@article{2373,
  keywords = {Medicare payment, Patient Safety Indicators, hospital-acquired conditions, value-based purchasing},
  author = {Lane Koenig and Samuel A. Soltoff and Berna Demiralp and Akinluwa A. Demehin and Nancy E. Foster and Caroline Rossi Steinberg and Christopher Vaz and Scott Wetzel and Susan Xu},
  title = {Complication Rates, Hospital Size, and Bias in the CMS Hospital-Acquired Condition Reduction Program.},
  abstract = {<p>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.</p>},
  year = {2017},
  journal = {Am J Med Qual},
  volume = {32},
  pages = {611-616},
  month = {12/2017},
  issn = {1555-824X},
  doi = {10.1177/1062860616681840},
  language = {eng},
}