using the standard methodology to providers at any point; this approach was used only for the purposes of this report. Because these metrics are intended to evaluate relative provider performance, we assigned a percentile to each provider for his or her performance on the given metric using our attribution methodology and then, similarly, assigned a percentile to each provider using the standard methodology. This yielded 2 percentile scores for each provider and each metric. We then compared these percentile ranks for providers in 2 ways: (1) we determined how often providers who scored in the top half of the group for a given metric (above the 50th percentile) also scored in the top half of the group for that metric by using the other calculation method, and (2) we calculated the absolute value of the difference in percentiles between the 2 methods to characterize the impact on a provider’s ranking for that metric that might result from switching to the other method. For instance, if a provider scored at the 20th percentile for the group in patient satisfaction with 1 attribution method and scored at the 40th percentile for the group in patient satisfaction using the other method, the absolute change in percentile would be 20 percentile points. But, this provider would still be below the 50th percentile by both methods (concordant bottom half performance). We did not perform this comparison for metrics assigned to the discharging provider (such as discharge summary turnaround time or readmissions) because the attending of record designation is assigned to the discharging provider at our hospital.

The dashboard was successfully operationalized on July 1, 2011, with displays visible to providers as shown in Figure 2. Consistent with the principles of providing effective performance feedback to providers, the display simultaneously showed providers their individual performance as well as the performance of their peers. Providers were able to view their spider-web plot for prior quarters. Not shown are additional views that allowed providers to see quarterly trends in their data versus their peers across several fiscal years. Also available to providers was their ranking relative to their peers for each metric; specific peers were deidentified in the display.

There was notable discordance between provider rankings between the 2 methodologies, as shown in Table 2. Provider performance above or below the median was concordant 56% to 75% of the time (depending on the particular metric), indicating substantial discordance because top-half or bottom-half concordance would be expected to occur by chance 50% of the time. Although the provider percentile differences between the 2 methods tended to be modest for most providers (the median difference between the methods was 13 to 22 percentile points for the various metrics), there were some providers for whom the method of calculation dramatically impacted their rankings. For 5 of the 6 metrics we examined, at least 1 provider had a 50-percentile or greater change in his or her ranking based on the method used. This indicates that at least some providers would have had markedly different scores relative to their peers had we used the alternative methodology (Table 2). In VTE prophylaxis, for example, at least 1 provider had a 94-percentile change in his or her ranking; similarly, a provider had an 88-perentile change in his or her LOS ranking between the 2 methodologies.

We found that it is possible to assign metrics across 1 hospital stay to multiple providers by using billing data. We also found a meaningful discrepancy in how well providers scored (relative to their peers) based on the method used for attribution. These results imply that hospitals should consider attributing performance metrics based on ascribed ownership from billing data and not just from attending of record status.

As hospitalist programs and providers in general are increasingly being asked to develop dashboards to monitor individual and group performance, correctly attributing care to providers is likely to become increasingly important. Experts agree that principles of effective provider performance dashboards include ranking individual provider performance relative to peers, clearly displaying data in an easily accessible format, and ensuring that data can be credibly attributed to the individual provider.^3,4,6 However, there appears to be no gold standard method for attribution, especially in the inpatient setting. Our results imply that hospitals should consider attributing performance metrics based on ascribed ownership from billing data and not just from attending of record status.

Several limitations of our findings are important to consider. First, our program is a relatively small, academic group with handoffs that typically occur every 1 to 2 weeks and sometimes with additional handoffs on weekends. Different care patterns and settings might impact the utility of our attribution methodology relative to the standard methodology. Additionally, it is important to note that the relative merits of the different methodologies cannot be ascertained from our comparison. We can demonstrate discordance between the attribution methodologies, but we cannot say that 1 method is correct and the other is flawed. Although we believe that our day-weighted approach feels fairer to providers based on group input and feedback, we did not conduct a formal survey to examine providers’ preferences for the standard versus day-weighted approaches. The appropriateness of a particular attribution method needs to be assessed locally and may vary based on the clinical setting. For instance, on a service in which patients are admitted for procedures, it may make more sense to attribute the outcome of the case to the proceduralist even if that provider did not bill for the patient’s care on a daily basis. Finally, the computational requirements of our methodology are not trivial and require linking billing data with administrative patient-level data, which may be challenging to operationalize in some institutions.

These limitations aside, we believe that our attribution methodology has face validity. For example, a provider might be justifiably frustrated if, using the standard methodology, he or she is charged with the LOS of a patient who had been hospitalized for months, particularly if that patient is discharged shortly after the provider assumes care. Our method addresses this type of misattribution. Particularly when individual provider compensation is based on performance on metrics (as is the case at our institution), optimizing provider attribution to particular patients may be important, and face validity may be required for group buy-in.

In summary, we have demonstrated that it is possible to use billing data to assign ownership of patients to multiple providers over 1 hospital stay. This could be applied to other hospitalist programs as well as other healthcare settings in which multiple providers care for patients during 1 healthcare encounter (eg, ICUs).

Disclosure

The authors declare they have no relevant conflicts of interest.

Hospitalists’ performance is routinely evaluated by third-party payers, employers, and patients. As hospitalist programs mature, there is a need to develop processes to identify, internally measure, and report on individual and group performance. We know from Society of Hospital Medicine (SHM) data that a significant amount of hospitalists’ total compensation is at least partially based on performance. Often this is based at least in part on quality data. In 2006, SHM issued a white paper detailing the key elements of a successful performance monitoring and reporting process.^1,2 Recommendations included the identification of meaningful operational and clinical performance metrics, and the ability to monitor and report both group and individual metrics was highlighted as an essential component. There is evidence that comparison of individual provider performance with that of their peers is a necessary element of successful provider dashboards.³ Additionally, regular feedback and a clear, visual presentation of the data are important components of successful provider feedback dashboards.^3-6

Much of the literature regarding provider feedback dashboards has been based in the outpatient setting. The majority of these dashboards focus on the management of chronic illnesses (eg, diabetes and hypertension), rates of preventative care services (eg, colonoscopy or mammogram), or avoidance of unnecessary care (eg, antibiotics for sinusitis).^4,5 Unlike in the outpatient setting, in which 1 provider often provides a majority of the care for a given episode of care, hospitalized patients are often cared for by multiple providers, challenging the appropriate attribution of patient-level metrics to specific providers. Under the standard approach, an entire hospitalization is attributed to 1 physician, generally the attending of record for the hospitalization, which may be the admitting provider or the discharging provider, depending on the approach used by the hospital. However, assigning responsibility for an entire hospitalization to a provider who may have only seen the patient for a small percentage of a hospitalization may jeopardize the validity of metrics. As provider metrics are increasingly being used for compensation, it is important to ensure that the method for attribution correctly identifies the providers caring for patients. To our knowledge there is no gold standard approach for attributing metrics to providers when patients are cared for by multiple providers, and the standard attending of record–based approach may lack face validity in many cases.

We aimed to develop and operationalize a system to more fairly attribute patient-level data to individual providers across a single hospitalization even when multiple providers cared for the patient. We then compared our methodology to the standard approach, in which the attending of record receives full attribution for each metric, to determine the difference on a provider level between the 2 models.

Clinical Setting

The Johns Hopkins Hospital is a 1145-bed, tertiary-care hospital. Over the years of this project, the Johns Hopkins Hospitalist Program was an approximately 20-physician group providing care in a variety of settings, including a dedicated hospitalist floor, where this metrics program was initiated. Hospitalists in this setting work Monday through Friday, with 1 hospitalist and a moonlighter covering on the weekends. Admissions are performed by an admitter, and overnight care is provided by a nocturnist. Initially 17 beds, this unit expanded to 24 beds in June 2012. For the purposes of this article, we included all general medicine patients admitted to this floor between July 1, 2010, and June 30, 2014, who were cared for by hospitalists. During this period, all patients were inpatients; no patients were admitted under observation status. All of these patients were cared for by hospitalists without housestaff or advanced practitioners. Since 2014, the metrics program has been expanded to other hospitalist-run services in the hospital, but for simplicity, we have not presented these more recent data.

