在机器学习驱动的决策支持中整合健康的社会决定因素,用于糖尿病病例管理:顺序混合方法研究协议
Seung-Yup Lee1, Leslie W Hayes2, Bunyamin Ozaydin1
1School of Health Professions, University of Alabama at Birmingham, Birmingham, AL, United States.
JMIR research protocols
|September 25, 2024
概括
这项研究开发了一个基于数据的系统,使用临床数据和健康的社会决定因素 (SDoH) 来优先考虑糖尿病患者的病例管理,旨在减少差异并优化资源使用.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 公共卫生 公共卫生
背景情况:
- 临床因素和健康的社会决定因素 (SDoH) 在病例管理转诊中的整合可以优化资源分配,减少糖尿病患者的健康结果差异.
- 深南地区面临着与糖尿病相关的重大健康差异,特别是在住院和急诊室 (ED) 访问方面,由SDoH加剧.
研究的目的:
- 开发一个数据驱动的决策支持系统,集成临床因素和SDoH,以优先考虑患者的病例管理服务.
- 设计预测验证和实施前评估策略,使用混合方法方法来指导系统实施.
主要方法:
- 开发一种可解释的人工智能模型,使用电子健康记录数据 (人口统计,SDoH,并发症,住院因素,实验室结果,药物) 来预测糖尿病患者住院后的ED使用情况.
- 使用混合方法方法来验证预测结果,并制定由病例经理,临床医生和质量/患者安全专家提供信息的实施策略.
- 研究环境:美国深南的一个大型高等保健学术医疗中心.
主要成果:
- 数据从174,871例住院治疗 (2018年1月至2023年9月) 中抽取,涉及89,355名独特的患者.
- 85%的住院检查 (N=148,640) 分配给模型培训,其余的26,231次检查用于混合方法验证.
- 包括患者接触的临床和SDoH数据项目.
结论:
- 拟议的数据驱动风险分层模型整合了SDoH和临床数据,以估计糖尿病患者即将使用ED的个性化风险.
- 该模型可能会自动化案例管理推,改善服务优先级和优化资源配置.
- 混合方法方法确保与医院质量和患者安全考虑保持一致,以改善患者护理.
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