Daniel C. McFarland, DO

Pain management is an integral component of patient‐centered medical care and is a major concern for patients who are hospitalized.[1] Patient‐reported ratings of pain management are highly correlated with overall satisfaction with healthcare delivery.[2] Current research indicates that patient satisfaction with pain management may be improving[3]; however, there may be structural and county‐level disparities in these improvements in satisfaction. Although patient satisfaction with pain management increased from 2008 to 2012, a discrepancy in patient satisfaction with pain management has emerged between 3 different hospital systems (safety net, acute care, critical access hospitals)[3] Specifically, acute care hospitals provide less satisfactory pain management as compared to critical access hospitals.[3] Although patients' perception of pain management is an integral part of delivering patient‐centered care, prior research indicates that there may not be a simple inverse association between pain intensity score and patient satisfaction.[4] The management of pain in hospitals continues to be problematic, perhaps, for instance, due to discrepancies in understanding the relationship between patient satisfaction and pain management. Certainly for this reason and many others, satisfaction with pain management is now one of the dimensions assessed by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, which is a global measure of patient satisfaction.

The HCAHPS survey is utilized by 85% of all US‐based hospitals and gathers patient satisfaction information pertaining to 10 dimensions, including pain management. Patient satisfaction scores (via HCAHPS) now constitute 30% of Hospital Value‐Based Purchasing (HVBP), which makes up 2% of at‐risk reimbursements by the Centers for Medicare and Medicaid Services (CMS) as put forth by the Affordable Care Act (ACA) of 2010.[5] The ACA mandates that payments to hospitals must partly depend on metrics that assess patient satisfaction, as broadly measured by the HCAHPS, which are completed by patients upon hospital discharge.[5, 6] Therefore, patient satisfaction, as measured by patients, now directly affects CMS payments for over 3000 hospitals across the United States. This constitutes a large amount of money for most hospitals that operate on high revenue but have low profit margins. As such, the 2% at‐risk reimbursement may place many hospitals at financial risk that could be ameliorated with effective inpatient pain management.

In addition to its critical role in reimbursement to hospitals, patient satisfaction with pain management is also integrally related to providing patient‐centered care. As such, patient satisfaction with pain management is considered a critical element of various models of the patient‐centered approach to providing medical care. Although a medical inpatient team can assess objective signs of pain, patient‐centric pain measurements are paramount in understanding the pain experience of patients and providing adequate pain management care. Moreover, patients, doctors, payers of medical services, and now CMS increasingly regard a patient‐centered approach to medical care as crucial for the delivery of high‐quality care.

HCAHPS survey sampling represents an excellent opportunity to help assess current gaps in patient‐centered clinical care. However, ecological factors, such as county‐level demographics and hospital size (eg, bed number), are known to influence health outcomes but have not been adequately studied in pain management patient satisfaction.[7] Hospital and county‐level factors may influence the degree to which patients experience patient‐centered pain management care. For instance, most patient satisfaction scores are worse in urban areas.[8, 9] These disparities in patient satisfaction scores could be associated with population density, greater ethnic diversity or nonEnglish‐speaking individuals, or number of hospital beds.

The US Census demographics and hospital‐bed number provide a concurrent measure that can be used across the country to estimate hospital ecology. This study evaluated the influence of county‐level demographic and structural factors (ie, hospital beds) on patient satisfaction with hospital pain management in all HCAHPS‐participating hospitals across the United States. We hypothesized that demographic diversity, higher population density, and higher numbers of hospital beds would predict lower levels of patient satisfaction with inpatient pain management.

METHODS

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total of 4621 US hospitals (85%). The majority of hospitals (73.8%, n = 2884) collected over 300 surveys, fewer (n = 696) collected 100 to 299 surveys, and a small number of hospitals (n = 327) collected less than 100 surveys. Based on the most conservative estimate, results were available from at least 934,800 individual surveys. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] = 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%).

DISCUSSION

By utilizing county‐level demographic data and the HCAHPS survey measures from across the United States, this study provides a representative sample of US hospitals that can be used to define ecological trends in patient satisfaction with pain management. This statistical model demonstrates the nonrandom variability of pain management satisfaction across the United States, even after CMS patient‐mix adjustment. Although the quality of pain management may be increasing by some reports, our present results indicate that pain management satisfaction is not equitable with the rest of the country among select groups of patients (eg, foreign born, female gender, areas of long travel times to work) or in certain care settings (eg, larger hospitals, population dense areas). These data suggest that areas of pain management may lack in quality compared to pain management across the entire US as a whole. This is consistent with the increasingly recognized contribution of multiple nonmedical determinates to health outcomes.[12] These results demonstrate the overall magnitude of healthcare disparity in the United States, and are particularly concerning because African Americans and Hispanics tend to rate overall satisfaction higher than Caucasians in other studies.[13, 14] The same minority reporting bias may be reflected in HCAHPS results. These patients may be reporting higher pain management satisfaction that is not consistent with the level of care they received, as studies have consistently indicated worse pain management delivery for racial and ethnic minorities.[15]

The present findings reveal structural (eg, hospital beds) and demographic (eg, population density, foreign born) gaps in satisfaction with pain management. An effort to improve pain management for all people in the heterogeneous makeup of the United States is an enormous challenge. However, change may be forthcoming, as Hospital Value‐Based Purchasing draws attention pain practice inequities in real time. Although several of the significant explanatory variables cannot be modified (eg, size of hospital, urban setting, patients served), pain management delivery should receive extra attention in hospitals with those characteristics. Pain management delivery in large, urban hospitals that serve foreign‐born patients may be improved with focused multilevel interventions. Future research should examine these inequities further and develop multilevel interventions that target hospitals in at‐risk areas with the aim of lessening disparities in hospital‐based pain management.

Pain management is an integral component of patient‐centered medical care and is a major concern for patients who are hospitalized.[1] Patient‐reported ratings of pain management are highly correlated with overall satisfaction with healthcare delivery.[2] Current research indicates that patient satisfaction with pain management may be improving[3]; however, there may be structural and county‐level disparities in these improvements in satisfaction. Although patient satisfaction with pain management increased from 2008 to 2012, a discrepancy in patient satisfaction with pain management has emerged between 3 different hospital systems (safety net, acute care, critical access hospitals)[3] Specifically, acute care hospitals provide less satisfactory pain management as compared to critical access hospitals.[3] Although patients' perception of pain management is an integral part of delivering patient‐centered care, prior research indicates that there may not be a simple inverse association between pain intensity score and patient satisfaction.[4] The management of pain in hospitals continues to be problematic, perhaps, for instance, due to discrepancies in understanding the relationship between patient satisfaction and pain management. Certainly for this reason and many others, satisfaction with pain management is now one of the dimensions assessed by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, which is a global measure of patient satisfaction.

