利用数据来支持医疗公平在一个集成的交付和融资系统
Jane Kogan1, Joan Eichner2, Hyagriv Simhan3,4
1UPMC Insurance Services Division and UPMC Center for High-Value Health Care Pittsburgh Pennsylvania USA.
学习健康系统 (LHS) 通过整合健康的社会决定因素 (SDOH) 数据,可以促进健康公平. 这涉及跨部门的合作和使用患者报告的SDOH将个人与基本资源联系起来,改善护理和结果.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 卫生公平研究 卫生公平研究
- 在医疗保健中的数据整合.
背景情况:
- 学习健康系统 (LHS) 需要综合数据,包括健康的社会决定因素 (SDOH),以推动医疗保健转型和促进健康公平.
- 有效的医疗保健需要采用整体方法,以满足所有患者的不同需求.
研究的目的:
- 描述一个集成的交付和融资系统如何利用其学习生态系统来增强健康公平.
- 证明整合医疗保健和人力服务数据的影响,并收集患者报告的SDOH.
主要方法:
- 一个跨部门的倡议整合了医疗保健和人力服务数据,以更好地满足客户的综合需求.
- 一个系统层面的倡议专注于收集和利用患者报告的SDOH,以将患者与必要的资源联系起来.
主要成果:
- 这些倡议加强了卫生系统管理各种患者需求,减少健康差异和改善健康结果的能力.
- 数据的整合确定了281,000名共享服务客户,并改善了100名成年医疗补助/特殊需要计划会员的护理管理.
- 在一年内,9173名患者接受了查,超过700人获得了社会服务和支持.
结论:
- 学习健康系统有机会增强和维持创新的数据实践,以促进健康公平.
- 持续学习和数据整合使LHS能够加速医疗保健转型并实现更大的健康公平.
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