通过开发临床医生信息算法来改善孕产妇健康公平性和结果:可行性研究
Jena Wallander Gemkow1, Eve Walter1, Nivedita Mohanty2
1AllianceChicago, Chicago, Illinois (Jena Wallander Gemkow, Dr. Walter, Ta-Yun Yang).
The Journal of ambulatory care management
|March 11, 2026
概括
这项研究开发了一种算法,用于识别联邦合格卫生中心 (FQHC) 的高风险孕妇患者. 虽然改进了产后访问预测,但该算法在预测不良母亲健康结果时的准确性有限.
科学领域:
- 孕产妇健康 孕产妇健康
- 医疗信息学 医疗信息学
- 人口健康管理 人口健康管理
背景情况:
- 产后不良母亲健康结果正在增加.
- 门诊设置可以解决产后并发症.
- 联邦合格卫生中心 (FQHCs) 服务于弱势群体.
研究的目的:
- 为人口健康工具开发和测试一个算法.
- 在FQHC中识别高风险的产前患者.
- 改善识别需要产后护理的患者.
主要方法:
- 工具开发的以人为中心的设计.
- 焦点小组和采访FQHC临床医生.
- 使用18个FQHC的电子健康记录 (EHR) 数据进行预测建模.
主要成果:
- 算法提高了产后访问回忆率45% (准确率96%).
- 对不良结果的回忆增加了16%,但预测准确率为42%.
- 用户采访表明,该工具在识别高风险患者方面具有实用性.
结论:
- 了解EHR数据和临床医生的参与对于干预开发至关重要.
- 未来的研究应该整合多样化的数据源,以进行全面的风险评估.
- 优化对脆弱的孕产妇群体的护理需要强大的工具和数据.
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