The HCAHPS survey is utilized by 85% of all US‐based hospitals and gathers patient satisfaction information pertaining to 10 dimensions, including pain management. Patient satisfaction scores (via HCAHPS) now constitute 30% of Hospital Value‐Based Purchasing (HVBP), which makes up 2% of at‐risk reimbursements by the Centers for Medicare and Medicaid Services (CMS) as put forth by the Affordable Care Act (ACA) of 2010.[5] The ACA mandates that payments to hospitals must partly depend on metrics that assess patient satisfaction, as broadly measured by the HCAHPS, which are completed by patients upon hospital discharge.[5, 6] Therefore, patient satisfaction, as measured by patients, now directly affects CMS payments for over 3000 hospitals across the United States. This constitutes a large amount of money for most hospitals that operate on high revenue but have low profit margins. As such, the 2% at‐risk reimbursement may place many hospitals at financial risk that could be ameliorated with effective inpatient pain management.

In addition to its critical role in reimbursement to hospitals, patient satisfaction with pain management is also integrally related to providing patient‐centered care. As such, patient satisfaction with pain management is considered a critical element of various models of the patient‐centered approach to providing medical care. Although a medical inpatient team can assess objective signs of pain, patient‐centric pain measurements are paramount in understanding the pain experience of patients and providing adequate pain management care. Moreover, patients, doctors, payers of medical services, and now CMS increasingly regard a patient‐centered approach to medical care as crucial for the delivery of high‐quality care.

HCAHPS survey sampling represents an excellent opportunity to help assess current gaps in patient‐centered clinical care. However, ecological factors, such as county‐level demographics and hospital size (eg, bed number), are known to influence health outcomes but have not been adequately studied in pain management patient satisfaction.[7] Hospital and county‐level factors may influence the degree to which patients experience patient‐centered pain management care. For instance, most patient satisfaction scores are worse in urban areas.[8, 9] These disparities in patient satisfaction scores could be associated with population density, greater ethnic diversity or nonEnglish‐speaking individuals, or number of hospital beds.

The US Census demographics and hospital‐bed number provide a concurrent measure that can be used across the country to estimate hospital ecology. This study evaluated the influence of county‐level demographic and structural factors (ie, hospital beds) on patient satisfaction with hospital pain management in all HCAHPS‐participating hospitals across the United States. We hypothesized that demographic diversity, higher population density, and higher numbers of hospital beds would predict lower levels of patient satisfaction with inpatient pain management.

METHODS

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total of 4621 US hospitals (85%). The majority of hospitals (73.8%, n = 2884) collected over 300 surveys, fewer (n = 696) collected 100 to 299 surveys, and a small number of hospitals (n = 327) collected less than 100 surveys. Based on the most conservative estimate, results were available from at least 934,800 individual surveys. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] = 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%).

DISCUSSION

By utilizing county‐level demographic data and the HCAHPS survey measures from across the United States, this study provides a representative sample of US hospitals that can be used to define ecological trends in patient satisfaction with pain management. This statistical model demonstrates the nonrandom variability of pain management satisfaction across the United States, even after CMS patient‐mix adjustment. Although the quality of pain management may be increasing by some reports, our present results indicate that pain management satisfaction is not equitable with the rest of the country among select groups of patients (eg, foreign born, female gender, areas of long travel times to work) or in certain care settings (eg, larger hospitals, population dense areas). These data suggest that areas of pain management may lack in quality compared to pain management across the entire US as a whole. This is consistent with the increasingly recognized contribution of multiple nonmedical determinates to health outcomes.[12] These results demonstrate the overall magnitude of healthcare disparity in the United States, and are particularly concerning because African Americans and Hispanics tend to rate overall satisfaction higher than Caucasians in other studies.[13, 14] The same minority reporting bias may be reflected in HCAHPS results. These patients may be reporting higher pain management satisfaction that is not consistent with the level of care they received, as studies have consistently indicated worse pain management delivery for racial and ethnic minorities.[15]

The present findings reveal structural (eg, hospital beds) and demographic (eg, population density, foreign born) gaps in satisfaction with pain management. An effort to improve pain management for all people in the heterogeneous makeup of the United States is an enormous challenge. However, change may be forthcoming, as Hospital Value‐Based Purchasing draws attention pain practice inequities in real time. Although several of the significant explanatory variables cannot be modified (eg, size of hospital, urban setting, patients served), pain management delivery should receive extra attention in hospitals with those characteristics. Pain management delivery in large, urban hospitals that serve foreign‐born patients may be improved with focused multilevel interventions. Future research should examine these inequities further and develop multilevel interventions that target hospitals in at‐risk areas with the aim of lessening disparities in hospital‐based pain management.

Pain management is an integral component of patient‐centered medical care and is a major concern for patients who are hospitalized.[1] Patient‐reported ratings of pain management are highly correlated with overall satisfaction with healthcare delivery.[2] Current research indicates that patient satisfaction with pain management may be improving[3]; however, there may be structural and county‐level disparities in these improvements in satisfaction. Although patient satisfaction with pain management increased from 2008 to 2012, a discrepancy in patient satisfaction with pain management has emerged between 3 different hospital systems (safety net, acute care, critical access hospitals)[3] Specifically, acute care hospitals provide less satisfactory pain management as compared to critical access hospitals.[3] Although patients' perception of pain management is an integral part of delivering patient‐centered care, prior research indicates that there may not be a simple inverse association between pain intensity score and patient satisfaction.[4] The management of pain in hospitals continues to be problematic, perhaps, for instance, due to discrepancies in understanding the relationship between patient satisfaction and pain management. Certainly for this reason and many others, satisfaction with pain management is now one of the dimensions assessed by the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey, which is a global measure of patient satisfaction.

The HCAHPS survey is utilized by 85% of all US‐based hospitals and gathers patient satisfaction information pertaining to 10 dimensions, including pain management. Patient satisfaction scores (via HCAHPS) now constitute 30% of Hospital Value‐Based Purchasing (HVBP), which makes up 2% of at‐risk reimbursements by the Centers for Medicare and Medicaid Services (CMS) as put forth by the Affordable Care Act (ACA) of 2010.[5] The ACA mandates that payments to hospitals must partly depend on metrics that assess patient satisfaction, as broadly measured by the HCAHPS, which are completed by patients upon hospital discharge.[5, 6] Therefore, patient satisfaction, as measured by patients, now directly affects CMS payments for over 3000 hospitals across the United States. This constitutes a large amount of money for most hospitals that operate on high revenue but have low profit margins. As such, the 2% at‐risk reimbursement may place many hospitals at financial risk that could be ameliorated with effective inpatient pain management.

