Prediction of emergency department revisits using area-level social determinants of health measures and health information exchange information
Prediction of emergency department revisits using area-level social determinants of health measures and health information exchange information
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DOI：
10.1016/j.ijmedinf.2019.06.013
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发表时间：
2019-09-01
影响因子：
4.9
通讯作者：
Ben-Assuli, Ofir
中科院分区：
文献类型：
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作者：
Vest, Joshua R.;Ben-Assuli, Ofir
简介：可互操作的健康信息技术，如电子健康记录（EHR）和健康信息交换（HIE），可以更好地访问来自多个组织的患者信息。此外，越来越多的公共数据来源描述了健康因素的社会决定因素。这些数据可能有助于更好地为风险预测模型提供信息，但这些数据的相对重要性或价值尚未确定。本研究评估了不同类别的信息单独和组合的性能，在预测急诊科（艾德）revisions.Methods：在一个样本的279,611成人艾德遭遇。我们使用5类信息比较了两类提升决策树机器学习算法的性能：1）仅健康指标的社会决定因素，2）仅当前访问EHR信息，3）当前和历史EHR信息，4）仅HIE信息，5）所有可用信息的组合。仅健康测量的社会决定因素模型的整体表现最差，曲线下面积AUC为0.61。同时使用所有信息类的模型具有最佳性能（AUC = 0.732）。使用HIE信息的模型只执行优于所有其他单一的信息类models.Conclusions：广泛的信息来源，这是反映病人的依赖多个组织的护理，更好地支持风险预测建模在急诊科。
Introduction: Interoperable health information technologies, like electronic health records (EHR) and health information exchange (HIE), provide greater access to patient information from across multiple organizations. Also, an increasing number of public data sources exist to describe social determinant of health factors. These data may help better inform risk prediction models, but the relative importance or value of these data has not been established. This study assessed the performance of different classes of information individually, and in combination, in predicting emergency department (ED) revisits.Methods: In a sample of 279,611 adult ED encounters. We compared the performance of Two-Class Boosted Decision Trees machine learning algorithm using 5 classes of information: 1) social determinants of health measures only, 2) current visit EHR information only, 3) current and historical EHR information, 4) HIE information only, and 5) all available information combined.Results: The social determinants of health measure only model had the overall worst performance with an area under the curve AUC of 0.61. The model using all information classes together had the best performance (AUC = 0.732). The model using HIE information only performed better than all other single information class models.Conclusions: Broad information sources, which are reflective of patients' reliance on multiple organizations for care, better support risk prediction modeling in the emergency department.