Individual Provider Metrics

Metrics were chosen to reflect institutional quality and efficiency priorities. Our choice of metrics was restricted to those that (1) plausibly reflect provider performance, at least in part, and (2) could be accessed in electronic form (without any manual chart review). Whenever possible, we chose metrics with objective data. Additionally, because funding for this effort was provided by the hospital, we sought to ensure that enough of the metrics were related to cost to justify ongoing hospital support of the project. SAS 9.2 (SAS Institute Inc, Cary, NC) was used to calculate metric weights. Specific metrics included American College of Chest Physicians (ACCP)–compliant venous thromboembolism (VTE) prophylaxis,⁷ observed-to-expected length of stay (LOS) ratio, percentage of discharges per day, discharges before 3 pm, depth of coding, patient satisfaction, readmissions, communication with the primary care provider, and time to signature for discharge summaries (Table 1).

Appropriate prophylaxis for VTE was calculated by using an algorithm embedded within the computerized provider order entry system, which assessed the prescription of ACCP-compliant VTE prophylaxis within 24 hours following admission. This included a risk assessment, and credit was given for no prophylaxis and/or mechanical and/or pharmacologic prophylaxis per the ACCP guidelines.⁷

Observed-to-expected LOS was defined by using the University HealthSystem Consortium (UHC; now Vizient Inc) expected LOS for the given calendar year. This approach incorporates patient diagnoses, demographics, and other administrative variables to define an expected LOS for each patient.

The percent of patients discharged per day was defined from billing data as the percentage of a provider’s evaluation and management charges that were the final charge of a patient’s stay (regardless of whether a discharge day service was coded).

Discharge prior to 3 pm was defined from administrative data as the time a patient was discharged from the electronic medical system.

Depth of coding was defined as the number of coded diagnoses submitted to the Maryland Health Services Cost Review Commission for determining payment and was viewed as an indicator of the thoroughness of provider documentation.

Patient satisfaction was defined at the patient level (for those patients who turned in patient satisfaction surveys) as the pooled value of the 5 provider questions on the hospital’s patient satisfaction survey administered by Press Ganey: “time the physician spent with you,” “did the physician show concern for your questions/worries,” “did the physician keep you informed,” “friendliness/courtesy of the physician,” and “skill of the physician.”⁸

Readmission rates were defined as same-hospital readmissions divided by the total number of patients discharged by a given provider, with exclusions based on the Centers for Medicare and Medicaid Services hospital-wide, all-cause readmission measure.¹ The expected same-hospital readmission rate was defined for each patient as the observed readmission rate in the entire UHC (Vizient) data set for all patients with the same All Patient Refined Diagnosis Related Group and severity of illness, as we have described previously.⁹

Communication with the primary care provider was the only self-reported metric used. It was based on a mandatory prompt on the discharge worksheet in the electronic medical record (EMR). Successful communication with the outpatient provider was defined as verbal or electronic communication by the hospitalist with the outpatient provider. Partial (50%) credit was given for providers who attempted but were unsuccessful in communicating with the outpatient provider, for patients for whom the provider had access to the Johns Hopkins EMR system, and for planned admissions without new or important information to convey. No credit was given for providers who indicated that communication was not indicated, who indicated that a patient and/or family would update the provider, or who indicated that the discharge summary would be sufficient.⁹ Because the discharge worksheet could be initiated at any time during the hospitalization, providers could document communication with the outpatient provider at any point during hospitalization.

Discharge summary turnaround was defined as the average number of days elapsed between the day of discharge and the signing of the discharge summary in the EMR.

Assigning Ownership of Patients to Individual Providers

Using billing data, we assigned ownership of patient care based on the type, timing, and number of charges that occurred during each hospitalization (Figure 1). Eligible charges included all history and physical (codes 99221, 99222, and 99223), subsequent care (codes 99231, 99232, and 99233), and discharge charges (codes 99238 and 99239).

By using a unique identifier assigned for each hospitalization, professional fees submitted by providers were used to identify which provider saw the patient on the admission day, discharge day, as well as subsequent care days. Providers’ productivity, bonus supplements, and policy compliance were determined by using billing data, which encouraged the prompt submittal of charges.

The provider who billed the admission history and physical (codes 99221, 99222, and 99223) within 1 calendar date of the patient’s initial admission was defined as the admitting provider. Patients transferred to the hospitalist service from other services were not assigned an admitting hospitalist. The sole metric assigned to the admitting hospitalist was ACCP-compliant VTE prophylaxis.

The provider who billed the final subsequent care or discharge code (codes 99231, 99232, 99233, 99238, and 99239) within 1 calendar date of discharge was defined as the discharging provider. For hospitalizations characterized by a single provider charge (eg, for patients admitted and discharged on the same day), the provider billing this charge was assigned as both the admitting and discharging physician. Patients upgraded to the intensive care unit (ICU) were not counted as a discharge unless the patient was downgraded and discharged from the hospitalist service. The discharging provider was assigned responsibility for the time of discharge, the percent of patients discharged per day, the discharge summary turnaround time, and hospital readmissions.

Metrics that were assigned to multiple providers for a single hospitalization were termed “provider day–weighted” metrics. The formula for calculating the weight for each provider day–weighted metric was as follows: weight for provider A = [number of daily charges billed by provider A] divided by [LOS +1]. The initial hospital day was counted as day 0. LOS plus 1 was used to recognize that a typical hospitalization will have a charge on the day of admission (day 0) and a charge on the day of discharge such that an LOS of 2 days (eg, a patient admitted on Monday and discharged on Wednesday) will have 3 daily charges. Provider day–weighted metrics included patient satisfaction, communication with the outpatient provider, depth of coding, and observed-to-expected LOS.

Our billing software prevented providers from the same group from billing multiple daily charges, thus ensuring that there were no duplicated charges submitted for a given day.

Presenting Results

Providers were only shown data from the day-weighted approach. For ease of visual interpretation, scores for each metric were scaled ordinally from 1 (worst performance) to 9 (best performance; Table 1). Data were displayed in a dashboard format on a password-protected website for each provider to view his or her own data relative to that of the hospitalist peer group. The dashboard was implemented in this format on July 1, 2011. Data were updated quarterly (Figure 2).

Results were displayed in a polyhedral or spider-web graph (Figure 2). Provider and group metrics were scaled according to predefined benchmarks established for each metric and standardized to a scale ranging from 1 to 9. The scale for each metric was set based on examining historical data and group median performance on the metrics to ensure that there was a range of performance (ie, to avoid having most hospitalists scoring a 1 or 9). Scaling thresholds were periodically adjusted as appropriate to maintain good visual discrimination. Higher scores (creating a larger-volume polygon) are desirable even for metrics such as LOS, for which a low value is desirable. Both a spider-web graph and trends over time were available to the provider (Figure 2). These graphs display a comparison of the individual provider scores for each metric to the hospitalist group average for that metric.

Comparison with the Standard (Attending of Record) Method of Attribution

For the purposes of this report, we sought to determine whether there were meaningful differences between our day-weighted approach versus the standard method of attribution, in which the attending of record is assigned responsibility for each metric that would not have been attributed to the discharging attending under both methods. Our goal was to determine where and whether there was a meaningful difference between the 2 methodologies, recognizing that the degree of difference between these 2 methodologies might vary in other institutions and settings. In our hospital, the attending of record is generally the discharging attending. In order to compare the 2 methodologies, we arbitrarily picked 2015 to retrospectively evaluate the differences between these 2 methods of attribution. We did not display or provide data using the standard methodology to providers at any point; this approach was used only for the purposes of this report. Because these metrics are intended to evaluate relative provider performance, we assigned a percentile to each provider for his or her performance on the given metric using our attribution methodology and then, similarly, assigned a percentile to each provider using the standard methodology. This yielded 2 percentile scores for each provider and each metric. We then compared these percentile ranks for providers in 2 ways: (1) we determined how often providers who scored in the top half of the group for a given metric (above the 50th percentile) also scored in the top half of the group for that metric by using the other calculation method, and (2) we calculated the absolute value of the difference in percentiles between the 2 methods to characterize the impact on a provider’s ranking for that metric that might result from switching to the other method. For instance, if a provider scored at the 20th percentile for the group in patient satisfaction with 1 attribution method and scored at the 40th percentile for the group in patient satisfaction using the other method, the absolute change in percentile would be 20 percentile points. But, this provider would still be below the 50th percentile by both methods (concordant bottom half performance). We did not perform this comparison for metrics assigned to the discharging provider (such as discharge summary turnaround time or readmissions) because the attending of record designation is assigned to the discharging provider at our hospital.