In addition to its critical role in reimbursement to hospitals, patient satisfaction with pain management is also integrally related to providing patient‐centered care. As such, patient satisfaction with pain management is considered a critical element of various models of the patient‐centered approach to providing medical care. Although a medical inpatient team can assess objective signs of pain, patient‐centric pain measurements are paramount in understanding the pain experience of patients and providing adequate pain management care. Moreover, patients, doctors, payers of medical services, and now CMS increasingly regard a patient‐centered approach to medical care as crucial for the delivery of high‐quality care.

HCAHPS survey sampling represents an excellent opportunity to help assess current gaps in patient‐centered clinical care. However, ecological factors, such as county‐level demographics and hospital size (eg, bed number), are known to influence health outcomes but have not been adequately studied in pain management patient satisfaction.[7] Hospital and county‐level factors may influence the degree to which patients experience patient‐centered pain management care. For instance, most patient satisfaction scores are worse in urban areas.[8, 9] These disparities in patient satisfaction scores could be associated with population density, greater ethnic diversity or nonEnglish‐speaking individuals, or number of hospital beds.

The US Census demographics and hospital‐bed number provide a concurrent measure that can be used across the country to estimate hospital ecology. This study evaluated the influence of county‐level demographic and structural factors (ie, hospital beds) on patient satisfaction with hospital pain management in all HCAHPS‐participating hospitals across the United States. We hypothesized that demographic diversity, higher population density, and higher numbers of hospital beds would predict lower levels of patient satisfaction with inpatient pain management.

METHODS

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total of 4621 US hospitals (85%). The majority of hospitals (73.8%, n = 2884) collected over 300 surveys, fewer (n = 696) collected 100 to 299 surveys, and a small number of hospitals (n = 327) collected less than 100 surveys. Based on the most conservative estimate, results were available from at least 934,800 individual surveys. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] = 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%).

DISCUSSION

By utilizing county‐level demographic data and the HCAHPS survey measures from across the United States, this study provides a representative sample of US hospitals that can be used to define ecological trends in patient satisfaction with pain management. This statistical model demonstrates the nonrandom variability of pain management satisfaction across the United States, even after CMS patient‐mix adjustment. Although the quality of pain management may be increasing by some reports, our present results indicate that pain management satisfaction is not equitable with the rest of the country among select groups of patients (eg, foreign born, female gender, areas of long travel times to work) or in certain care settings (eg, larger hospitals, population dense areas). These data suggest that areas of pain management may lack in quality compared to pain management across the entire US as a whole. This is consistent with the increasingly recognized contribution of multiple nonmedical determinates to health outcomes.[12] These results demonstrate the overall magnitude of healthcare disparity in the United States, and are particularly concerning because African Americans and Hispanics tend to rate overall satisfaction higher than Caucasians in other studies.[13, 14] The same minority reporting bias may be reflected in HCAHPS results. These patients may be reporting higher pain management satisfaction that is not consistent with the level of care they received, as studies have consistently indicated worse pain management delivery for racial and ethnic minorities.[15]

The present findings reveal structural (eg, hospital beds) and demographic (eg, population density, foreign born) gaps in satisfaction with pain management. An effort to improve pain management for all people in the heterogeneous makeup of the United States is an enormous challenge. However, change may be forthcoming, as Hospital Value‐Based Purchasing draws attention pain practice inequities in real time. Although several of the significant explanatory variables cannot be modified (eg, size of hospital, urban setting, patients served), pain management delivery should receive extra attention in hospitals with those characteristics. Pain management delivery in large, urban hospitals that serve foreign‐born patients may be improved with focused multilevel interventions. Future research should examine these inequities further and develop multilevel interventions that target hospitals in at‐risk areas with the aim of lessening disparities in hospital‐based pain management.

The Affordable Care Act of 2010 mandates that government payments to hospitals and physicians must depend, in part, on metrics that assess the quality and efficiency of healthcare being provided to encourage value‐based healthcare.[1] Value in healthcare is defined by the delivery of high‐quality care at low cost.[2, 3] To this end, Hospital Value‐Based Purchasing (HVBP) and Physician Value‐Based Payment Modifier programs have been developed by the Centers for Medicare & Medicaid Services (CMS). HVBP is currently being phased in and affects CMS payments for fiscal year (FY) 2013 for over 3000 hospitals across the United States to incentivize healthcare delivery value. The final phase of implementation will be in FY 2017 and will then affect 2% of all CMS hospital reimbursement. HVBP is based on objective measures of hospital performance as well as a subjective measure of performance captured under the Patient Experience of Care domain. This subjective measure will remain at 30% of the aggregate score until FY 2016, when it will then be 25% the aggregate score moving forward.[4] The program rewards hospitals for both overall achievement and improvement in any domain, so that hospitals have multiple ways to receive financial incentives for providing quality care.[5] Even still, there appears to be a nonrandom pattern of patient satisfaction scores across the country with less favorable scores clustering in densely populated areas.[6]

Value‐Based Purchasing and other incentive‐based programs have been criticized for increasing disparities in healthcare by penalizing larger hospitals (including academic medical centers, safety‐net hospitals, and others that disproportionately serve lower socioeconomic communities) and favoring physician‐based specialty hospitals.[7, 8, 9] Therefore, hospitals that serve indigent and elderly populations may be at a disadvantage.[9, 10] HVBP portends significant economic consequences for the majority of hospitals that rely heavily on Medicare and Medicaid reimbursement, as most hospitals have large revenues but low profit margins.[11] Higher HVBP scores are associated with for profit status, smaller size, and location in certain areas of the United States.[12] Jha et al.[6] described Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores regional geographic variability, but concluded that poor satisfaction was due to poor quality.

The Patient Experience of Care domain quantifies patient satisfaction using the validated HCAHPS survey, which is provided to a random sample of patients continuously throughout the year at 48 hours to 6 weeks after discharge. It is a publically available standardized survey instrument used to measure patients perspectives on hospital care. It assesses the following 8 dimensions: nurse communication, doctor communication, hospital staff responsiveness, pain management, medicine communication, discharge information, hospital cleanliness and quietness, and overall hospital rating, of which the last 2 dimensions each have 2 measures (cleanliness and quietness) and (rating 9 or 10 and definitely recommend) to give a total of 10 distinct measures.

