智能社会参与支持系统的框架:在多个层面上识别和解决挑战,以减少健康差异.
Tera L Reynolds1, Hala Algrain1, Lorena de Leon2
1University of Maryland, Baltimore County, Baltimore, MD, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
管理护理组织 (MCO) 正在使用社会护理导航员 (SCN) 来解决社会需求. 使用机器学习的智能支持系统框架可以帮助SCN优先考虑减少健康差异的努力.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 健康的社会决定因素
- 健康差距 研究 研究 研究 研究
背景情况:
- 管理护理组织 (MCO) 越来越多地认识到健康对结果和医疗利用的影响的社会驱动因素.
- 微型企业雇佣社会护理导航员 (SCN) 并对社会需求 (食品,住房,交通) 进行选.
- 对于有社会需求的个人来说,在完成查和接受护理方面存在障碍,导致高SCN病例和潜在的最需要的个人未被确定.
研究的目的:
- 开发一个智能社会参与支持系统的框架.
- 解决优先考虑社会护理导航员对具有最高社会需求的个人所做的努力所面临的挑战.
- 在MCO背景下,减少社会驱动的健康结果中的健康差异.
主要方法:
- 开发了一个智能社会参与支持系统的框架.
- 建立在国家少数民族健康研究所的医疗保健系统领域和健康差异的健康差异研究框架之上.
- 综合学习健康系统,社区参与的方法和机器学习.
主要成果:
- 该框架旨在创建一个可适应,多层次的系统.
- 该系统旨在产生许多积极的结果,包括减少健康差异.
- 讨论了框架在不同MCO环境中的应用和适应.
结论:
- 智能社会参与支持系统框架可以提高社会护理导航的有效性.
- 通过这样一个系统优先考虑SCN的努力,可以改善最有社会需求的个体的结果.
- 这种方法有望减少由健康的社会决定因素驱动的健康差异.
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