The dashboard was successfully operationalized on July 1, 2011, with displays visible to providers as shown in Figure 2. Consistent with the principles of providing effective performance feedback to providers, the display simultaneously showed providers their individual performance as well as the performance of their peers. Providers were able to view their spider-web plot for prior quarters. Not shown are additional views that allowed providers to see quarterly trends in their data versus their peers across several fiscal years. Also available to providers was their ranking relative to their peers for each metric; specific peers were deidentified in the display.

There was notable discordance between provider rankings between the 2 methodologies, as shown in Table 2. Provider performance above or below the median was concordant 56% to 75% of the time (depending on the particular metric), indicating substantial discordance because top-half or bottom-half concordance would be expected to occur by chance 50% of the time. Although the provider percentile differences between the 2 methods tended to be modest for most providers (the median difference between the methods was 13 to 22 percentile points for the various metrics), there were some providers for whom the method of calculation dramatically impacted their rankings. For 5 of the 6 metrics we examined, at least 1 provider had a 50-percentile or greater change in his or her ranking based on the method used. This indicates that at least some providers would have had markedly different scores relative to their peers had we used the alternative methodology (Table 2). In VTE prophylaxis, for example, at least 1 provider had a 94-percentile change in his or her ranking; similarly, a provider had an 88-perentile change in his or her LOS ranking between the 2 methodologies.

We found that it is possible to assign metrics across 1 hospital stay to multiple providers by using billing data. We also found a meaningful discrepancy in how well providers scored (relative to their peers) based on the method used for attribution. These results imply that hospitals should consider attributing performance metrics based on ascribed ownership from billing data and not just from attending of record status.

As hospitalist programs and providers in general are increasingly being asked to develop dashboards to monitor individual and group performance, correctly attributing care to providers is likely to become increasingly important. Experts agree that principles of effective provider performance dashboards include ranking individual provider performance relative to peers, clearly displaying data in an easily accessible format, and ensuring that data can be credibly attributed to the individual provider.^3,4,6 However, there appears to be no gold standard method for attribution, especially in the inpatient setting. Our results imply that hospitals should consider attributing performance metrics based on ascribed ownership from billing data and not just from attending of record status.

Several limitations of our findings are important to consider. First, our program is a relatively small, academic group with handoffs that typically occur every 1 to 2 weeks and sometimes with additional handoffs on weekends. Different care patterns and settings might impact the utility of our attribution methodology relative to the standard methodology. Additionally, it is important to note that the relative merits of the different methodologies cannot be ascertained from our comparison. We can demonstrate discordance between the attribution methodologies, but we cannot say that 1 method is correct and the other is flawed. Although we believe that our day-weighted approach feels fairer to providers based on group input and feedback, we did not conduct a formal survey to examine providers’ preferences for the standard versus day-weighted approaches. The appropriateness of a particular attribution method needs to be assessed locally and may vary based on the clinical setting. For instance, on a service in which patients are admitted for procedures, it may make more sense to attribute the outcome of the case to the proceduralist even if that provider did not bill for the patient’s care on a daily basis. Finally, the computational requirements of our methodology are not trivial and require linking billing data with administrative patient-level data, which may be challenging to operationalize in some institutions.

These limitations aside, we believe that our attribution methodology has face validity. For example, a provider might be justifiably frustrated if, using the standard methodology, he or she is charged with the LOS of a patient who had been hospitalized for months, particularly if that patient is discharged shortly after the provider assumes care. Our method addresses this type of misattribution. Particularly when individual provider compensation is based on performance on metrics (as is the case at our institution), optimizing provider attribution to particular patients may be important, and face validity may be required for group buy-in.

In summary, we have demonstrated that it is possible to use billing data to assign ownership of patients to multiple providers over 1 hospital stay. This could be applied to other hospitalist programs as well as other healthcare settings in which multiple providers care for patients during 1 healthcare encounter (eg, ICUs).

Disclosure

The authors declare they have no relevant conflicts of interest.

From Physicians Inpatient Care Specialists (MDICS), Hanover, MD (Dr. Capstack, Ms. Vollono), Versant Statistical Solutions, Raleigh, NC (Ms. Segujja), Anne Arundel Medical Center, Annapolis, MD (Dr. Moser [at the time of the study], Dr. Meisenberg), and Johns Hopkins Hospital, Baltimore, MD (Dr. Michtalik).

Abstract

• Objective: To determine whether a higher than conventional physician assistant (PA)–to-physician hospitalist staffing ratio can achieve similar clinical outcomes for inpatients at a community hospital.
• Methods: Retrospective cohort study comparing 2 hospitalist groups at a 384-bed community hospital, one with a high PA-to-physician ratio model (“expanded PA”), with 3 physicians/3 PAs and the PAs rounding on 14 patients a day (35.73% of all visits), and the other with a low PA-to-physician ratio model (“conventional”), with 9 physicians/2 PAs and the PAs rounding on 9 patients a day (5.89% of all visits). For 16,964 adult patients discharged by the hospitalist groups with a medical principal APR-DRG code between January 2012 and June 2013, in-hospital mortality, cost of care, readmissions, length of stay (LOS) and consultant use were analyzed using logistic regression and adjusted for age, insurance status, severity of illness, and risk of mortality.
• Results: No statistically significant differences were found between the 2 groups for in-hospital mortality (odds ratio [OR], 0.89 [95% confidence interval {CI}, 0.66–1.19]; P = 0.42), readmissions (OR, 0.95 [95% CI, 0.87–1.04]; P = 0.27), length of stay (effect size 0.99 days shorter LOS in expanded PA group, 95% CI, 0.97 to 1.01 days; P = 0.34) or consultant use (OR 1.00, 95% CI 0.94–1.07, P = 0.90). Cost of care was less in the expanded PA group (effect size 3.52% less; estimated cost $2644 vs $2724; 95% CI 2.66%–4.39%, P < 0.001).
• Conclusion: An expanded PA hospitalist staffing model at a community hospital provided similar outcomes at a lower cost of care.

Hospitalist program staffing models must optimize efficiency while maintaining clinical outcomes in order to increase value and decrease costs [1]. The cost of hospitalist programs is burdensome, with nearly 94% of groups nationally requiring financial support beyond professional fees [2]. Nationally, for hospitalist groups serving adults, average institutional support is over $156,000 per physician full time equivalent (FTE) (182 twelve-hour clinical shifts per calendar year) [2]. Significant savings could be achieved if less costly physician assistants could be incorporated into clinical teams to provide similar care without sacrificing quality.

Nurse practitioners (NPs) and physician assistants (PAs) have been successfully employed on academic hospitalist services to complement physician staffing [3–10]. They perform admissions, consults, rounding visits and discharges with physician collaboration as permitted by each group’s policies and in accordance with hospital by-laws and state regulations. A median of 0.25 NP and 0.28 PA FTEs per physician FTE are employed by hospitalist groups that incorporate them, though staffing ratios vary widely [2].

Physicians Inpatient Care Specialists (MDICS) devel-oped a staffing model that deploys PAs to see a large proportion of its patients collaboratively with physicians, and with a higher patient census per PA than has been previously reported [2–5]. The group leaders believed that this would yield similar outcomes for patients at a lower cost to the supporting institution than a conventional staffing model which used fewer PAs to render patient care. Prior inpatient studies have demonstrated comparable clinical outcomes when comparing hospitalist PAs and NPs to residents and fellows [4–10], but to our knowledge no data exist directly comparing hospitalist PAs to hospitalist physicians. This study goes beyond prior work by examining the community, non-teaching setting, and directly comparing outcomes from the expanded use of PAs to those of a hospitalist group staffed with a greater proportion of attending physicians at the same hospital during the same time.

Methods

Setting

The study was performed at Anne Arundel Medical Center (AAMC), a 384-bed community hospital in Annapolis, Maryland, that serves a region of over 1 million people. Approximately 26,000 adult patients are discharged annually. During the study, more than 90% of internal medicine service inpatients were cared for by one of 2 hospitalist groups: a hospital-employed group (“conventional” group, Anne Arundel Medical Group) and a contracted hospitalist group (“expanded PA” group, Physicians Inpatient Care Specialists). The conventional group’s providers received a small incentive for Core Measures compliance for patients with stroke, myocardial infarction, congestive heart failure and pneumonia. The expanded PA group received a flat fee for providing hospitalist services and the group’s providers received a small incentive for productivity from their employer. The study was deemed exempt by the AAMC institutional review board.