The United States is a complex network of urban, suburban, and rural demographic areas. Hospitals exist within a unique contextual and compositional meshwork that determines its caseload. The top population density decile of the United States lives within 37 counties, whereas half of the most populous parts of the United States occupy a total of 250 counties out of a total of 3143 counties in the United States. If the 10 measures of patient satisfaction (HCAHPS) scores were abstracted from hospitals and viewed according to county‐level population density (separated into deciles across the United States), a trend would be apparent (Figure 1). Greater population density is associated with lower patient satisfaction in 9 of 10 categories. On the state level, composite scores of overall patient satisfaction (amount of positive scores) of hospitals show a 12% variability and a significant correlation with population density (r=0.479; Figure 2). The lowest overall satisfaction scores are obtained from hospitals located in the population‐dense regions of Washington, DC, New York State, California, Maryland, and New Jersey (ie, 63%65%), and the best scores are from Louisiana, South Dakota, Iowa, Maine, and Vermont (ie, 74%75%). The average patient satisfaction score is 71%2.9%. Lower patient satisfaction scores appear to cluster in population‐dense areas and may be associated with greater heterogeneous patient demographics and economic variability in addition to population density.

Figure 1

Figure 2

These observations are surprising considering that CMS already adjusts HCAHPS scores based on patient‐mix coefficients and mode of collection.[13, 14, 15, 16, 17, 18] Adjustments are updated multiple times per year and account for survey collection either by telephone, email, or paper survey, because the populations that select survey forms will differ. Previous studies have shown that demographic features influence the patient evaluation process. For example, younger and more educated patients were found to provide less positive evaluations of healthcare.[19]

This study examined whether patients perceptions of healthcare (pattern of patient satisfaction) as quantified under the patient experience domain of HVBP were affected and predicted by population density and other demographic factors that are outside the control of individual hospitals. In addition, hospital‐level data (eg, number of hospital beds) and county‐level data such as race, age, gender, overall population, income, time spent commuting to work, primary language, and place of birth were analyzed for correlation with patient satisfaction scores. Our study demonstrates that demographic and hospital‐level data can predict patient satisfaction scores and suggests that CMS may need to modify its adjustment formulas to eliminate bias in HVBP‐based reimbursement.

METHODS

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total 4621 hospitals listed by the Hospital Compare website (85%). The majority of hospitals (2884) collected over 300 surveys, fewer hospitals (696) collected 100 to 299 surveys, and fewer still (333) collected <100 surveys. In total, results were available from at least 934,800 individual surveys, by the most conservative estimate. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%). Multivariate regression modeling across all HCAHPS dimensions found that between 10 and 16 of the 20 predictors (US Census categories) were statistically significant and predictive of individual HCAHPS dimension scores and the aggregate percent positive score as demonstrated in Table 2. For example, county percentage of bachelors degrees positively predicts for positive doctor communication scores, and hospital beds negatively predicts for quiet dimension. The strongest positive and negative predictive variables by model regression coefficients for each HCAHPS dimension are also listed in Table 2.

Table 1 highlights multivariate regression modeling of the composite average positive score, which produced an adjusted R² of 0.222 (P<0.001). All variables were significant and predicted change of the composite HCAHPS except for place of birthforeign born (not listed in the table). Table 1 ranks variables from most positive to most negative predictors.

Other HCAHPS domains demonstrated statistically significant models (P<0.001) and are listed by their coefficients of determination (ie, adjusted R²) (Table 2). The best‐fit dimensions were help (adjusted R²=0.304), quiet (adjusted R²=0.299), doctor communication (adjusted R²=0.298), nurse communication (adjusted R²=0.245), and clean (adjusted R²=0.232). Models that were not as strongly predictive as the composite score included pain (adjusted R²=0.124), overall 9/10 (adjusted R²=0.136), definitely recommend (adjusted R²=0.150), and explained meds (adjusted R²=0.169).

A predictive formula for average positive scores was created by determination of the most predictive partial coefficients and the best‐fit model. Bachelor's degree and white only were the 2 greatest positive predictors, and number of hospital beds and nonEnglish speaking were the 2 greatest negative predictors. The PPSF (predictive formula) was chosen out of various combinations of predictors (Table 1), because its coefficient of determination (adjusted R²=0.155) was closest to the overall model's coefficient of determination (adjusted R²=0.222) without demonstrating colinearity. Possible predictive formulas were based on the predictors standardized and included the following combinations: the 2 greatest overall predictors (adjusted R²=0.051), the 2 greatest negative and positive predictors (adjusted R²=0.098), the 4 greatest overall predictors (adjusted R²=0.117), and the 8 greatest overall predictors (adjusted R²=0.201), which suffered from colinearity (household size plus nonEnglish speaking [Pearson=0.624] and under 18 years old [Pearson=0.708]). None of the correlated independent variables (eg, poverty and median income) were placed in the final model.

The mean WIPSAS scores closely corresponded with the national average of HCAHPS scores (71.6 vs 71.84) but compressed scores into a narrower distribution (SD 5.52 vs 5.92). The greatest positive and negative changes were by 8.51% and 2.25%, respectively. Essentially, a smaller number of hospitals in demographically challenged areas were more significantly impacted by the WIPSAS adjustment than the larger number of hospitals in demographically favorable areas. Large hospitals in demographically diverse counties saw the greatest positive change (e.g., Texas, California, and New York), whereas smaller hospitals in demographically nondiverse areas saw comparatively smaller decrements in the overall WIPSAS scores. The WIPSAS had the most beneficial effect on urban and rural safety‐net hospitals that serve diverse populations including many academic medical centers. This is illustrated by the reranking of the top 10 and bottom 10 hospitals in New York State by the WIPSAS (Table 3). For example, 3 academic medical centers in New York State, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were moved from the 46th, 43rd, and 42nd (out of 167 hospitals) respectively into the top 10 in patient satisfaction utilizing the WIPSAS methodology. Reported patient satisfaction scores, PPSF, WIPPSS, and WIPSAS scores for each hospital in the United States are available online (see Supporting Table S1 in the online version of this article).

DISCUSSION

The HVBP program is an incentive program that is meant to enhance the quality of care. This study illustrates healthcare inequalities in patient satisfaction that are not accounted for by the current CMS adjustments, and shows that education, ethnicity, primary language, and number of hospital beds are predictive of how patients evaluate their care via patient satisfaction scores. Hospitals that treat a disproportionate percentage of nonEnglish speaking, nonwhite, noneducated patients in large facilities are not meeting patient satisfaction standards. This inequity is not ameliorated by the adjustments currently performed by CMS, and has financial consequences for those hospitals that are not meeting national standards in patient satisfaction. These hospitals, which often include academic medical centers in urban areas, may therefore be penalized under the existing HVBP reimbursement models.

