药物流行病学
医学
健康档案
倾向得分匹配
护理的连续性
事件数据
电子健康档案
结果(博弈论)
算法
计算机科学
数据挖掘
内科学
医疗保健
数学
药方
数理经济学
分析
经济
药理学
经济增长
作者
James Flory,Yongkang Zhang,Samprit Banerjee,Fei Wang,Jea Young Min,Alvin I. Mushlin
摘要
Abstract Background and Objectives Use of algorithms to identify patients with high data‐continuity in electronic health records (EHRs) may increase study validity. Practical experience with this approach remains limited. Methods We developed and validated four algorithms to identify patients with high data continuity in an EHR‐based data source. Selected algorithms were then applied to a pharmacoepidemiologic study comparing rates of COVID‐19 hospitalization in patients exposed to insulin versus noninsulin antidiabetic drugs. Results A model using a short list of five EHR‐derived variables performed as well as more complex models to distinguish high‐ from low‐data continuity patients. Higher data continuity was associated with more accurate ascertainment of key variables. In the pharmacoepidemiologic study, patients with higher data continuity had higher observed rates of the COVID‐19 outcome and a large unadjusted association between insulin use and the outcome, but no association after propensity score adjustment. Discussion We found that a simple, portable algorithm to predict data continuity gave comparable performance to more complex methods. Use of the algorithm significantly impacted the results of an empirical study, with evidence of more valid results at higher levels of data continuity.
科研通智能强力驱动
Strongly Powered by AbleSci AI