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

Abstract

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 R > 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.

Patient Co-Morbidity and Functional Status Influence the Occurrence of Hospital Acquired Conditions More Strongly than Hospital Factors

“…19 In addition, several studies documented inherent bias in the CMS Hospital-Acquired Condition Reduction Program (HACRP), reporting different HACRP scores based solely on hospital size or use of surveillance (e.g.,DVT surveillance), in situations with identical complication rates. 20,21 Finally, and ironically, Rajaram et al reported that the CMS non-payment policy, as currently designed, actually penalized the highest-performing hospitals; those that were accredited by the Joint Commission, offered advanced services, were major teaching institutions, and had better performance on process and outcome measures. 22 These authors recommended reevaluation and reform of the CMS policy.…”

“…The problems with Medicare non-payment policies become even more evident when their influence on the funding of safety-net hospitals is examined. [23][24][25][26] Several studies have examined their impact on safety-net hospitals and reported that these crucial hospitals are not only more likely to be penalized, but they also incurred larger payment penalties, despite having comparable mortality rates. [23][24][25][26] Penalties on these hospitals are more serious since they are likely to run on a tight budget, have less resources for resilience, and provide care for an underserved patient group.…”

The Impact of Hospital Size on National Trends and Outcomes Following Open Esophagectomy

“…Understanding the financial implications of hospital size is essential is important in the context of Medicare reimbursements because patient outcomes (especially mortality and readmissions) are closely scrutinized and penalized [19,20]. Thus, there may be an impetus for ‘bad practices’ in order to avoid financial penalties.…”

Random variation drives a critical bias in the comparison of healthcare-associated infections

“…These include risk adjustment, surveillance bias, preventability, measure validity, and biases against high-volume hospitals. [4][5][6][7][8][9][10] HAI measurements are based on the standardized infection ratio (SIR), that is, the ratio of reported infections to those predicted by risk-adjusted models of the National Healthcare Safety Network (NHSN). 11 To avoid imprecise calculations, SIRs are not reported for hospitals with <1 predicted infection.…”