Using only 4 demographic and hospital‐specific predictors (ie, hospital beds, percent nonEnglish speaking, percent bachelors degrees, percent white), it is possible to utilize a simple formula to predict patient satisfaction with a significant degree of correlation to the reported scores available through Hospital Compare.

Our initial hypothesis that population density predicted lower patient satisfaction scores was confirmed, but these aforementioned demographic and hospital‐based factors were stronger independent predictors of HCAHPS scores. The WIPSAS is a representation of patient satisfaction and quality‐of‐care delivery across the country that accounts for nonrandom variation in patient satisfaction scores.

For hospitals in New York State, WIPSAS resulted in the placement of 3 urban‐based academic medical centers in the top 10 in patient satisfaction, when previously, based on the raw scores, their rankings were between 42nd and 46th statewide. Prior studies have suggested that large, urban, teaching, and not‐for‐profit hospitals were disadvantaged based on their hospital characteristics and patient features.[10, 11, 12] Under the current CMS reimbursement methodologies, these institutions are more likely to receive financial penalties.[8] The WIPSAS is a simple method to assess hospitals performance in the area of patient satisfaction that accounts for the demographic and hospital‐based factors (eg, number of beds) of the hospital. Its incorporation into CMS reimbursement calculations, or incorporation of a similar adjustment formula, should be strongly considered to account for predictive factors in patient satisfaction that could be addressed to enhance their scores.

Limitations for this study are the approximation of county‐level data for actual individual hospital demographic information and the exclusion of specialty hospitals, such as cancer centers and children's hospitals, in HCAHPS surveys. Repeated multivariate analyses at different time points would also serve to identify how CMS‐specific adjustments are recalibrated over time. Although we have primarily reported on the composite percent positive score as a surrogate for all HCAHPS dimensions, an individual adjustment formula could be generated for each dimension of the patient experience of care domain.

Although patient satisfaction is a component of how quality should be measured, further emphasis needs to be placed on nonrandom patient satisfaction variance so that HVBP can serve as an incentivizing program for at‐risk hospitals. Regional variation in scoring is not altogether accounted for by the current CMS adjustment system. Because patient satisfaction scores are now directly linked to reimbursement, further evaluation is needed to enhance patient satisfaction scoring paradigms to account for demographic and hospital‐specific factors.

Disclosure

Nothing to report.

The Affordable Care Act of 2010 mandates that government payments to hospitals and physicians must depend, in part, on metrics that assess the quality and efficiency of healthcare being provided to encourage value‐based healthcare.[1] Value in healthcare is defined by the delivery of high‐quality care at low cost.[2, 3] To this end, Hospital Value‐Based Purchasing (HVBP) and Physician Value‐Based Payment Modifier programs have been developed by the Centers for Medicare & Medicaid Services (CMS). HVBP is currently being phased in and affects CMS payments for fiscal year (FY) 2013 for over 3000 hospitals across the United States to incentivize healthcare delivery value. The final phase of implementation will be in FY 2017 and will then affect 2% of all CMS hospital reimbursement. HVBP is based on objective measures of hospital performance as well as a subjective measure of performance captured under the Patient Experience of Care domain. This subjective measure will remain at 30% of the aggregate score until FY 2016, when it will then be 25% the aggregate score moving forward.[4] The program rewards hospitals for both overall achievement and improvement in any domain, so that hospitals have multiple ways to receive financial incentives for providing quality care.[5] Even still, there appears to be a nonrandom pattern of patient satisfaction scores across the country with less favorable scores clustering in densely populated areas.[6]

Value‐Based Purchasing and other incentive‐based programs have been criticized for increasing disparities in healthcare by penalizing larger hospitals (including academic medical centers, safety‐net hospitals, and others that disproportionately serve lower socioeconomic communities) and favoring physician‐based specialty hospitals.[7, 8, 9] Therefore, hospitals that serve indigent and elderly populations may be at a disadvantage.[9, 10] HVBP portends significant economic consequences for the majority of hospitals that rely heavily on Medicare and Medicaid reimbursement, as most hospitals have large revenues but low profit margins.[11] Higher HVBP scores are associated with for profit status, smaller size, and location in certain areas of the United States.[12] Jha et al.[6] described Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores regional geographic variability, but concluded that poor satisfaction was due to poor quality.

The Patient Experience of Care domain quantifies patient satisfaction using the validated HCAHPS survey, which is provided to a random sample of patients continuously throughout the year at 48 hours to 6 weeks after discharge. It is a publically available standardized survey instrument used to measure patients perspectives on hospital care. It assesses the following 8 dimensions: nurse communication, doctor communication, hospital staff responsiveness, pain management, medicine communication, discharge information, hospital cleanliness and quietness, and overall hospital rating, of which the last 2 dimensions each have 2 measures (cleanliness and quietness) and (rating 9 or 10 and definitely recommend) to give a total of 10 distinct measures.

The United States is a complex network of urban, suburban, and rural demographic areas. Hospitals exist within a unique contextual and compositional meshwork that determines its caseload. The top population density decile of the United States lives within 37 counties, whereas half of the most populous parts of the United States occupy a total of 250 counties out of a total of 3143 counties in the United States. If the 10 measures of patient satisfaction (HCAHPS) scores were abstracted from hospitals and viewed according to county‐level population density (separated into deciles across the United States), a trend would be apparent (Figure 1). Greater population density is associated with lower patient satisfaction in 9 of 10 categories. On the state level, composite scores of overall patient satisfaction (amount of positive scores) of hospitals show a 12% variability and a significant correlation with population density (r=0.479; Figure 2). The lowest overall satisfaction scores are obtained from hospitals located in the population‐dense regions of Washington, DC, New York State, California, Maryland, and New Jersey (ie, 63%65%), and the best scores are from Louisiana, South Dakota, Iowa, Maine, and Vermont (ie, 74%75%). The average patient satisfaction score is 71%2.9%. Lower patient satisfaction scores appear to cluster in population‐dense areas and may be associated with greater heterogeneous patient demographics and economic variability in addition to population density.

