Logistic Efficiency

ORIGINAL RESEARCH

Demographic Factors and Hospital Size Predict Patient Satisfaction Variance—Implications for Hospital Value-Based Purchasing

Daniel C. McFarland, DO1*, Katherine A. Ornstein, PhD2, Randall F. Holcombe, MD1

1Division of Hematology/Oncology, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center, New York, New York; 2Department of Geriatrics and Palliative Medicine, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center, New York, New York.

BACKGROUND: Hospital Value-Based Purchasing (HVBP) incentivizes quality performance-based healthcare by link- ing payments directly to patient satisfaction scores obtained from Hospital Consumer Assessment of Health- care Providers and Systems (HCAHPS) surveys. Lower HCAHPS scores appear to cluster in heterogeneous population-dense areas and could bias Centers for Medi- care & Medicaid Services (CMS) reimbursement.

OBJECTIVE: Assess nonrandom variation in patient satis- faction as determined by HCAHPS.

DESIGN: Multivariate regression modeling was performed for individual dimensions of HCAHPS and aggregate scores. Standardized partial regression coefficients assessed strengths of predictors. Weighted Individual (hos- pital) Patient Satisfaction Adjusted Score (WIPSAS) utilized 4 highly predictive variables, and hospitals were reranked accordingly.

SETTING: A total of 3907 HVBP-participating hospitals.

PATIENTS: There were 934,800 patient surveys by the most conservative estimate.

MEASUREMENTS: A total of 3144 county demographics (US Census) and HCAHPS surveys.

RESULTS: Hospital size and primary language (non–English speaking) most strongly predicted unfavorable HCAHPS scores, whereas education and white ethnicity most strongly predicted favorable HCAHPS scores. The average adjusted patient satisfaction scores calculated by WIPSAS approxi- mated the national average of HCAHPS scores. However, WIPSAS changed hospital rankings by variable amounts depending on the strength of the predictive variables in the hospitals’ locations. Structural and demographic characteris- tics that predict lower scores were accounted for by WIPSAS that also improved rankings of many safety-net hospitals and academic medical centers in diverse areas.

CONCLUSIONS: Demographic and structural factors (eg, hospital beds) predict patient satisfaction scores even after CMS adjustments. CMS should consider WIPSAS or a simi- lar adjustment to account for the severity of patient satisfac- tion inequities that hospitals could strive to correct. Journal of Hospital Medicine 2015;10:503–509. VC 2015 Society of Hospital Medicine

The Affordable Care Act of 2010 mandates that gov- ernment 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 pro- grams have been developed by the Centers for Medi- care & 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 reim- bursement. 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 for- ward.4 The program rewards hospitals for both over- all 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 satisfac- tion 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 dispar- ities in healthcare by penalizing larger hospitals (including academic medical centers, safety-net hospi- tals, and others that disproportionately serve lower socioeconomic communities) and favoring physician- based specialty hospitals.7–9 Therefore, hospitals that serve indigent and elderly populations may be at a dis- advantage.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 mar- gins.11 Higher HVBP scores are associated with for profit status, smaller size, and location in certain areas

*Address for correspondence and reprint requests: Daniel McFarland, DO, Hematology/Oncology, Mount Sinai Medical Center, One Gustave L. Levy Place, Box 1079, New York, NY 10029; Telephone: 212–659-5420; Fax: 212–241-2684; E-mail: [email protected]

Additional Supporting Information may be found in the online version of this article.

Received: November 13, 2014; Revised: March 17, 2015; Accepted: April 3, 2015 2015 Society of Hospital Medicine DOI 10.1002/jhm.2371 Published online in Wiley Online Library (Wileyonlinelibrary.com).

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of the United States.12 Jha et al.6 described Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores’ regional geographic vari- ability, but concluded that poor satisfaction was due to poor quality.