Staffing Models, Patient Allocation, and Assignment

Admitted patients were designated to be admitted to one group or the other on the basis of standing arrangements with the patients’ primary care providers. Consultative referrals could also be made from subspecialists, who had discretion as to which group they wished to use.

Each morning, following sign-out report from the night team, each team of day providers determined which patients would be seen by which of their providers. Patients still on service from the previous day would be seen by the same provider again whenever possible in order to maintain continuity. Each individual provider had their own patients for the day who they rounded on independently and were responsible for. Physician involvement with patients seen primarily by PAs occurred as described below. Physicians in both groups were expected to take primary rounding responsibility for patients who were more acute or more complex based on morning sign-out report; there was no more formal mandate for patient allocation to particular provider type.

Physician-PA Collaboration

Patients

Patients discharged between 1 January 2012 and 30 June 2013 by the hospitalist groups were identified by searching AAMC’s Crimson Continuuum of Care (The Advisory Board, Washington, DC), a software analytic tool that is integrated with coded clinical data. Adult patient hospitalizations determined by Crimson to have a medical (non-surgical, non-obstetrical) APR-DRG code as the final principal diagnosis were included. Critically ill patients or those appropriate for “step-down unit” care were cared for by the in-house critical care staff; upon transfer out of critical or step-down care, patients were referred back to the admitting hospitalist team. A diagnosis (and its associated hospitalizations) was excluded for referral bias if the diagnosis was the  principal diagnosis for at least 1% of a group’s discharges and the percentage of patients with that diagnosis was at least two times greater in one group than the other. Hospitalizations with a diagnosis of “ungroupable” (APR-DRG 956) were also excluded.

Measurements

Demographic, insurance status, cost of care, length of stay (LOS), APR-DRG (All Patient Refined Diagnosis-Related Group) severity of illness (SOI) and risk of mortality (ROM), consultant utilization, 30-day all-cause readmission (“readmission rate”), and mortality information was obtained from administrative data and exported into a single database for statistical analysis. Readmissions, inpatient mortality, and cost of care were the primary outcomes; consultant use and length of stay were secondary outcomes. A hospitalization was considered a readmission if the patient returned to inpatient status at AAMC for any reason within 30 days of a previous inpatient discharge. Inpatient mortality was defined as patient death during hospitalization. The cost of care was measured using the case charges associated with each encounter. Charge capture data from both groups was analyzed to classify visits as “physician-only,” “physician co-visit,” and “PA-only” visits. A co-visit consists of the physician visiting the patient after the PA has already done so on the same day, taking their own history and performing their own physical exam, and writing a brief progress note. These data were compared against the exported administrative data to find matching encounters and associated visits, with only matching visits included in the analysis. If a duplicate charge was entered on the same day for a patient, any conflict was resolved in favor of the physician visit. A total of 49,883 and 28,663 matching charges were identified for the conventional and expanded PA groups.

Statistical Methods

Odds of inpatient mortality were calculated using logistic regression and adjusted for age, insurance status, APR-DRG ROM, and LOS. Odds of readmission were calculated using logistic regression and adjusted for age, LOS, insurance and APR-DRG SOI. Cost of care (effect size) was examined using multiple linear regression and adjusted for age, APR-DRG SOI, insurance status and LOS. This model was fit using the logarithmic transformations of cost of care and LOS to correct deviation from normality. Robust regression using MM estimation was used to estimate group effects due to the existence of outliers and high leverage points. Length of stay (effect size) was assessed using the log-transformed variable and adjusted for APR-DRG SOI, age, insurance status and consultant use. Finally, category logistic regression models were fit to estimate the odds of consultant use in the study groups and adjusted for age, LOS, insurance status and APR-DRG SOI.

Results

Records review identified 17,294 adult patient hospitalizations determined by Crimson to have a medical (non-surgical, non-obstetrical) APR-DRG code as the final principal diagnosis.  We excluded 15 expanded PA and 11 conventional hospitalizations that fell under APR-DRG code 956 “ungroupable.” Exclusion for referral bias resulted in the removal of 304 hospitalizations, 207 (3.03%) from the expanded PA group and 97 (0.92%) from the conventional group. These excluded hospitalizations came from 2 APR-DRG codes, urinary stones (code 465) and “other kidney and urinary tract diagnoses” (code 468). This left 6612 hospitalizations in the expanded PA group and 10,352 in the conventional group.

Charge capture data for both groups was used to determine the proportion of encounters rendered by each provider type or combination. In the expanded PA group, 35.73% of visits (10,241 of 28,663) were conducted by a PA, and 64.27% were conducted by a physician or by a PA with a billable physician “co-visit.” In the conventional group, 5.89% of visits (2938 of 49,883) were conducted by a PA, and 94.11% were conducted by a physician only or by a PA with a billable physician “co-visit”.

Readmissions

Overall, 929 of 6612 (14.05%) and 1417 of 10,352 (13.69%) patients were readmitted after being discharged by the expanded PA and conventional groups, respectively. After multivariate analysis, there was no statistically significant difference in odds of readmission between the groups (OR for conventional group, 0.95 [95% CI, 0.87–1.04]; P = 0.27). 

Inpatient Mortality

Unadjusted inpatient mortality for the expanded PA group was 1.30% and 0.99% for the conventional group.  After multivariate analysis, there was no statistically significant difference in odds of in-hospital mortality between the groups (OR for conventional group, 0.89 [95% CI, 0.66–1.19]; P = 0.42).

Patient Charges

The unadjusted mean patient charge in the expanded PA group was $7822 ± $7755 and in the conventional group mean patient charge was $8307 ± 10,034. Multivariate analysis found significantly lower adjusted patient charges in the expanded PA group relative to the conventional group (3.52% lower in the expanded PA group [95% CI, 2.66%–4.39%, P < 0.001). When comparing a “standard” patient who was between 80–89 and had Medicare insurance and an SOI of “major,” the cost of care was $2644 in the expanded PA group vs $2724 in the conventional group.

Length of Stay

Unadjusted mean length of stay was 4.1 ± 3.9 days and 4.3 ± 5.6 days for the expanded PA and conventional groups, respectively. After multivariate analysis, when comparing the statistical model “standard” patient, there was no significant difference in the length of stay between the 2 groups (effect size, 0.99 days shorter LOS in the expanded PA group [95% CI, 0.97–1.01 days]; P = 0.34)

Consultant Use

Utilization of consultants was also assessed. The expanded PA group used a mean of 0.55 consultants per case, and the conventional group used 0.56. After multivariate adjustment, there was no significant difference in consulting service use between groups (OR 1.00 [95% CI, 0.94–1.07]; P = 0.90).

Discussion

Maximizing value and minimizing health care costs is a national priority. To our knowledge, this is the first study to compare hospitalist PAs in a community, non-teaching practice directly and contemporaneously to peer PAs and attending physicians and examine the impact on outcomes. In our study, a much larger proportion of patient visits were conducted primarily by PAs without a same-day physician visit in the expanded PA group (35.73%, vs 5.89% in the conventional group). There was no statistically significant difference in inpatient mortality, length of stay or readmissions. In addition, costs of care measured as hospital charges to patients were lower in the expanded PA group. Consultants were not used disproportionately by the expanded PA group in order to achieve these results. Our results are consistent with studies that have compared PAs and NPs at academic centers to traditional housestaff teams and which show that services staffed with PAs or NPs that provide direct care to medical inpatients are non-inferior [4–10].

This study’s expanded PA group’s PAs rounded on 14 patients per day, close to the “magic 15” that is considered by many a good compromise for hospitalist physicians between productivity and quality [11,12].  This is substantially more than the 6 to 10 patients PAs have been responsible for in previously reported studies [3,4,6]. As the median salary for a PA hospitalist is $102,960 compared with the median internal medicine physician hospitalist salary of $253,977 [2], using hospitalist PAs in a collaboration model as described herein could result in significant savings for supporting institutions without sacrificing quality.