Figure 1

Figure 2

These observations are surprising considering that CMS already adjusts HCAHPS scores based on patient‐mix coefficients and mode of collection.[13, 14, 15, 16, 17, 18] Adjustments are updated multiple times per year and account for survey collection either by telephone, email, or paper survey, because the populations that select survey forms will differ. Previous studies have shown that demographic features influence the patient evaluation process. For example, younger and more educated patients were found to provide less positive evaluations of healthcare.[19]

This study examined whether patients perceptions of healthcare (pattern of patient satisfaction) as quantified under the patient experience domain of HVBP were affected and predicted by population density and other demographic factors that are outside the control of individual hospitals. In addition, hospital‐level data (eg, number of hospital beds) and county‐level data such as race, age, gender, overall population, income, time spent commuting to work, primary language, and place of birth were analyzed for correlation with patient satisfaction scores. Our study demonstrates that demographic and hospital‐level data can predict patient satisfaction scores and suggests that CMS may need to modify its adjustment formulas to eliminate bias in HVBP‐based reimbursement.

METHODS

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total 4621 hospitals listed by the Hospital Compare website (85%). The majority of hospitals (2884) collected over 300 surveys, fewer hospitals (696) collected 100 to 299 surveys, and fewer still (333) collected <100 surveys. In total, results were available from at least 934,800 individual surveys, by the most conservative estimate. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%). Multivariate regression modeling across all HCAHPS dimensions found that between 10 and 16 of the 20 predictors (US Census categories) were statistically significant and predictive of individual HCAHPS dimension scores and the aggregate percent positive score as demonstrated in Table 2. For example, county percentage of bachelors degrees positively predicts for positive doctor communication scores, and hospital beds negatively predicts for quiet dimension. The strongest positive and negative predictive variables by model regression coefficients for each HCAHPS dimension are also listed in Table 2.

Table 1 highlights multivariate regression modeling of the composite average positive score, which produced an adjusted R² of 0.222 (P<0.001). All variables were significant and predicted change of the composite HCAHPS except for place of birthforeign born (not listed in the table). Table 1 ranks variables from most positive to most negative predictors.

Other HCAHPS domains demonstrated statistically significant models (P<0.001) and are listed by their coefficients of determination (ie, adjusted R²) (Table 2). The best‐fit dimensions were help (adjusted R²=0.304), quiet (adjusted R²=0.299), doctor communication (adjusted R²=0.298), nurse communication (adjusted R²=0.245), and clean (adjusted R²=0.232). Models that were not as strongly predictive as the composite score included pain (adjusted R²=0.124), overall 9/10 (adjusted R²=0.136), definitely recommend (adjusted R²=0.150), and explained meds (adjusted R²=0.169).

A predictive formula for average positive scores was created by determination of the most predictive partial coefficients and the best‐fit model. Bachelor's degree and white only were the 2 greatest positive predictors, and number of hospital beds and nonEnglish speaking were the 2 greatest negative predictors. The PPSF (predictive formula) was chosen out of various combinations of predictors (Table 1), because its coefficient of determination (adjusted R²=0.155) was closest to the overall model's coefficient of determination (adjusted R²=0.222) without demonstrating colinearity. Possible predictive formulas were based on the predictors standardized and included the following combinations: the 2 greatest overall predictors (adjusted R²=0.051), the 2 greatest negative and positive predictors (adjusted R²=0.098), the 4 greatest overall predictors (adjusted R²=0.117), and the 8 greatest overall predictors (adjusted R²=0.201), which suffered from colinearity (household size plus nonEnglish speaking [Pearson=0.624] and under 18 years old [Pearson=0.708]). None of the correlated independent variables (eg, poverty and median income) were placed in the final model.

The mean WIPSAS scores closely corresponded with the national average of HCAHPS scores (71.6 vs 71.84) but compressed scores into a narrower distribution (SD 5.52 vs 5.92). The greatest positive and negative changes were by 8.51% and 2.25%, respectively. Essentially, a smaller number of hospitals in demographically challenged areas were more significantly impacted by the WIPSAS adjustment than the larger number of hospitals in demographically favorable areas. Large hospitals in demographically diverse counties saw the greatest positive change (e.g., Texas, California, and New York), whereas smaller hospitals in demographically nondiverse areas saw comparatively smaller decrements in the overall WIPSAS scores. The WIPSAS had the most beneficial effect on urban and rural safety‐net hospitals that serve diverse populations including many academic medical centers. This is illustrated by the reranking of the top 10 and bottom 10 hospitals in New York State by the WIPSAS (Table 3). For example, 3 academic medical centers in New York State, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were moved from the 46th, 43rd, and 42nd (out of 167 hospitals) respectively into the top 10 in patient satisfaction utilizing the WIPSAS methodology. Reported patient satisfaction scores, PPSF, WIPPSS, and WIPSAS scores for each hospital in the United States are available online (see Supporting Table S1 in the online version of this article).

DISCUSSION

The HVBP program is an incentive program that is meant to enhance the quality of care. This study illustrates healthcare inequalities in patient satisfaction that are not accounted for by the current CMS adjustments, and shows that education, ethnicity, primary language, and number of hospital beds are predictive of how patients evaluate their care via patient satisfaction scores. Hospitals that treat a disproportionate percentage of nonEnglish speaking, nonwhite, noneducated patients in large facilities are not meeting patient satisfaction standards. This inequity is not ameliorated by the adjustments currently performed by CMS, and has financial consequences for those hospitals that are not meeting national standards in patient satisfaction. These hospitals, which often include academic medical centers in urban areas, may therefore be penalized under the existing HVBP reimbursement models.

Using only 4 demographic and hospital‐specific predictors (ie, hospital beds, percent nonEnglish speaking, percent bachelors degrees, percent white), it is possible to utilize a simple formula to predict patient satisfaction with a significant degree of correlation to the reported scores available through Hospital Compare.

Our initial hypothesis that population density predicted lower patient satisfaction scores was confirmed, but these aforementioned demographic and hospital‐based factors were stronger independent predictors of HCAHPS scores. The WIPSAS is a representation of patient satisfaction and quality‐of‐care delivery across the country that accounts for nonrandom variation in patient satisfaction scores.

For hospitals in New York State, WIPSAS resulted in the placement of 3 urban‐based academic medical centers in the top 10 in patient satisfaction, when previously, based on the raw scores, their rankings were between 42nd and 46th statewide. Prior studies have suggested that large, urban, teaching, and not‐for‐profit hospitals were disadvantaged based on their hospital characteristics and patient features.[10, 11, 12] Under the current CMS reimbursement methodologies, these institutions are more likely to receive financial penalties.[8] The WIPSAS is a simple method to assess hospitals performance in the area of patient satisfaction that accounts for the demographic and hospital‐based factors (eg, number of beds) of the hospital. Its incorporation into CMS reimbursement calculations, or incorporation of a similar adjustment formula, should be strongly considered to account for predictive factors in patient satisfaction that could be addressed to enhance their scores.