The Patient Experience of Care domain quantifies patient satisfaction using the validated HCAHPS sur- vey, 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 popu- lation 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 (sepa- rated into deciles across the United States), a trend would be apparent (Figure 1). Greater population den- sity 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 sig-

nificant correlation with population density (r 5 20.479; 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% 6 2.9%. Lower patient satisfaction scores appear to cluster in population-dense areas and may be asso- ciated with greater heterogeneous patient demo- graphics and economic variability in addition to population density.

These observations are surprising considering that CMS already adjusts HCAHPS scores based on patient-mix coefficients and mode of collection.13–18

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 quan- tified 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

FIG. 1. Overall patient satisfaction by population density decile. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS)

scores are segregated by population density deciles (representing 33 million

people each). Population density increases along the grey scale. The com-

posite score and 9 out of 10 HCAHPS dimensions demonstrate lower patient

satisfaction as population density increases (darker shade). Abbreviations:

Doc, doctor; Def Rec, definitely recommend.

FIG. 2. Averaged Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores by state correlated with state population

(Pop) density. Bivariate correlation of composite HCAHPS scores predicted

by state population density without District of Columbia, r 5 20.479,

P < 0.001 (2-tailed). This observed correlation informed the hypothesis that

population density could predict for lower patient satisfaction via HCAHPS

scores.

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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 Data Collection

Publically available data were obtained from Hospital Compare,20 American Hospital Directory,21 and the US Census Bureau22 websites. Twenty relevant US Census data categories were selected by their rele- vance for this study out of the 50 publically reported US Census categories, and included the following: county population, county population density, percent of population change over 1 year, poverty level (per- cent), income level per capita, median household income, average household size, travel time to work, percentage of high school or college graduates, non- English primary language spoken at home, percentage of residents born outside of the United States, popula- tion percent in same residence for over 1 year, gender, race (white alone, white alone (not Hispanic or Lat- ino), black or African American alone), population over 65 years old, and population under 18 years old.

HCAHPS Development

The HCAHPS survey is 32 questions in length, com- prised of 10 evaluative dimensions. All short-term, acute care, nonspecialty hospitals are invited to partic- ipate in the HCAHPS survey.

Data Analysis

Statistical analyses used the Statistical Package for Social Sciences version 16.0 for Windows (SPSS Inc., Chicago, IL). Data were checked for statistical assumptions, including normality, linearity of relation- ships, and full range of scores. Categories in both the Hospital Compare (HCAHPS) and US Census datasets were analyzed to assess their distribution curves. The category of population densities (per county) was con- verted to a logarithmic scale to account for a skewed distribution and long tail in the area of low popula- tion density. Data were subsequently merged into an Excel (Microsoft, Redmond, WA) spreadsheet using the VLookup function such that relevant 2010 census county data were added to each hospital’s Hospital Compare data. Linear regression modeling was per- formed. Bivariate analysis was conducted (ENTER method) to determine the significant US Census data predictors for each of the 10 Hospital Compare dimensions including the composite overall satisfac- tion score. Significant predictors were then analyzed in a multivariate model (BACKWORDS method) for each Hospital Compare dimension and the composite

average positive score. Models were assessed by deter- minates of correlation (adjusted R2) to assess for goodness of fit. Statistically significant predictor varia- bles for overall patient satisfaction scores were then ranked according to their partial regression coeffi- cients (standardized b).

A patient satisfaction predictive model was sought based upon significant predictors of aggregate percent positive HCAHPS scores. Various predictor combina- tions were formed based on their partial coefficients (ie, standardized b coefficients); combinations were assessed based on their R2 values and assessed for col- inearity. Combinations of partial coefficients included the 2, 4, and 8 most predictive variables as well the 2 most positive and negative predictors. They were then incorporated into a multivariate analysis model (FOR- WARD method) and assessed based on their adjusted R2 values. A 4-variable combination (the 2 most pre- dictive positive partial coefficients plus the 2 most pre- dictive negative partial coefficients) was selected as a predictive model, and a formula predictive of the composite overall satisfaction score was generated. This formula (predicted patient satisfaction formula [PPSF]) predicts hospital patient satisfaction HCAHPS scores based on the 4 predictive variables for particu- lar county and hospital characteristics.

