ICES: Small: Collaborative Research: Data-driven mechanisms in healthcare

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The main objective of the project is to develop and study new incentive and reimbursement mechanisms within the context of the delivery of medical care. The project takes a game-theoretic perspective, with the main players being the government, health plans, healthcare providers, and patients. Some of the design goals of the mechanisms are to motivate the parties involved to improve healthcare quality while controlling costs. Among others, the study will try to extend the algorithmic game theory framework to address the unique challenges that occur in the context of healthcare. A key goal of the study is understanding the extent to which utilizing electronically available medical records may enable new mechanisms in the context of healthcare. In particular, the project studies potential advantages of high quality risk-adjustment schemes that are empowered by the application of machine learning to medical data.In recent years, much of the focus in the US public policy debate has been given to problems surrounding providing and sustaining healthcare services. One of the key problems the US healthcare system faces is that of aligning incentives among the various stakeholders - the government, health plans, care providers, and patients - to ensure favorable outcomes and efficient resource utilization. The study will use tools from algorithms and game theory to develop new alignment strategies. In particular, the study will look at ways in which the collection and data-mining of electronic health records may be used to provide better incentives for the stakeholders, and lead to better overall care at lower cost.