Limitations for this study are the approximation of county‐level data for actual individual hospital demographic information and the exclusion of specialty hospitals, such as cancer centers and children's hospitals, in HCAHPS surveys. Repeated multivariate analyses at different time points would also serve to identify how CMS‐specific adjustments are recalibrated over time. Although we have primarily reported on the composite percent positive score as a surrogate for all HCAHPS dimensions, an individual adjustment formula could be generated for each dimension of the patient experience of care domain.

Although patient satisfaction is a component of how quality should be measured, further emphasis needs to be placed on nonrandom patient satisfaction variance so that HVBP can serve as an incentivizing program for at‐risk hospitals. Regional variation in scoring is not altogether accounted for by the current CMS adjustment system. Because patient satisfaction scores are now directly linked to reimbursement, further evaluation is needed to enhance patient satisfaction scoring paradigms to account for demographic and hospital‐specific factors.

Disclosure

Nothing to report.

The Affordable Care Act of 2010 mandates that government payments to hospitals and physicians must depend, in part, on metrics that assess the quality and efficiency of healthcare being provided to encourage value‐based healthcare.[1] Value in healthcare is defined by the delivery of high‐quality care at low cost.[2, 3] To this end, Hospital Value‐Based Purchasing (HVBP) and Physician Value‐Based Payment Modifier programs have been developed by the Centers for Medicare & Medicaid Services (CMS). HVBP is currently being phased in and affects CMS payments for fiscal year (FY) 2013 for over 3000 hospitals across the United States to incentivize healthcare delivery value. The final phase of implementation will be in FY 2017 and will then affect 2% of all CMS hospital reimbursement. HVBP is based on objective measures of hospital performance as well as a subjective measure of performance captured under the Patient Experience of Care domain. This subjective measure will remain at 30% of the aggregate score until FY 2016, when it will then be 25% the aggregate score moving forward.[4] The program rewards hospitals for both overall achievement and improvement in any domain, so that hospitals have multiple ways to receive financial incentives for providing quality care.[5] Even still, there appears to be a nonrandom pattern of patient satisfaction scores across the country with less favorable scores clustering in densely populated areas.[6]

Value‐Based Purchasing and other incentive‐based programs have been criticized for increasing disparities in healthcare by penalizing larger hospitals (including academic medical centers, safety‐net hospitals, and others that disproportionately serve lower socioeconomic communities) and favoring physician‐based specialty hospitals.[7, 8, 9] Therefore, hospitals that serve indigent and elderly populations may be at a disadvantage.[9, 10] HVBP portends significant economic consequences for the majority of hospitals that rely heavily on Medicare and Medicaid reimbursement, as most hospitals have large revenues but low profit margins.[11] Higher HVBP scores are associated with for profit status, smaller size, and location in certain areas of the United States.[12] Jha et al.[6] described Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores regional geographic variability, but concluded that poor satisfaction was due to poor quality.

The Patient Experience of Care domain quantifies patient satisfaction using the validated HCAHPS survey, which is provided to a random sample of patients continuously throughout the year at 48 hours to 6 weeks after discharge. It is a publically available standardized survey instrument used to measure patients perspectives on hospital care. It assesses the following 8 dimensions: nurse communication, doctor communication, hospital staff responsiveness, pain management, medicine communication, discharge information, hospital cleanliness and quietness, and overall hospital rating, of which the last 2 dimensions each have 2 measures (cleanliness and quietness) and (rating 9 or 10 and definitely recommend) to give a total of 10 distinct measures.

The United States is a complex network of urban, suburban, and rural demographic areas. Hospitals exist within a unique contextual and compositional meshwork that determines its caseload. The top population density decile of the United States lives within 37 counties, whereas half of the most populous parts of the United States occupy a total of 250 counties out of a total of 3143 counties in the United States. If the 10 measures of patient satisfaction (HCAHPS) scores were abstracted from hospitals and viewed according to county‐level population density (separated into deciles across the United States), a trend would be apparent (Figure 1). Greater population density is associated with lower patient satisfaction in 9 of 10 categories. On the state level, composite scores of overall patient satisfaction (amount of positive scores) of hospitals show a 12% variability and a significant correlation with population density (r=0.479; Figure 2). The lowest overall satisfaction scores are obtained from hospitals located in the population‐dense regions of Washington, DC, New York State, California, Maryland, and New Jersey (ie, 63%65%), and the best scores are from Louisiana, South Dakota, Iowa, Maine, and Vermont (ie, 74%75%). The average patient satisfaction score is 71%2.9%. Lower patient satisfaction scores appear to cluster in population‐dense areas and may be associated with greater heterogeneous patient demographics and economic variability in addition to population density.

Figure 1

Figure 2

These observations are surprising considering that CMS already adjusts HCAHPS scores based on patient‐mix coefficients and mode of collection.[13, 14, 15, 16, 17, 18] Adjustments are updated multiple times per year and account for survey collection either by telephone, email, or paper survey, because the populations that select survey forms will differ. Previous studies have shown that demographic features influence the patient evaluation process. For example, younger and more educated patients were found to provide less positive evaluations of healthcare.[19]

This study examined whether patients perceptions of healthcare (pattern of patient satisfaction) as quantified under the patient experience domain of HVBP were affected and predicted by population density and other demographic factors that are outside the control of individual hospitals. In addition, hospital‐level data (eg, number of hospital beds) and county‐level data such as race, age, gender, overall population, income, time spent commuting to work, primary language, and place of birth were analyzed for correlation with patient satisfaction scores. Our study demonstrates that demographic and hospital‐level data can predict patient satisfaction scores and suggests that CMS may need to modify its adjustment formulas to eliminate bias in HVBP‐based reimbursement.

METHODS

RESULTS

Complete HCAHPS scores were obtained from 3907 hospitals out of a total 4621 hospitals listed by the Hospital Compare website (85%). The majority of hospitals (2884) collected over 300 surveys, fewer hospitals (696) collected 100 to 299 surveys, and fewer still (333) collected <100 surveys. In total, results were available from at least 934,800 individual surveys, by the most conservative estimate. Missing HCAHPS hospital data averaged 13.4 (standard deviation [SD] 12.2) hospitals per state. County‐level data were obtained from all 3144 county or county equivalents across the United States (100%). Multivariate regression modeling across all HCAHPS dimensions found that between 10 and 16 of the 20 predictors (US Census categories) were statistically significant and predictive of individual HCAHPS dimension scores and the aggregate percent positive score as demonstrated in Table 2. For example, county percentage of bachelors degrees positively predicts for positive doctor communication scores, and hospital beds negatively predicts for quiet dimension. The strongest positive and negative predictive variables by model regression coefficients for each HCAHPS dimension are also listed in Table 2.