PPSF 5 KMV 1 BHB HBð Þ 1 BNE NEð Þ 1 BE Eð Þ 1 BW Wð Þ

where KMV 5 coefficient constant (70.9), B 5 un- standardized b coefficient (see Table 1 for values), HB 5 number of hospital beds, NE 5 proportion of non-English speakers, E 5 education (proportion with bachelor’s degree), and W 5 proportion identified as white race only.

The PPSF was then modified by weighting with the partial coefficient (b) to remove the bias in patient sat- isfaction generated by demographic and structural fac- tors over which individual hospitals have limited or no control. This formula generated a Weighted Indi- vidual (hospital) Predicted Patient Satisfaction Score (WIPPSS). Application of this formula narrowed the predicted distribution of patient satisfaction for all hospitals across the country.

WIPPSS 5 KMV 1 BHB HBð Þ 12bHBð Þ 1 BNE NEð Þ 12bNEð Þ 1 BE Eð Þ 12bEð Þ 1 BW Wð Þ 12bWð Þ

where b 5 standardized b coefficient (see Table 1 for values).

To create an adjusted score with direct relevance to the reported patient satisfaction scores, the reported scores were multiplied by an adjustment factor that defines the difference between individual hospital- weighted scores and the national mean HCAHPS score across the United States. This formula, the Weighted Individual (hospital) Patient Satisfaction Adjustment Score (WIPSAS), represents a patient

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satisfaction score adjusted for demographic and struc- tural factors that can be utilized for interhospital com- parisons across all areas of the country.

WIPSAS 5 PSrep 1 1 PSUSA2WIPPSSXð Þ=100½ �

where PSrep 5 patient satisfaction reported score, PSUSA 5 mean reported score for United States (71.84), and WIPPSSX 5 WIPPSS for individual hospital.

Application of Data Analysis

PPSF, WIPPSS, and WIPSAS were calculated for all HCAHPS-participating hospitals and compared with averaged raw HCAHPS scores across the United States. WIPSAS and raw scores were specifically analyzed for New York State to demonstrate exactly how adjustments would change state-level rankings.

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 devia- tion [SD] 12.2) hospitals per state. County-level data were obtained from all 3144 county or county equiva- lents 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 per- centage 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 pro- duced an adjusted R2 of 0.222 (P < 0.001). All varia- bles were significant and predicted change of the composite HCAHPS except for place of birth–foreign

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 5 0.304), quiet (adjusted R2 5 0.299), doctor com- munication (adjusted R2 5 0.298), nurse communica-

tion (adjusted R2 5 0.245), and clean (adjusted R2 5 0.232). Models that were not as strongly predic- tive as the composite score included pain (adjusted

R2 5 0.124), overall 9/10 (adjusted R2 5 0.136), defi-

nitely recommend (adjusted R2 5 0.150), and explained meds (adjusted R2 5 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 non–English speak- ing were the 2 greatest negative predictors. The PPSF (predictive formula) was chosen out of various combi- nations of predictors (Table 1), because its coefficient of determination (adjusted R2 5 0.155) was closest to

TABLE 1. Multivariate Regression of Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) Average Positive Score by County and Hospital Demographics

B SE b t P

Educational attainment–bachelor’s degree 0.157 0.018 0.27 8.612 <0.001 White alone percent 2012 0.09 0.012 0.235 7.587 <0.001 Resident population percent under 18 years 0.404 0.0444 0.209 9.085 <0.001 Black or African American alone percent 2012 0.083 0.014 0.191 5.936 <0.001 Median household income 2007–2011 20.00003 0.00 20.062 22.027 0.043 Population density (log) 2010 20.277 0.083 20.087 23.3333 0.001 Average travel time to work 20.107 0.024 20.088 24.366 <0.001 Educational attainment–high school 20.082 0.026 20.088 23.147 0.002 Average household size 22.58 0.727 20.107 23.55 <0.001 Total females percent 2012 20.423 0.067 20.107 26.296 <0.001 Percent non–English speaking at home 2007–2011 20.052 0.018 20.14 22.929 0.003 No. of hospital beds 20.006 0.00 20.213 212.901 <0.001 Adjusted R2 0.222

