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

ICES：小型：协作研究：医疗保健中的数据驱动机制

• 批准号: 1215990
• 负责人: Mark Braverman
• 金额: $ 18.33万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1215990&HistoricalAwards=false
• 关键词: ICES; Small; Collaborative; Research; Data

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.

该项目的主要目的是在提供医疗服务的背景下开发和研究新的激励和报销机制。该项目具有游戏理论的观点，主要参与者是政府，健康计划，医疗保健提供者和患者。该机制的一些设计目标是激励有关各方在控制成本的同时提高医疗保健质量。除其他外，该研究将尝试扩展算法游戏理论框架，以应对医疗保健背景下发生的独特挑战。该研究的一个关键目标是了解利用电子可用的医疗记录的程度可以在医疗保健背景下实现新机制。特别是，项目研究的高质量风险调整方案的潜在优势是将机器学习应用于医学数据所赋予的能力。近年来，美国公共政策辩论的重点是提供和维持医疗服务的问题。美国医疗保健系统面临的关键问题之一是使各种利益相关者（政府，健康计划，护理提供者和患者）之间的激励措施保持一致，以确保有利的结果和有效的资源利用。该研究将使用算法和游戏理论的工具来制定新的对齐策略。特别是，该研究将研究电子健康记录收集和数据挖掘的方式，以便为利益相关者提供更好的激励措施，并以较低的成本提供更好的整体护理。