Identifying Poor-Performing Hospitals in the Medicare Hospital-Acquired Condition Reduction Program: An Assessment of Reliability.

Centers for Medicare and Medicaid Services (CMS) estimated that Medicare's Hospital-Acquired Condition Reduction Program (HAC-RP) would reduce hospital payments by $364 million in fiscal year 2016. Although observers have questioned the validity of certain HAC-RP measures, less attention has been paid to the determination of low-performing hospitals (bottom quartile) and the reliability of penalty assignment. This study used publicly available data from CMS's Hospital Compare to simulate the consistency of hospitals' scores and the assignment of penalties under repeated measurement with no change in each hospital's underlying quality. The simulation showed that 64.0% of all hospitals and 40.6% of hospitals subject to payment penalty are statistically significantly different from the penalty threshold at the 95% confidence level. The proportion of hospitals statistically different from the threshold showed significant variation by ownership status, teaching status, bed size, and other factors. The simulation further showed that due only to chance, 18.0% of penalized hospitals would escape penalty on repeated measurement. Policymakers should consider alterations to the HAC-RP to improve its reliability.

Complication Rates, Hospital Size, and Bias in the CMS Hospital-Acquired Condition Reduction Program.

In 2016, Medicare's Hospital-Acquired Condition Reduction Program (HAC-RP) will reduce hospital payments by $364 million. Although observers have questioned the validity of certain HAC-RP measures, less attention has been paid to the determination of low-performing hospitals (bottom quartile) and the assignment of penalties. This study investigated possible bias in the HAC-RP by simulating hospitals' likelihood of being in the worst-performing quartile for 8 patient safety measures, assuming identical expected complication rates across hospitals. Simulated likelihood of being a poor performer varied with hospital size. This relationship depended on the measure's complication rate. For 3 of 8 measures examined, the equal-quality simulation identified poor performers similarly to empirical data (c-statistic approximately 0.7 or higher) and explained most of the variation in empirical performance by size (Efron's R2 > 0.85). The Centers for Medicare & Medicaid Services could address potential bias in the HAC-RP by stratifying by hospital size or using a broader "all-harm" measure.

The Medicare Hospital Readmissions Reduction Program: potential unintended consequences for hospitals serving vulnerable populations.

OBJECTIVE: To explore the impact of the Hospital Readmissions Reduction Program (HRRP) on hospitals serving vulnerable populations. DATA SOURCES/STUDY SETTING: Medicare inpatient claims to calculate condition-specific readmission rates. Medicare cost reports and other sources to determine a hospital's share of duals, profit margin, and characteristics. STUDY DESIGN: Regression analyses and projections were used to estimate risk-adjusted readmission rates and financial penalties under the HRRP. Findings were compared across groups of hospitals, determined based on their share of duals, to assess differential impacts of the HRRP. PRINCIPAL FINDINGS: Both patient dual-eligible status and a hospital's dual-eligible share of Medicare discharges have a positive impact on risk-adjusted hospital readmission rates. Under current Centers for Medicare and Medicaid Service methodology, which does not adjust for socioeconomic status, high-dual hospitals are more likely to have excess readmissions than low-dual hospitals. As a result, HRRP penalties will disproportionately fall on high-dual hospitals, which are more likely to have negative all-payer margins, raising concerns of unintended consequences of the program for vulnerable populations. CONCLUSIONS: Policies to reduce hospital readmissions must balance the need to ensure continued access to quality care for vulnerable populations.