Table 1 highlights multivariate regression modeling of the composite average positive score, which produced an adjusted R² of 0.222 (P<0.001). All variables were significant and predicted change of the composite HCAHPS except for place of birthforeign born (not listed in the table). Table 1 ranks variables from most positive to most negative predictors.

Other HCAHPS domains demonstrated statistically significant models (P<0.001) and are listed by their coefficients of determination (ie, adjusted R²) (Table 2). The best‐fit dimensions were help (adjusted R²=0.304), quiet (adjusted R²=0.299), doctor communication (adjusted R²=0.298), nurse communication (adjusted R²=0.245), and clean (adjusted R²=0.232). Models that were not as strongly predictive as the composite score included pain (adjusted R²=0.124), overall 9/10 (adjusted R²=0.136), definitely recommend (adjusted R²=0.150), and explained meds (adjusted R²=0.169).

A predictive formula for average positive scores was created by determination of the most predictive partial coefficients and the best‐fit model. Bachelor's degree and white only were the 2 greatest positive predictors, and number of hospital beds and nonEnglish speaking were the 2 greatest negative predictors. The PPSF (predictive formula) was chosen out of various combinations of predictors (Table 1), because its coefficient of determination (adjusted R²=0.155) was closest to the overall model's coefficient of determination (adjusted R²=0.222) without demonstrating colinearity. Possible predictive formulas were based on the predictors standardized and included the following combinations: the 2 greatest overall predictors (adjusted R²=0.051), the 2 greatest negative and positive predictors (adjusted R²=0.098), the 4 greatest overall predictors (adjusted R²=0.117), and the 8 greatest overall predictors (adjusted R²=0.201), which suffered from colinearity (household size plus nonEnglish speaking [Pearson=0.624] and under 18 years old [Pearson=0.708]). None of the correlated independent variables (eg, poverty and median income) were placed in the final model.

The mean WIPSAS scores closely corresponded with the national average of HCAHPS scores (71.6 vs 71.84) but compressed scores into a narrower distribution (SD 5.52 vs 5.92). The greatest positive and negative changes were by 8.51% and 2.25%, respectively. Essentially, a smaller number of hospitals in demographically challenged areas were more significantly impacted by the WIPSAS adjustment than the larger number of hospitals in demographically favorable areas. Large hospitals in demographically diverse counties saw the greatest positive change (e.g., Texas, California, and New York), whereas smaller hospitals in demographically nondiverse areas saw comparatively smaller decrements in the overall WIPSAS scores. The WIPSAS had the most beneficial effect on urban and rural safety‐net hospitals that serve diverse populations including many academic medical centers. This is illustrated by the reranking of the top 10 and bottom 10 hospitals in New York State by the WIPSAS (Table 3). For example, 3 academic medical centers in New York State, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were moved from the 46th, 43rd, and 42nd (out of 167 hospitals) respectively into the top 10 in patient satisfaction utilizing the WIPSAS methodology. Reported patient satisfaction scores, PPSF, WIPPSS, and WIPSAS scores for each hospital in the United States are available online (see Supporting Table S1 in the online version of this article).

DISCUSSION

The HVBP program is an incentive program that is meant to enhance the quality of care. This study illustrates healthcare inequalities in patient satisfaction that are not accounted for by the current CMS adjustments, and shows that education, ethnicity, primary language, and number of hospital beds are predictive of how patients evaluate their care via patient satisfaction scores. Hospitals that treat a disproportionate percentage of nonEnglish speaking, nonwhite, noneducated patients in large facilities are not meeting patient satisfaction standards. This inequity is not ameliorated by the adjustments currently performed by CMS, and has financial consequences for those hospitals that are not meeting national standards in patient satisfaction. These hospitals, which often include academic medical centers in urban areas, may therefore be penalized under the existing HVBP reimbursement models.

Using only 4 demographic and hospital‐specific predictors (ie, hospital beds, percent nonEnglish speaking, percent bachelors degrees, percent white), it is possible to utilize a simple formula to predict patient satisfaction with a significant degree of correlation to the reported scores available through Hospital Compare.

Our initial hypothesis that population density predicted lower patient satisfaction scores was confirmed, but these aforementioned demographic and hospital‐based factors were stronger independent predictors of HCAHPS scores. The WIPSAS is a representation of patient satisfaction and quality‐of‐care delivery across the country that accounts for nonrandom variation in patient satisfaction scores.

For hospitals in New York State, WIPSAS resulted in the placement of 3 urban‐based academic medical centers in the top 10 in patient satisfaction, when previously, based on the raw scores, their rankings were between 42nd and 46th statewide. Prior studies have suggested that large, urban, teaching, and not‐for‐profit hospitals were disadvantaged based on their hospital characteristics and patient features.[10, 11, 12] Under the current CMS reimbursement methodologies, these institutions are more likely to receive financial penalties.[8] The WIPSAS is a simple method to assess hospitals performance in the area of patient satisfaction that accounts for the demographic and hospital‐based factors (eg, number of beds) of the hospital. Its incorporation into CMS reimbursement calculations, or incorporation of a similar adjustment formula, should be strongly considered to account for predictive factors in patient satisfaction that could be addressed to enhance their scores.

Limitations for this study are the approximation of county‐level data for actual individual hospital demographic information and the exclusion of specialty hospitals, such as cancer centers and children's hospitals, in HCAHPS surveys. Repeated multivariate analyses at different time points would also serve to identify how CMS‐specific adjustments are recalibrated over time. Although we have primarily reported on the composite percent positive score as a surrogate for all HCAHPS dimensions, an individual adjustment formula could be generated for each dimension of the patient experience of care domain.

Although patient satisfaction is a component of how quality should be measured, further emphasis needs to be placed on nonrandom patient satisfaction variance so that HVBP can serve as an incentivizing program for at‐risk hospitals. Regional variation in scoring is not altogether accounted for by the current CMS adjustment system. Because patient satisfaction scores are now directly linked to reimbursement, further evaluation is needed to enhance patient satisfaction scoring paradigms to account for demographic and hospital‐specific factors.

Disclosure

Nothing to report.