Demographic factors and hospital size predict patient satisfaction variance--implications for hospital value-based...

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

Assess nonrandom variation in patient satisfaction as determined by HCAHPS.

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

A total of 3907 HVBP-participating hospitals.

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

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

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 approximated 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 characteristics 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.

Demographic and structural factors (eg, hospital beds) predict patient satisfaction scores even after CMS adjustments. CMS should consider WIPSAS or a similar adjustment to account for the severity of patient satisfaction inequities that hospitals could strive to correct.