We recognize several limitations to this study. First, the data were obtained retrospectively from a single center and patient assignment between groups was nonrandomized. The significant differences in the baseline characteristics of patients between the study groups, however, were adjusted for in multivariate analysis, and potential referral bias was addressed through our  exclusion criteria. Second, our comparison relied on coding rather than clinical data for diagnosis grouping. However, administrative data is commonly used to determine the primary diagnosis for study patients and the standard for reimbursement. Third, we recognize that there may have been unmeasured confounders that may have affected the outcomes. However, the same resources, including consultants and procedure services, were readily available to both groups and there was no significant difference in consultation rates. Fourth, “cost of care” was measured as overall charges to patients, not cost to the hospital. However, given that all the encounters occurred at the same hospital in the same time frame, the difference should be proportional and equal between groups. Finally, our readmission rates did not account for patients readmitted to other institutions. However, there should not have been a differential effect between the 2 study groups, given the shared patient catchment area and our exclusion for referral bias.

It should also be noted that the expanded PA group used a structured collaboration framework and incorporated a structured education program for its PAs. These components are integral to the expanded PA model, and our results may not be generalizable outside of a similar framework. The expanded PA group’s PAs were carefully selected at the time of hire, specifically educated, and supported through ongoing collaboration to provide efficient and appropriate care at the “top of their licenses”. Not all medical groups will be able to provide this level of support and education, and not all hospitalist PAs will want to and/or be able to reach this level of proficiency. However, successful implementation is entirely achievable for groups that invest the effort. The MDICS education process included 80 hours of didactic sessions spread over several months and is based on the Society of Hospital Medicine Core Competencies [13] as well as 6 months of supervised bedside education with escalating clinical responsibilities under the tutelage of an experienced physician or PA. Year-long academic PA fellowships have also been developed for purposes of similar training at several institutions [14].

Conclusion

Our results show that expanded use of well-educated PAs functioning within a formal collaboration arrangement with physicians provides similar clinical quality to a conventional PA staffing model with no excess patient care costs. The model also allows substantial salary savings to supporting institutions, which is important to hospital and policy stakeholders given the implications for hospitalist group staffing, increasing value, and allocation of precious time and financial resources.

Acknowledgements: The authors wish to thank Kevin Funk, MBA, of MDICS, Clarence Richardson, MBA, of GeBBs Software International, and Heather Channing, Kayla King, and Laura Knox of Anne Arundel Healthcare Enterprise, who provided invaluable help with the data aggregation used for this study.

Corresponding author: Timothy M. Capstack, MD, 7250 Parkway Dr, Suite 500, Hanover, MD 21076, [email protected].

Financial disclosures: Dr. Capstack has ownership interest in Physicians Inpatient Care Specialists (MDICS). Ms. Segujja received compensation from MDICS for statistical analysis.

From Physicians Inpatient Care Specialists (MDICS), Hanover, MD (Dr. Capstack, Ms. Vollono), Versant Statistical Solutions, Raleigh, NC (Ms. Segujja), Anne Arundel Medical Center, Annapolis, MD (Dr. Moser [at the time of the study], Dr. Meisenberg), and Johns Hopkins Hospital, Baltimore, MD (Dr. Michtalik).

Abstract

• Objective: To determine whether a higher than conventional physician assistant (PA)–to-physician hospitalist staffing ratio can achieve similar clinical outcomes for inpatients at a community hospital.
• Methods: Retrospective cohort study comparing 2 hospitalist groups at a 384-bed community hospital, one with a high PA-to-physician ratio model (“expanded PA”), with 3 physicians/3 PAs and the PAs rounding on 14 patients a day (35.73% of all visits), and the other with a low PA-to-physician ratio model (“conventional”), with 9 physicians/2 PAs and the PAs rounding on 9 patients a day (5.89% of all visits). For 16,964 adult patients discharged by the hospitalist groups with a medical principal APR-DRG code between January 2012 and June 2013, in-hospital mortality, cost of care, readmissions, length of stay (LOS) and consultant use were analyzed using logistic regression and adjusted for age, insurance status, severity of illness, and risk of mortality.
• Results: No statistically significant differences were found between the 2 groups for in-hospital mortality (odds ratio [OR], 0.89 [95% confidence interval {CI}, 0.66–1.19]; P = 0.42), readmissions (OR, 0.95 [95% CI, 0.87–1.04]; P = 0.27), length of stay (effect size 0.99 days shorter LOS in expanded PA group, 95% CI, 0.97 to 1.01 days; P = 0.34) or consultant use (OR 1.00, 95% CI 0.94–1.07, P = 0.90). Cost of care was less in the expanded PA group (effect size 3.52% less; estimated cost $2644 vs $2724; 95% CI 2.66%–4.39%, P < 0.001).
• Conclusion: An expanded PA hospitalist staffing model at a community hospital provided similar outcomes at a lower cost of care.

Hospitalist program staffing models must optimize efficiency while maintaining clinical outcomes in order to increase value and decrease costs [1]. The cost of hospitalist programs is burdensome, with nearly 94% of groups nationally requiring financial support beyond professional fees [2]. Nationally, for hospitalist groups serving adults, average institutional support is over $156,000 per physician full time equivalent (FTE) (182 twelve-hour clinical shifts per calendar year) [2]. Significant savings could be achieved if less costly physician assistants could be incorporated into clinical teams to provide similar care without sacrificing quality.

Nurse practitioners (NPs) and physician assistants (PAs) have been successfully employed on academic hospitalist services to complement physician staffing [3–10]. They perform admissions, consults, rounding visits and discharges with physician collaboration as permitted by each group’s policies and in accordance with hospital by-laws and state regulations. A median of 0.25 NP and 0.28 PA FTEs per physician FTE are employed by hospitalist groups that incorporate them, though staffing ratios vary widely [2].

Physicians Inpatient Care Specialists (MDICS) devel-oped a staffing model that deploys PAs to see a large proportion of its patients collaboratively with physicians, and with a higher patient census per PA than has been previously reported [2–5]. The group leaders believed that this would yield similar outcomes for patients at a lower cost to the supporting institution than a conventional staffing model which used fewer PAs to render patient care. Prior inpatient studies have demonstrated comparable clinical outcomes when comparing hospitalist PAs and NPs to residents and fellows [4–10], but to our knowledge no data exist directly comparing hospitalist PAs to hospitalist physicians. This study goes beyond prior work by examining the community, non-teaching setting, and directly comparing outcomes from the expanded use of PAs to those of a hospitalist group staffed with a greater proportion of attending physicians at the same hospital during the same time.

Methods

Setting

The study was performed at Anne Arundel Medical Center (AAMC), a 384-bed community hospital in Annapolis, Maryland, that serves a region of over 1 million people. Approximately 26,000 adult patients are discharged annually. During the study, more than 90% of internal medicine service inpatients were cared for by one of 2 hospitalist groups: a hospital-employed group (“conventional” group, Anne Arundel Medical Group) and a contracted hospitalist group (“expanded PA” group, Physicians Inpatient Care Specialists). The conventional group’s providers received a small incentive for Core Measures compliance for patients with stroke, myocardial infarction, congestive heart failure and pneumonia. The expanded PA group received a flat fee for providing hospitalist services and the group’s providers received a small incentive for productivity from their employer. The study was deemed exempt by the AAMC institutional review board.

Staffing Models, Patient Allocation, and Assignment

Admitted patients were designated to be admitted to one group or the other on the basis of standing arrangements with the patients’ primary care providers. Consultative referrals could also be made from subspecialists, who had discretion as to which group they wished to use.

Each morning, following sign-out report from the night team, each team of day providers determined which patients would be seen by which of their providers. Patients still on service from the previous day would be seen by the same provider again whenever possible in order to maintain continuity. Each individual provider had their own patients for the day who they rounded on independently and were responsible for. Physician involvement with patients seen primarily by PAs occurred as described below. Physicians in both groups were expected to take primary rounding responsibility for patients who were more acute or more complex based on morning sign-out report; there was no more formal mandate for patient allocation to particular provider type.

