通过与健康相关的社会需求建模患者的进展
Haleigh Kampman1, Ofir Ben-Assuli2, Joshua Vest1,3
1Health Policy and Management, Indiana University, Indianapolis, Indiana, United States.
Applied clinical informatics
|September 19, 2025
概括
这项研究通过使用隐藏的马尔科夫建模,随着时间的推移追踪了与健康相关的社会需求 (HRSN). 大多数患者仍然处于低风险状态,而高风险个体面临着持续的挑战,突出了对干预的需求.
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
- 计算健康 计算健康
背景情况:
- 与健康相关的社会需求 (HRSNs) 显著影响患者的结果.
- 对HRSN经验的纵向表征对于有效的干预至关重要.
- 了解风险状态之间的人口层面过渡对于公共卫生战略至关重要.
研究的目的:
- 描述患者群体中与健康相关的社会需求 (HRSNs) 的纵向经验.
- 识别与各种HRSNs相关的不同风险状态.
- 分析患者在这些风险状态之间随着时间的推移而发生的转变.
主要方法:
- 利用隐藏的马尔科夫建模 (HMM) 在2018-2020年电子健康记录数据上.
- 使用自然语言处理 (NLP) 来提取有关食品,法律,交通,就业,金融和住房需求的数据.
- 鉴定风险状态之间的比较特征和过渡频率.
主要成果:
- 五个隐藏状态有效地模拟了纵向HRSN体验.
- 在48,055名患者中,80%进入低风险状态 (1和2);17%处于中高风险状态;3%处于非常高风险状态.
- 低风险/非常高风险的患者过渡状态很少发生 (约. 每1.5年),而中高风险患者每年过渡一次.
结论:
- 患者对HRSNs的经验可以纵向划分为不同的风险状态.
- 低风险患者可能拥有维持健康的资源,而非常高风险患者则面临着持续的需求.
- 建议进行早期查和及时干预,以解决持续的HRSNs并减轻危害.
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