从保险索赔数据预测自杀行为风险,与从保险索赔和电子健康记录中获得的链接数据相比
Gregory E Simon1,2, Susan M Shortreed1,3, Eric Johnson1
1Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.
Pharmacoepidemiology and drug safety
|December 19, 2023
概括
仅仅使用保险索赔数据来预测自杀行为风险与使用保险索赔和电子健康记录 (EHR) 结合使用同样有效. 这一发现简化了对医疗产品和自杀风险的观察性研究的数据要求.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 精神病学是一个精神病学.
背景情况:
- 关于医疗产品对自杀行为的影响的观察性研究通常使用健康记录数据来控制先前存在的风险.
- 准确预测自杀风险对于患者安全和有效干预至关重要.
研究的目的:
- 用结合保险索赔和电子健康记录 (EHR) 的自杀风险高维模型的预测性能与仅使用保险索赔数据的模型进行比较.
- 确定是否结合EHR数据可以显著改善自杀风险预测模型.
主要方法:
- 利用来自七个大型卫生系统的数据,包括从2009年至2017年的门诊心理健康访问.
- 开发并验证了随机森林模型,以预测非致命和致命的自我伤害在访问后90天内.
- 仅使用保险索赔数据的模型与使用链接索赔和EHR数据 (包括人口统计和患者健康问卷/PHQ-9分数) 的模型进行了比较.
主要成果:
- 在使用所有数据的模型和仅使用索赔数据的模型之间,在分类性能上没有显著差异 (AUC=0.846).
- 两种模型类型在各种风险层表现出类似的校准性能.
- 即使分析仅限于PHQ-9数据收集频率更高的时期,结果也保持一致.
结论:
- 用于自杀风险预测的高维模型不需要包含链接的EHR数据.
- 仅靠保险索赔数据就足以在观察性研究中开发强大的自杀风险预测模型.
- 研究人员可以自信地使用保险索赔数据,而无需为此目的集成EHR数据的复杂性.
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