Balestra Martina, Chen Ji, Iturrate Eduardo, Aphinyanaphongs Yindalon, Nov Oded
NYU Center for Urban Science and Progress, Brooklyn, New York, USA.
Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA.
JAMIA Open. 2021 Oct 5;4(3):ooab083. doi: 10.1093/jamiaopen/ooab083. eCollection 2021 Jul.
The widespread deployment of electronic health records (EHRs) has introduced new sources of error and inefficiencies to the process of ordering medications in the hospital setting. Existing work identifies orders that require pharmacy intervention by comparing them to a patient's medical records. In this work, we develop a machine learning model for identifying medication orders requiring intervention using only provider behavior and other contextual features that may reflect these new sources of inefficiencies.
Data on providers' actions in the EHR system and pharmacy orders were collected over a 2-week period in a major metropolitan hospital system. A classification model was then built to identify orders requiring pharmacist intervention. We tune the model to the context in which it would be deployed and evaluate global and local feature importance.
The resultant model had an area under the receiver-operator characteristic curve of 0.91 and an area under the precision-recall curve of 0.44.
Providers' actions can serve as useful predictors in identifying medication orders that require pharmacy intervention. Careful model tuning for the clinical context in which the model is deployed can help to create an effective tool for improving health outcomes without using sensitive patient data.
电子健康记录（EHR）的广泛应用给医院环境中的药物订购流程带来了新的错误来源和低效率问题。现有工作通过将医嘱与患者病历进行比较来识别需要药房干预的医嘱。在本研究中，我们开发了一种机器学习模型，仅使用医护人员行为和其他可能反映这些新的低效率来源的上下文特征来识别需要干预的药物医嘱。
在一个大型都市医院系统中，收集了为期两周的医护人员在电子健康记录系统中的操作数据以及药房医嘱数据。然后构建了一个分类模型来识别需要药剂师干预的医嘱。我们针对模型的部署环境对其进行调整，并评估全局和局部特征的重要性。
所得模型的受试者工作特征曲线下面积为0.91，精确率-召回率曲线下面积为0.44。
医护人员的行为可作为识别需要药房干预的药物医嘱的有用预测指标。针对模型部署的临床环境进行仔细的模型调整，有助于创建一个无需使用敏感患者数据就能改善健康结果的有效工具。
