Date of Completion
Rating Inflation; IoT; Healthcare
Field of Study
Doctor of Philosophy
The Nursing Home Compare System supported by the Centers for Medicare & Medicaid Services (CMS) is being widely used by patients, medical providers and payers. However, evidence suggests that the rating system is prone to self-reporting inflation, leading to biased and misleading ratings. This dissertation consists of three essays analyzing a series of issues that arise in this rating system, including inflation detection, performance evaluation, audit design, and technology adoption.
In the first essay, we use data over 2009-2013 for 1219 California nursing homes to empirically examine the key factors affecting a nursing home’s rating. We find a significant association between a nursing home’s rating change and its profits, and then demonstrate this association does not always lead to legitimate efforts to improve service quality, but can induce self-reporting inflation. A prediction model is then developed to evaluate the extensiveness of inflation based on which 6 to 8.5% of the nursing homes are identified as likely inflators.
Given limited CMS resources, it is important to optimize the inspection process and develop an effective audit process to control inflation. In the second essay, we first formulate the inspection problem by using an innovative graph-based method, and solve the problem based on CMS data. The results support CMS’s current practice in term of minimizing inflation detection difficulty, and suggest an audit system. We then conduct a detailed simulation study on the optimal audit parameter settings. Our result suggests a moderate audit policy to balance the tradeoff between audit net budget and efficiency.
IoT technologies enable automatic data collection, which can release nursing homes from self-reporting burden and reduce the possibility of misreporting. However, IoT technologies can be costly, and CMS may consider subsidizing IoT adoption to control inflation. In the third essay, we develop a two-level game theoretical framework to study how IoT adoption can affect nursing homes’ operational decisions, and how CMS should subsidize IoT adoption. We analyze reactions of honest and inflating nursing homes to IoT adoption, and analyze how CMS can control IoT adoption by auditing and subsidization. We also obtain insights on budget allocation between subsidization and auditing.
Han, Xu, "Rating Game – Inflation Detection, Audit Design, and Technology Adoption on the Nursing Home Compare System" (2017). Doctoral Dissertations. 1605.
Available for download on Wednesday, January 31, 2018