在索赔中建立治疗终止的验证框架,使用自然语言处理和电子健康记录来处理索赔数据
Chun-Ting Yang1, Kerry Ngan1, Dae Hyun Kim2,3
1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Clinical pharmacology and therapeutics
|April 8, 2025
概括
这项研究开发了一个自然语言处理 (NLP) 框架,以验证药物停用在索赔数据中的有效性. 对抗精神病药物,二胺,华法林和DOACs的基于索赔的算法的准确性因药物和差距长度而异.
科学领域:
- 医疗信息学 医疗信息学
- 药物监督 药物监督 药物监督
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 医疗保健索赔数据中药物停用的准确测量对于患者安全和研究至关重要.
- 目前依赖处方填充缺口的方法缺乏强有力的验证,可能导致不准确的结论.
- 电子健康记录 (EHR) 为验证基于索赔的算法提供了丰富的数据源.
研究的目的:
- 建立和评估基于NLP的验证框架,用于评估基于索赔的药物停用算法.
- 将索赔数据中的不同处方间隔长度 (15-90天) 的表现与NLP衍生的EHR参考标准进行比较.
- 评估该框架在各种药物类别中的实用性,包括抗精神病药物 (APM),二胺 (BZD),华法林和直接口服抗凝剂 (DOAC).
主要方法:
- 从EHR (2007-2020) 中确定了使用APM,BZD,华法林或DOAC的36,656名患者队列.
- 电子健康记录数据与医疗保险D部分索赔 (97,900项索赔) 有关.
- 通过NLP辅助的电子健康记录图表审查,建立了药物停用的参考标准;索赔数据使用15-90天的间隔来定义停用.
主要成果:
- 基于索赔的停药算法的准确性在不同的药物和处方间隔长度之间有显著差异.
- 例如,90天间隔算法显示灵敏度从0.33 (华法林) 到0.47 (BZDs) 和特异性从0.75 (BZDs) 到0.87 (DOACs).
- 与90天间隙相比,15天间隙算法通常具有更高的灵敏度,但较低的特异性,正确的预测值受停药率的影响.
结论:
- 开发的基于NLP的框架是可扩展和有效的,用于验证跨多种药物类别的药物停用算法.
- 基于索赔的停药定义需要仔细考虑药物类型和选择的间隔长度,因为性能变化.
- 这种验证方法提高了来自医疗保健索赔数据的现实证据的可靠性.
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