Physician-PA Collaboration

Patients

Patients discharged between 1 January 2012 and 30 June 2013 by the hospitalist groups were identified by searching AAMC’s Crimson Continuuum of Care (The Advisory Board, Washington, DC), a software analytic tool that is integrated with coded clinical data. Adult patient hospitalizations determined by Crimson to have a medical (non-surgical, non-obstetrical) APR-DRG code as the final principal diagnosis were included. Critically ill patients or those appropriate for “step-down unit” care were cared for by the in-house critical care staff; upon transfer out of critical or step-down care, patients were referred back to the admitting hospitalist team. A diagnosis (and its associated hospitalizations) was excluded for referral bias if the diagnosis was the  principal diagnosis for at least 1% of a group’s discharges and the percentage of patients with that diagnosis was at least two times greater in one group than the other. Hospitalizations with a diagnosis of “ungroupable” (APR-DRG 956) were also excluded.

Measurements

Demographic, insurance status, cost of care, length of stay (LOS), APR-DRG (All Patient Refined Diagnosis-Related Group) severity of illness (SOI) and risk of mortality (ROM), consultant utilization, 30-day all-cause readmission (“readmission rate”), and mortality information was obtained from administrative data and exported into a single database for statistical analysis. Readmissions, inpatient mortality, and cost of care were the primary outcomes; consultant use and length of stay were secondary outcomes. A hospitalization was considered a readmission if the patient returned to inpatient status at AAMC for any reason within 30 days of a previous inpatient discharge. Inpatient mortality was defined as patient death during hospitalization. The cost of care was measured using the case charges associated with each encounter. Charge capture data from both groups was analyzed to classify visits as “physician-only,” “physician co-visit,” and “PA-only” visits. A co-visit consists of the physician visiting the patient after the PA has already done so on the same day, taking their own history and performing their own physical exam, and writing a brief progress note. These data were compared against the exported administrative data to find matching encounters and associated visits, with only matching visits included in the analysis. If a duplicate charge was entered on the same day for a patient, any conflict was resolved in favor of the physician visit. A total of 49,883 and 28,663 matching charges were identified for the conventional and expanded PA groups.

Statistical Methods

Odds of inpatient mortality were calculated using logistic regression and adjusted for age, insurance status, APR-DRG ROM, and LOS. Odds of readmission were calculated using logistic regression and adjusted for age, LOS, insurance and APR-DRG SOI. Cost of care (effect size) was examined using multiple linear regression and adjusted for age, APR-DRG SOI, insurance status and LOS. This model was fit using the logarithmic transformations of cost of care and LOS to correct deviation from normality. Robust regression using MM estimation was used to estimate group effects due to the existence of outliers and high leverage points. Length of stay (effect size) was assessed using the log-transformed variable and adjusted for APR-DRG SOI, age, insurance status and consultant use. Finally, category logistic regression models were fit to estimate the odds of consultant use in the study groups and adjusted for age, LOS, insurance status and APR-DRG SOI.

Results

Records review identified 17,294 adult patient hospitalizations determined by Crimson to have a medical (non-surgical, non-obstetrical) APR-DRG code as the final principal diagnosis.  We excluded 15 expanded PA and 11 conventional hospitalizations that fell under APR-DRG code 956 “ungroupable.” Exclusion for referral bias resulted in the removal of 304 hospitalizations, 207 (3.03%) from the expanded PA group and 97 (0.92%) from the conventional group. These excluded hospitalizations came from 2 APR-DRG codes, urinary stones (code 465) and “other kidney and urinary tract diagnoses” (code 468). This left 6612 hospitalizations in the expanded PA group and 10,352 in the conventional group.

Charge capture data for both groups was used to determine the proportion of encounters rendered by each provider type or combination. In the expanded PA group, 35.73% of visits (10,241 of 28,663) were conducted by a PA, and 64.27% were conducted by a physician or by a PA with a billable physician “co-visit.” In the conventional group, 5.89% of visits (2938 of 49,883) were conducted by a PA, and 94.11% were conducted by a physician only or by a PA with a billable physician “co-visit”.

Readmissions

Overall, 929 of 6612 (14.05%) and 1417 of 10,352 (13.69%) patients were readmitted after being discharged by the expanded PA and conventional groups, respectively. After multivariate analysis, there was no statistically significant difference in odds of readmission between the groups (OR for conventional group, 0.95 [95% CI, 0.87–1.04]; P = 0.27). 

Inpatient Mortality

Unadjusted inpatient mortality for the expanded PA group was 1.30% and 0.99% for the conventional group.  After multivariate analysis, there was no statistically significant difference in odds of in-hospital mortality between the groups (OR for conventional group, 0.89 [95% CI, 0.66–1.19]; P = 0.42).

Patient Charges

The unadjusted mean patient charge in the expanded PA group was $7822 ± $7755 and in the conventional group mean patient charge was $8307 ± 10,034. Multivariate analysis found significantly lower adjusted patient charges in the expanded PA group relative to the conventional group (3.52% lower in the expanded PA group [95% CI, 2.66%–4.39%, P < 0.001). When comparing a “standard” patient who was between 80–89 and had Medicare insurance and an SOI of “major,” the cost of care was $2644 in the expanded PA group vs $2724 in the conventional group.

Length of Stay

Unadjusted mean length of stay was 4.1 ± 3.9 days and 4.3 ± 5.6 days for the expanded PA and conventional groups, respectively. After multivariate analysis, when comparing the statistical model “standard” patient, there was no significant difference in the length of stay between the 2 groups (effect size, 0.99 days shorter LOS in the expanded PA group [95% CI, 0.97–1.01 days]; P = 0.34)

Consultant Use

Utilization of consultants was also assessed. The expanded PA group used a mean of 0.55 consultants per case, and the conventional group used 0.56. After multivariate adjustment, there was no significant difference in consulting service use between groups (OR 1.00 [95% CI, 0.94–1.07]; P = 0.90).

Discussion

Maximizing value and minimizing health care costs is a national priority. To our knowledge, this is the first study to compare hospitalist PAs in a community, non-teaching practice directly and contemporaneously to peer PAs and attending physicians and examine the impact on outcomes. In our study, a much larger proportion of patient visits were conducted primarily by PAs without a same-day physician visit in the expanded PA group (35.73%, vs 5.89% in the conventional group). There was no statistically significant difference in inpatient mortality, length of stay or readmissions. In addition, costs of care measured as hospital charges to patients were lower in the expanded PA group. Consultants were not used disproportionately by the expanded PA group in order to achieve these results. Our results are consistent with studies that have compared PAs and NPs at academic centers to traditional housestaff teams and which show that services staffed with PAs or NPs that provide direct care to medical inpatients are non-inferior [4–10].

This study’s expanded PA group’s PAs rounded on 14 patients per day, close to the “magic 15” that is considered by many a good compromise for hospitalist physicians between productivity and quality [11,12].  This is substantially more than the 6 to 10 patients PAs have been responsible for in previously reported studies [3,4,6]. As the median salary for a PA hospitalist is $102,960 compared with the median internal medicine physician hospitalist salary of $253,977 [2], using hospitalist PAs in a collaboration model as described herein could result in significant savings for supporting institutions without sacrificing quality.

We recognize several limitations to this study. First, the data were obtained retrospectively from a single center and patient assignment between groups was nonrandomized. The significant differences in the baseline characteristics of patients between the study groups, however, were adjusted for in multivariate analysis, and potential referral bias was addressed through our  exclusion criteria. Second, our comparison relied on coding rather than clinical data for diagnosis grouping. However, administrative data is commonly used to determine the primary diagnosis for study patients and the standard for reimbursement. Third, we recognize that there may have been unmeasured confounders that may have affected the outcomes. However, the same resources, including consultants and procedure services, were readily available to both groups and there was no significant difference in consultation rates. Fourth, “cost of care” was measured as overall charges to patients, not cost to the hospital. However, given that all the encounters occurred at the same hospital in the same time frame, the difference should be proportional and equal between groups. Finally, our readmission rates did not account for patients readmitted to other institutions. However, there should not have been a differential effect between the 2 study groups, given the shared patient catchment area and our exclusion for referral bias.

It should also be noted that the expanded PA group used a structured collaboration framework and incorporated a structured education program for its PAs. These components are integral to the expanded PA model, and our results may not be generalizable outside of a similar framework. The expanded PA group’s PAs were carefully selected at the time of hire, specifically educated, and supported through ongoing collaboration to provide efficient and appropriate care at the “top of their licenses”. Not all medical groups will be able to provide this level of support and education, and not all hospitalist PAs will want to and/or be able to reach this level of proficiency. However, successful implementation is entirely achievable for groups that invest the effort. The MDICS education process included 80 hours of didactic sessions spread over several months and is based on the Society of Hospital Medicine Core Competencies [13] as well as 6 months of supervised bedside education with escalating clinical responsibilities under the tutelage of an experienced physician or PA. Year-long academic PA fellowships have also been developed for purposes of similar training at several institutions [14].

