使用电子健康记录识别阿片类药物使用障碍:超越诊断代码
Melissa N Poulsen1, Cara M Nordberg1, Vanessa Troiani2
1Department of Population Health Sciences, Geisinger, Danville, PA, USA.
Drug and alcohol dependence
|September 16, 2023
概括
这项研究开发了一种电子健康记录方法来识别阿片类药物使用障碍 (OUD),包括没有先前阿片类药物处方或慢性疼痛的个人. 该方法有效地识别了使用各种数据源的中度/重度OUD病例.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 临床研究 临床研究
背景情况:
- 阿片类药物使用障碍 (OUD) 识别通常错过了没有处方阿片类药物使用或慢性疼痛的个人.
- 电子健康记录 (EHR) 为OUD检测提供了有价值的数据来源.
研究的目的:
- 开发和验证基于EHR的方法来识别中度/严重的OUD.
- 包括代表性不足的人群,例如没有处方阿片类药物使用史或慢性疼痛的人群.
主要方法:
- 利用了来自100万名患者的结构化和非结构化EHR数据 (2012-2020年).
- 开发了六个子算法,将OUD指标 (级别) 和OUD (MOUD) 订单的药物结合起来.
- 使用图表检查计算正预测值 (PPV) 的验证算法.
主要成果:
- 超过14000名患者在各个子算法中满足了标准.
- 四个子算法在中度/严重的OUD中实现了PPV≥0.90.
- 确诊的OUD病例中很大一部分缺乏处方阿片类药物使用 (79.7%) 或慢性疼痛诊断 (43.5%).
结论:
- 将非结构化EHR数据与MOUD命令集成,可以增强OUD识别.
- 开发的方法成功地识别了中度/严重的OUD,包括那些没有先前阿片类药物处方或慢性疼痛的人.
- 仅限于处方阿片类药物使用者或慢性疼痛患者的EHR算法可能会排除大量OUD患者.
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