Valuing Improvement in Value-Based Purchasing
ABSTRACT Medicare will soon implement hospital value-based purchasing (VBP) using a scoring system that rewards both achievement (absolute performance) and improvement (performance increase over time). However, improvement is defined so as to give less credit to initial low performers than initial high performers. Because initial low performers are disproportionately hospitals in socioeconomically disadvantaged areas, these institutions stand to lose under Medicare's VBP proposal.
We developed an alternative improvement scale and applied it to hospital performance throughout the United States. By using 2005 to 2008 Medicare process measures for acute myocardial infarction (AMI) and heart failure (HF), we calculated hospital scores using Medicare's proposal and our alternative. Hospital performance scores were compared across 5 locational dimensions of socioeconomic disadvantage: poverty, unemployment, physician shortage, and high school and college graduation rates. Medicare's proposed scoring system yielded higher overall scores for the most locationally advantaged hospitals for 4 of 5 dimensions in AMI and 2 of 5 dimensions for HF. By using our alternative, differences in overall scores between hospitals in the most and least advantaged areas were attenuated, with locationally advantaged hospitals having higher overall scores for 3 of 5 dimensions for AMI and 1 of 5 dimensions for HF.
Using an alternative VBP formula that reflects the principle of "equal credit for equal improvement" resulted in a more equitable distribution of overall payment scores, which could allow hospitals in both socioeconomically advantaged and disadvantaged areas to succeed under VBP.
- Circulation Cardiovascular Quality and Outcomes 03/2012; 5(2):148-9. DOI:10.1161/CIRCOUTCOMES.112.965178 · 5.04 Impact Factor
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ABSTRACT: The Medicare and Premier Inc. Hospital Quality Incentive Demonstration (HQID), a hospital-based pay-for-performance program, changed its incentive design from one rewarding only high performance (Phase 1) to another rewarding high performance, moderate performance, and improvement (Phase 2). We tested whether this design change reduced the gap in incentive payments among hospitals treating patients across the gradient of socioeconomic disadvantage. To estimate incentive payments in both phases, we used data from the Premier Inc. website and from Medicare Provider Analysis and Review files. We used data from the American Hospital Association Annual Survey and Centers for Medicare and Medicaid Services Impact File to identify hospital characteristics. Hospitals were divided into quartiles based on their Disproportionate Share Index (DSH), from lowest disadvantage (Quartile 1) to highest disadvantage (Quartile 4). In both phases of the HQID, we tested for differences across the DSH quartiles for three outcomes: (1) receipt of any incentive payments; (2) total incentive payments; and (3) incentive payments per discharge. For each of the study outcomes, we performed a hospital-level difference-in-differences analysis to test whether the gap between Quartile 1 and the other quartiles decreased from Phase 1 to Phase 2. In Phase 1, there were significant gaps across the DSH quartiles for the receipt of any payment and for payment per discharge. In Phase 2, the gap was not significant for the receipt of any payment, but it remained significant for payment per discharge. For the receipt of any incentive payment, difference-in-difference estimates showed significant reductions in the gap between Quartile 1 and the other quartiles (Quartile 2, 17.5 percentage points [p < .05]; Quartile 3, 18.1 percentage points [p < .01]; Quartile 4, 28.3 percentage points [p < .01]). For payments per discharge, the gap was also significantly reduced between Quartile 1 and the other quartiles (Quartile 2, $14.92 per discharge [p < .10]; Quartile 3, $17.34 per discharge [p < .05]; Quartile 4, $21.31 per discharge [p < .01]). There were no significant reductions in the gap for total payments. The design change in the HQID reduced the disparity in the receipt of any incentive payment and for incentive payments per discharge between hospitals caring for the most and least socioeconomically disadvantaged patient populations.Health Services Research 03/2012; 47(4):1418-36. DOI:10.1111/j.1475-6773.2012.01393.x · 2.49 Impact Factor
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ABSTRACT: BACKGROUND: -Heart failure (HF) readmission rates are primarily derived from Medicare enrollees. Given increasing public scrutiny of HF readmissions, understanding the rate and predictors in populations covered by other payers is also important, particularly among patients with systolic dysfunction, for whom most HF-specific therapies are targeted. METHODS AND RESULTS: -MarketScan(®) Commercial and Medicaid Administrative Claims Databases were used to identify all first hospitalizations with an ICD-9 discharge diagnosis code for HF (primary position) and systolic HF (any position) between 1/1/2005 and 6/30/2008. Among 4,584 unique systolic HF index admissions (mean age 55 years), 30-day crude readmission rates were higher for Medicaid than commercially insured patients: all-cause 17.4% v. 11.8%; HF-related 6.7% v. 4.0%, respectively. In unadjusted analysis, higher comorbidity and prior healthcare utilization predicted readmission; age, gender, and plan type did not. After adjustment for case mix, the odds of all-cause and HF-related readmission were 32% and 68% higher, respectively, among Medicaid than commercially insured patients (p <0.02 for both). No significant differences in readmission rates were seen for managed care versus fee-for-service or capitated versus non-capitated plan types. CONCLUSIONS: -Compared to commonly cited Medicare HF readmission rates of 20-25%, Medicaid patients with systolic HF had lower 30-day readmission rates, and commercially insured patients had even lower rates. Even after adjustment for case mix, Medicaid patients were more likely to be readmitted than commercially insured patients, suggesting that more attention should be focused on readmissions among socio-economically disadvantaged populations.Circulation Heart Failure 10/2012; 5(6). DOI:10.1161/CIRCHEARTFAILURE.112.967356 · 5.95 Impact Factor