NOTE: A multivariate linear regression model of statistically significant dimensions of patient satisfaction as determined by Hospital Consumer Assessment of Healthcare Providers and Systems scores is provided. The dependent variable is the composite of average patient satisfaction scores by hospital (3192 hospitals). Predictors (independent variables) were collected from US Census data for counties or county equivalents. All of the listed predictors (first column) are statistically significant. They are placed in order of partial regression coefficient contribution to the model from most positive to most negative contribution. Adjusted R2 (last row) is used to signify the goodness of fit. Abbreviations: b, standardized b (partial coefficient); B, unstandardized b coefficient; P, statistical significance; SE, standard error; t, t statistic.

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the overall model’s coefficient of determination (adjusted R2 5 0.222) without demonstrating colinear- ity. Possible predictive formulas were based on the predictors’ standardized b and included the following combinations: the 2 greatest overall predictors (adjusted R2 5 0.051), the 2 greatest negative and pos- itive predictors (adjusted R2 5 0.098), the 4 greatest overall predictors (adjusted R2 5 0.117), and the 8 greatest overall predictors (adjusted R2 5 0.201), which suffered from colinearity (household size plus non–English speaking [Pearson 5 0.624] and under 18 years old [Pearson 5 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 distri- bution (SD 5.52 vs 5.92). The greatest positive and negative changes were by 8.51% and 2.25%, respec- tively. Essentially, a smaller number of hospitals in demographically challenged areas were more signifi- cantly impacted by the WIPSAS adjustment than the larger number of hospitals in demographically favor- able 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 compara- tively 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 WIP- SAS (Table 3). For example, 3 academic medical

TABLE 3. Top Ten Highest-Ranked Hospitals in New York State by HCAHPS Scores Compared to WIPSAS

Ten Highest Ranked New York State

Hospitals by HCAHPS

Ten Highest Ranked New York

State Hospitals After WIPSAS

1. River Hospital, Inc. 1. River Hospital, Inc. 2. Westfield Memorial Hospital, Inc. 2. Westfield Memorial Hospital, Inc. 3. Clifton Fine Hospital 3. Clifton Fine Hospital 4. Hospital For Special Surgery 4. Hospital For Special Surgery 5. Delaware Valley Hospital, Inc. 5. New York–Presbyterian Hospital 6. Putnam Hospital Center 6. Delaware Valley Hospital, Inc. 7. Margaretville Memorial Hospital 7. Montefiore Medical Center 8. Community Memorial Hospital, Inc. 8. St. Francis Hospital, Roslyn 9. Lewis County General Hospital 9. Putnam Hospital Center 10. St. Francis Hospital, Roslyn 10. Mount Sinai Hospital

NOTE: Top 10 highest-ranked hospitals in New York State by overall patient satisfaction out of 167 evalu- able hospitals are shown. The left column represents the current top 10 hospitals in 2013 by HCAHPS over- all patient satisfaction scores, and the right column represents the top 10 hospitals after the WIPSAS adjustment. The 4 factors used to create the WIPSAS adjustment were the 2 most positive partial regression coefficients (education–bachelor’s degree, white alone percent 2012) and the 2 most negative partial regres- sion coefficients (number of hospital beds, non–English speaking at home). Three urban academic medical centers, Montefiore Medical Center, New York Presbyterian Hospital, and Mount Sinai Hospital, were reranked from the 46th, 43rd, and 42nd respectively into the top 10. Abbreviations: HCAHPS, Hospital Con- sumer Assessment of Healthcare Providers and Systems; WIPSAS, Weighted Individual (hospital) Patient Satisfaction Adjustment Score.

