EAGER: Engineering Incentives for Health Care Systems

Texas A&M Engineering Experiment Station, College Station TX

Investigators

Abstract

This grant provides funding for the study of basic research issues related to engineering incentives in health care systems, such as design of appropriate incentives, estimation of system-wide performance for a set of incentives and predicting participant response to given incentives. We specifically focus on how incentives can be used to reduce costs by aligning objective functions of non-cooperative decision makers. New methods will be developed in order to compute equilibria in large distributed systems. This project will develop novel distributed, iterative methods that will allow us to efficiently decompose the overall problem into a series of sequential bilateral equilibria for each pair of connected players and then iteratively refine the local equilibria until a fixed point for the entire network is attained. If successful, the results of this research will enhance the understanding of types of incentives, how to model distributed systems with incentives, and how incentives should be used for large systems of strategic decision makers. This work will lay the foundation for a formal approach to engineering incentives in complex, distributed systems, and define a research agenda to study incentives and policy implications in health care systems from a design and engineering perspective. New methods developed in this project will further understanding of iterative algorithms used to compute equilibria in complex networks of decision makers. The primary societal impacts of this research will be to allow health care policy makers to estimate overall costs and impacts of incentives in health care systems, and eventually, be able to devise intervention strategies that will lead to desired provisioning of health care. The proposed work will also contribute multidisciplinary education, and promote the ability to combine knowledge in technically diverse fields (distributed systems, economics, optimization, health policy) and apply this knowledge in the context of understanding and managing health care systems.