Conclusion

Our results show that expanded use of well-educated PAs functioning within a formal collaboration arrangement with physicians provides similar clinical quality to a conventional PA staffing model with no excess patient care costs. The model also allows substantial salary savings to supporting institutions, which is important to hospital and policy stakeholders given the implications for hospitalist group staffing, increasing value, and allocation of precious time and financial resources.

Acknowledgements: The authors wish to thank Kevin Funk, MBA, of MDICS, Clarence Richardson, MBA, of GeBBs Software International, and Heather Channing, Kayla King, and Laura Knox of Anne Arundel Healthcare Enterprise, who provided invaluable help with the data aggregation used for this study.

Corresponding author: Timothy M. Capstack, MD, 7250 Parkway Dr, Suite 500, Hanover, MD 21076, [email protected].

Financial disclosures: Dr. Capstack has ownership interest in Physicians Inpatient Care Specialists (MDICS). Ms. Segujja received compensation from MDICS for statistical analysis.

From Physicians Inpatient Care Specialists (MDICS), Hanover, MD (Dr. Capstack, Ms. Vollono), Versant Statistical Solutions, Raleigh, NC (Ms. Segujja), Anne Arundel Medical Center, Annapolis, MD (Dr. Moser [at the time of the study], Dr. Meisenberg), and Johns Hopkins Hospital, Baltimore, MD (Dr. Michtalik).

Abstract

• Objective: To determine whether a higher than conventional physician assistant (PA)–to-physician hospitalist staffing ratio can achieve similar clinical outcomes for inpatients at a community hospital.
• Methods: Retrospective cohort study comparing 2 hospitalist groups at a 384-bed community hospital, one with a high PA-to-physician ratio model (“expanded PA”), with 3 physicians/3 PAs and the PAs rounding on 14 patients a day (35.73% of all visits), and the other with a low PA-to-physician ratio model (“conventional”), with 9 physicians/2 PAs and the PAs rounding on 9 patients a day (5.89% of all visits). For 16,964 adult patients discharged by the hospitalist groups with a medical principal APR-DRG code between January 2012 and June 2013, in-hospital mortality, cost of care, readmissions, length of stay (LOS) and consultant use were analyzed using logistic regression and adjusted for age, insurance status, severity of illness, and risk of mortality.
• Results: No statistically significant differences were found between the 2 groups for in-hospital mortality (odds ratio [OR], 0.89 [95% confidence interval {CI}, 0.66–1.19]; P = 0.42), readmissions (OR, 0.95 [95% CI, 0.87–1.04]; P = 0.27), length of stay (effect size 0.99 days shorter LOS in expanded PA group, 95% CI, 0.97 to 1.01 days; P = 0.34) or consultant use (OR 1.00, 95% CI 0.94–1.07, P = 0.90). Cost of care was less in the expanded PA group (effect size 3.52% less; estimated cost $2644 vs $2724; 95% CI 2.66%–4.39%, P < 0.001).
• Conclusion: An expanded PA hospitalist staffing model at a community hospital provided similar outcomes at a lower cost of care.

Hospitalist program staffing models must optimize efficiency while maintaining clinical outcomes in order to increase value and decrease costs [1]. The cost of hospitalist programs is burdensome, with nearly 94% of groups nationally requiring financial support beyond professional fees [2]. Nationally, for hospitalist groups serving adults, average institutional support is over $156,000 per physician full time equivalent (FTE) (182 twelve-hour clinical shifts per calendar year) [2]. Significant savings could be achieved if less costly physician assistants could be incorporated into clinical teams to provide similar care without sacrificing quality.

Nurse practitioners (NPs) and physician assistants (PAs) have been successfully employed on academic hospitalist services to complement physician staffing [3–10]. They perform admissions, consults, rounding visits and discharges with physician collaboration as permitted by each group’s policies and in accordance with hospital by-laws and state regulations. A median of 0.25 NP and 0.28 PA FTEs per physician FTE are employed by hospitalist groups that incorporate them, though staffing ratios vary widely [2].

Physicians Inpatient Care Specialists (MDICS) devel-oped a staffing model that deploys PAs to see a large proportion of its patients collaboratively with physicians, and with a higher patient census per PA than has been previously reported [2–5]. The group leaders believed that this would yield similar outcomes for patients at a lower cost to the supporting institution than a conventional staffing model which used fewer PAs to render patient care. Prior inpatient studies have demonstrated comparable clinical outcomes when comparing hospitalist PAs and NPs to residents and fellows [4–10], but to our knowledge no data exist directly comparing hospitalist PAs to hospitalist physicians. This study goes beyond prior work by examining the community, non-teaching setting, and directly comparing outcomes from the expanded use of PAs to those of a hospitalist group staffed with a greater proportion of attending physicians at the same hospital during the same time.

Methods

Setting

The study was performed at Anne Arundel Medical Center (AAMC), a 384-bed community hospital in Annapolis, Maryland, that serves a region of over 1 million people. Approximately 26,000 adult patients are discharged annually. During the study, more than 90% of internal medicine service inpatients were cared for by one of 2 hospitalist groups: a hospital-employed group (“conventional” group, Anne Arundel Medical Group) and a contracted hospitalist group (“expanded PA” group, Physicians Inpatient Care Specialists). The conventional group’s providers received a small incentive for Core Measures compliance for patients with stroke, myocardial infarction, congestive heart failure and pneumonia. The expanded PA group received a flat fee for providing hospitalist services and the group’s providers received a small incentive for productivity from their employer. The study was deemed exempt by the AAMC institutional review board.

Staffing Models, Patient Allocation, and Assignment

Admitted patients were designated to be admitted to one group or the other on the basis of standing arrangements with the patients’ primary care providers. Consultative referrals could also be made from subspecialists, who had discretion as to which group they wished to use.

Each morning, following sign-out report from the night team, each team of day providers determined which patients would be seen by which of their providers. Patients still on service from the previous day would be seen by the same provider again whenever possible in order to maintain continuity. Each individual provider had their own patients for the day who they rounded on independently and were responsible for. Physician involvement with patients seen primarily by PAs occurred as described below. Physicians in both groups were expected to take primary rounding responsibility for patients who were more acute or more complex based on morning sign-out report; there was no more formal mandate for patient allocation to particular provider type.

Physician-PA Collaboration

Patients

Patients discharged between 1 January 2012 and 30 June 2013 by the hospitalist groups were identified by searching AAMC’s Crimson Continuuum of Care (The Advisory Board, Washington, DC), a software analytic tool that is integrated with coded clinical data. Adult patient hospitalizations determined by Crimson to have a medical (non-surgical, non-obstetrical) APR-DRG code as the final principal diagnosis were included. Critically ill patients or those appropriate for “step-down unit” care were cared for by the in-house critical care staff; upon transfer out of critical or step-down care, patients were referred back to the admitting hospitalist team. A diagnosis (and its associated hospitalizations) was excluded for referral bias if the diagnosis was the  principal diagnosis for at least 1% of a group’s discharges and the percentage of patients with that diagnosis was at least two times greater in one group than the other. Hospitalizations with a diagnosis of “ungroupable” (APR-DRG 956) were also excluded.

Measurements

Demographic, insurance status, cost of care, length of stay (LOS), APR-DRG (All Patient Refined Diagnosis-Related Group) severity of illness (SOI) and risk of mortality (ROM), consultant utilization, 30-day all-cause readmission (“readmission rate”), and mortality information was obtained from administrative data and exported into a single database for statistical analysis. Readmissions, inpatient mortality, and cost of care were the primary outcomes; consultant use and length of stay were secondary outcomes. A hospitalization was considered a readmission if the patient returned to inpatient status at AAMC for any reason within 30 days of a previous inpatient discharge. Inpatient mortality was defined as patient death during hospitalization. The cost of care was measured using the case charges associated with each encounter. Charge capture data from both groups was analyzed to classify visits as “physician-only,” “physician co-visit,” and “PA-only” visits. A co-visit consists of the physician visiting the patient after the PA has already done so on the same day, taking their own history and performing their own physical exam, and writing a brief progress note. These data were compared against the exported administrative data to find matching encounters and associated visits, with only matching visits included in the analysis. If a duplicate charge was entered on the same day for a patient, any conflict was resolved in favor of the physician visit. A total of 49,883 and 28,663 matching charges were identified for the conventional and expanded PA groups.