TABLE 2. Multivariate Regression of Hospital Consumer Assessment of Healthcare Providers and Systems by County and Hospital Demographics

Average

Positive

Scores

Nurse

Communication

Doctor

Communication Help Pain

Explain

Meds Clean Quiet

Discharge

Explain

Recommend

9/10

Definitely

Recommend

Educational–bachelor’s 0.27 0.19 0.45 0.10 0.10 0.05 0.08 0.33 0.15 0.27 0.416 Hospital beds 2 0.21 20.16 20.19 20.26 20.16 20.17 2 0.27 20.26 20.06 20.11 — Population density 2010 20.09 20.07 20.28 20.20 20.08 20.23 2 0.14 2 0.19 0.22 0.07 * White alone percent 0.24 0.25 0.09 0.16 0.23 0.07 0.16 — 0.17 0.31 0.317 Total females percent 20.11 20.05 20.06 20.07 20.06 20.03 2 0.05 2 0.09 20.12 20.09 — African American alone 0.19 0.19 — 0.09 0.23 0.09 0.07 0.34 * 0.09 0.084 Average travel time to work 20.09 20.10 * 20.09 20.06 20.04 2 0.08 * 20.12 20.17 20.16 Foreign-born percent * 20.16 0.14 20.06 20.12 20.08 0.06 2 0.13 20.18 * * Average household size 20.11 20.05 20.15 20.07 * 20.07 * 2 0.01 * 20.07 0.076 Non–English speaking 20.14 20.12 20.50 20.07 * * * * * 20.34 20.28 Education–high school 20.09 20.09 20.40 * — — — 2 0.27 0.06 20.08 * Household income 20.06 * 20.35 20.08 * * 2 0.16 2 0.41 — — 20.265 Population 65 years and over * 20.14 20.14 20.12 * 20.11 2 0.15 — — * 20.10 White, not Hispanic/Latino * * 20.20 * * * 0.09 0.13 0.09 20.22 20.25 Population under 18 0.21 — 0.15 — 0.08 — — — 0.11 0.20 — Population (county) * 20.06 20.08 * 20.03 20.05 * * 20.06 * * All ages in poverty — — 20.24 — — — 2 0.10 2 0.22 20.08 * 20.281 1 year at same residence * 0.13 0.12 0.11 — — 0.10 * 20.04 * * Per capita income * 20.07 * * * * * 0.09 — — * Population percent change * * * * * * 2 0.05 — — * * Adjusted R2 0.22 0.25 0.30 0.30 0.12 0.17 0.23 0.30 0.19 0.14 0.15

NOTE: Linear regression modeling results of 10 dimensions of patient satisfaction (ie, Hospital Consumer Assessment of Healthcare Providers and Systems [HCAHPS]) and Average Positive Scores (top row) by county demo- graphics and hospital size (left column) are shown. Adjusted R2 (last row) is used to signify the goodness of fit. All models are statistically significant with P 5 <0.001. Partial regression coefficients (b) are used to positively or neg- atively assess contribution to the individual models (ie, each column). The dash (—) indicates nonsignificance and the asterisk (*) indicates a value that was statistically significant in univariate analysis but not in multivariate analysis. Independent variables (first column) are ordered from top to bottom by the number of HCAHPS dimensions that each contributes to HCAHPS predictive scoring.

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centers in New York State, Montefiore Medical Cen- ter, 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 meth- odology. 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 illus- trates healthcare inequalities in patient satisfaction that are not accounted for by the current CMS adjust- ments, and shows that education, ethnicity, primary language, and number of hospital beds are predictive of how patients evaluate their care via patient satisfac- tion scores. Hospitals that treat a disproportionate percentage of non–English speaking, nonwhite, none- ducated 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 reim- bursement models.

Using only 4 demographic and hospital-specific pre- dictors (ie, hospital beds, percent non–English 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 pre- dicted 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 pre- viously, 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.10–12

Under the current CMS reimbursement methodolo- gies, these institutions are more likely to receive finan- cial 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 hos- pital. Its incorporation into CMS reimbursement cal-

culations, 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 demo- graphic information and the exclusion of specialty hos- pitals, such as cancer centers and children’s hospitals, in HCAHPS surveys. Repeated multivariate analyses at dif- ferent 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.

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