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Winning at All Costs: Analysis of Inflation in Nursing Homes’ Rating System

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  • Xu Han
  • Niam Yaraghi
  • Ram Gopal

Abstract

The Nursing Home Compare system administrated by the Centers for Medicare & Medicaid Services (CMS) is widely used by patients, medical providers and payers. We argue that the rating system is prone to inflation in self†reported measures, which leads to biased and misleading ratings. We use the CMS rating data over 2009–2013 and the corresponding financial data reported by Office of Statewide Health Planning and Development and patients’ complaints data reported by California Department of Public Health for 1219 nursing homes in California to empirically examine the key factors affecting the star rating of a nursing home. We find a significant association between the changes in a nursing home's star rating and its profits, which points to a financial incentive for nursing homes to improve the ratings. We then demonstrate that this association does not always lead to legitimate efforts to improve service quality, but instead can induce inflation in self†reporting in the rating procedure. A prediction model is then developed to evaluate the extensiveness of inflation among the suspect population based on which 6% to 8.5% of the nursing homes are identified as likely inflators. We also summarize the key characteristics of likely inflators, which can be useful for future audit.

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  • Xu Han & Niam Yaraghi & Ram Gopal, 2018. "Winning at All Costs: Analysis of Inflation in Nursing Homes’ Rating System," Production and Operations Management, Production and Operations Management Society, vol. 27(2), pages 215-233, February.
  • Handle: RePEc:bla:popmgt:v:27:y:2018:i:2:p:215-233
    DOI: 10.1111/poms.12804
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    Cited by:

    1. Ya‐Tang Chuang & Manaf Zargoush & Somayeh Ghazalbash & Saied Samiedaluie & Kerry Kuluski & Sara Guilcher, 2023. "From prediction to decision: Optimizing long‐term care placements among older delayed discharge patients," Production and Operations Management, Production and Operations Management Society, vol. 32(4), pages 1041-1058, April.
    2. Marios Kokkodis & Theodoros Lappas & Gerald C. Kane, 2022. "Optional purchase verification in e‐commerce platforms: More representative product ratings and higher quality reviews," Production and Operations Management, Production and Operations Management Society, vol. 31(7), pages 2943-2961, July.
    3. Tomer Geva & Maytal Saar‐Tsechansky, 2021. "Who Is a Better Decision Maker? Data‐Driven Expert Ranking Under Unobserved Quality," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 127-144, January.

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