Statistical Methods

Odds of inpatient mortality were calculated using logistic regression and adjusted for age, insurance status, APR-DRG ROM, and LOS. Odds of readmission were calculated using logistic regression and adjusted for age, LOS, insurance and APR-DRG SOI. Cost of care (effect size) was examined using multiple linear regression and adjusted for age, APR-DRG SOI, insurance status and LOS. This model was fit using the logarithmic transformations of cost of care and LOS to correct deviation from normality. Robust regression using MM estimation was used to estimate group effects due to the existence of outliers and high leverage points. Length of stay (effect size) was assessed using the log-transformed variable and adjusted for APR-DRG SOI, age, insurance status and consultant use. Finally, category logistic regression models were fit to estimate the odds of consultant use in the study groups and adjusted for age, LOS, insurance status and APR-DRG SOI.

Results

Records review identified 17,294 adult patient hospitalizations determined by Crimson to have a medical (non-surgical, non-obstetrical) APR-DRG code as the final principal diagnosis.  We excluded 15 expanded PA and 11 conventional hospitalizations that fell under APR-DRG code 956 “ungroupable.” Exclusion for referral bias resulted in the removal of 304 hospitalizations, 207 (3.03%) from the expanded PA group and 97 (0.92%) from the conventional group. These excluded hospitalizations came from 2 APR-DRG codes, urinary stones (code 465) and “other kidney and urinary tract diagnoses” (code 468). This left 6612 hospitalizations in the expanded PA group and 10,352 in the conventional group.

Charge capture data for both groups was used to determine the proportion of encounters rendered by each provider type or combination. In the expanded PA group, 35.73% of visits (10,241 of 28,663) were conducted by a PA, and 64.27% were conducted by a physician or by a PA with a billable physician “co-visit.” In the conventional group, 5.89% of visits (2938 of 49,883) were conducted by a PA, and 94.11% were conducted by a physician only or by a PA with a billable physician “co-visit”.

Readmissions

Overall, 929 of 6612 (14.05%) and 1417 of 10,352 (13.69%) patients were readmitted after being discharged by the expanded PA and conventional groups, respectively. After multivariate analysis, there was no statistically significant difference in odds of readmission between the groups (OR for conventional group, 0.95 [95% CI, 0.87–1.04]; P = 0.27). 

Inpatient Mortality

Unadjusted inpatient mortality for the expanded PA group was 1.30% and 0.99% for the conventional group.  After multivariate analysis, there was no statistically significant difference in odds of in-hospital mortality between the groups (OR for conventional group, 0.89 [95% CI, 0.66–1.19]; P = 0.42).

Patient Charges

The unadjusted mean patient charge in the expanded PA group was $7822 ± $7755 and in the conventional group mean patient charge was $8307 ± 10,034. Multivariate analysis found significantly lower adjusted patient charges in the expanded PA group relative to the conventional group (3.52% lower in the expanded PA group [95% CI, 2.66%–4.39%, P < 0.001). When comparing a “standard” patient who was between 80–89 and had Medicare insurance and an SOI of “major,” the cost of care was $2644 in the expanded PA group vs $2724 in the conventional group.

Length of Stay

Unadjusted mean length of stay was 4.1 ± 3.9 days and 4.3 ± 5.6 days for the expanded PA and conventional groups, respectively. After multivariate analysis, when comparing the statistical model “standard” patient, there was no significant difference in the length of stay between the 2 groups (effect size, 0.99 days shorter LOS in the expanded PA group [95% CI, 0.97–1.01 days]; P = 0.34)

Consultant Use

Utilization of consultants was also assessed. The expanded PA group used a mean of 0.55 consultants per case, and the conventional group used 0.56. After multivariate adjustment, there was no significant difference in consulting service use between groups (OR 1.00 [95% CI, 0.94–1.07]; P = 0.90).

Discussion

Maximizing value and minimizing health care costs is a national priority. To our knowledge, this is the first study to compare hospitalist PAs in a community, non-teaching practice directly and contemporaneously to peer PAs and attending physicians and examine the impact on outcomes. In our study, a much larger proportion of patient visits were conducted primarily by PAs without a same-day physician visit in the expanded PA group (35.73%, vs 5.89% in the conventional group). There was no statistically significant difference in inpatient mortality, length of stay or readmissions. In addition, costs of care measured as hospital charges to patients were lower in the expanded PA group. Consultants were not used disproportionately by the expanded PA group in order to achieve these results. Our results are consistent with studies that have compared PAs and NPs at academic centers to traditional housestaff teams and which show that services staffed with PAs or NPs that provide direct care to medical inpatients are non-inferior [4–10].

This study’s expanded PA group’s PAs rounded on 14 patients per day, close to the “magic 15” that is considered by many a good compromise for hospitalist physicians between productivity and quality [11,12].  This is substantially more than the 6 to 10 patients PAs have been responsible for in previously reported studies [3,4,6]. As the median salary for a PA hospitalist is $102,960 compared with the median internal medicine physician hospitalist salary of $253,977 [2], using hospitalist PAs in a collaboration model as described herein could result in significant savings for supporting institutions without sacrificing quality.

We recognize several limitations to this study. First, the data were obtained retrospectively from a single center and patient assignment between groups was nonrandomized. The significant differences in the baseline characteristics of patients between the study groups, however, were adjusted for in multivariate analysis, and potential referral bias was addressed through our  exclusion criteria. Second, our comparison relied on coding rather than clinical data for diagnosis grouping. However, administrative data is commonly used to determine the primary diagnosis for study patients and the standard for reimbursement. Third, we recognize that there may have been unmeasured confounders that may have affected the outcomes. However, the same resources, including consultants and procedure services, were readily available to both groups and there was no significant difference in consultation rates. Fourth, “cost of care” was measured as overall charges to patients, not cost to the hospital. However, given that all the encounters occurred at the same hospital in the same time frame, the difference should be proportional and equal between groups. Finally, our readmission rates did not account for patients readmitted to other institutions. However, there should not have been a differential effect between the 2 study groups, given the shared patient catchment area and our exclusion for referral bias.

It should also be noted that the expanded PA group used a structured collaboration framework and incorporated a structured education program for its PAs. These components are integral to the expanded PA model, and our results may not be generalizable outside of a similar framework. The expanded PA group’s PAs were carefully selected at the time of hire, specifically educated, and supported through ongoing collaboration to provide efficient and appropriate care at the “top of their licenses”. Not all medical groups will be able to provide this level of support and education, and not all hospitalist PAs will want to and/or be able to reach this level of proficiency. However, successful implementation is entirely achievable for groups that invest the effort. The MDICS education process included 80 hours of didactic sessions spread over several months and is based on the Society of Hospital Medicine Core Competencies [13] as well as 6 months of supervised bedside education with escalating clinical responsibilities under the tutelage of an experienced physician or PA. Year-long academic PA fellowships have also been developed for purposes of similar training at several institutions [14].

Conclusion

Our results show that expanded use of well-educated PAs functioning within a formal collaboration arrangement with physicians provides similar clinical quality to a conventional PA staffing model with no excess patient care costs. The model also allows substantial salary savings to supporting institutions, which is important to hospital and policy stakeholders given the implications for hospitalist group staffing, increasing value, and allocation of precious time and financial resources.

Acknowledgements: The authors wish to thank Kevin Funk, MBA, of MDICS, Clarence Richardson, MBA, of GeBBs Software International, and Heather Channing, Kayla King, and Laura Knox of Anne Arundel Healthcare Enterprise, who provided invaluable help with the data aggregation used for this study.

Corresponding author: Timothy M. Capstack, MD, 7250 Parkway Dr, Suite 500, Hanover, MD 21076, [email protected].

Financial disclosures: Dr. Capstack has ownership interest in Physicians Inpatient Care Specialists (MDICS). Ms. Segujja received compensation from MDICS for statistical analysis.