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连接电子健康记录与创伤登记数据:评估概率性连接的价值。

Linking Electronic Health Record and Trauma Registry Data: Assessing the Value of Probabilistic Linkage.

作者信息

Durojaiye Ashimiyu B, Puett Lisa L, Levin Scott, Toerper Matthew, McGeorge Nicolette M, Webster Kristen L W, Deol Gurmehar S, Kharrazi Hadi, Lehmann Harold P, Gurses Ayse P

机构信息

Center for Health Care Human Factors, Armstrong Institute for Patient Safety and Quality, Johns Hopkins Medicine, Johns Hopkins University, Baltimore, Maryland, United States.

Division of Health Sciences Informatics, School of Medicine, Johns Hopkins University, Baltimore, Maryland, United States.

出版信息

Methods Inf Med. 2018 Nov;57(5-06):261-269. doi: 10.1055/s-0039-1681087. Epub 2019 Mar 15.

Abstract

BACKGROUND

Electronic health record (EHR) systems contain large volumes of novel heterogeneous data that can be linked to trauma registry data to enable innovative research not possible with either data source alone.

OBJECTIVE

This article describes an approach for linking electronically extracted EHR data to trauma registry data at the institutional level and assesses the value of probabilistic linkage.

METHODS

Encounter data were independently obtained from the EHR data warehouse ( = 1,632) and the pediatric trauma registry ( = 1,829) at a Level I pediatric trauma center. Deterministic linkage was attempted using nine different combinations of medical record number (MRN), encounter identity (ID) (visit ID), age, gender, and emergency department (ED) arrival date. True matches from the best performing variable combination were used to create a gold standard, which was used to evaluate the performance of each variable combination, and to train a probabilistic algorithm that was separately used to link records unmatched by deterministic linkage and the entire cohort. Additional records that matched probabilistically were investigated via chart review and compared against records that matched deterministically.

RESULTS

Deterministic linkage with exact matching on any three of MRN, encounter ID, age, gender, and ED arrival date gave the best yield of 1,276 true matches while an additional probabilistic linkage step following deterministic linkage yielded 110 true matches. These records contained a significantly higher number of boys compared to records that matched deterministically and etiology was attributable to mismatch between MRNs in the two data sets. Probabilistic linkage of the entire cohort yielded 1,363 true matches.

CONCLUSION

The combination of deterministic and an additional probabilistic method represents a robust approach for linking EHR data to trauma registry data. This approach may be generalizable to studies involving other registries and databases.

摘要

背景

电子健康记录(EHR)系统包含大量新型异构数据,这些数据可与创伤登记数据相链接,以开展仅使用任一数据源无法实现的创新性研究。

目的

本文描述了一种在机构层面将电子提取的EHR数据与创伤登记数据相链接的方法,并评估概率性链接的价值。

方法

在一家一级儿科创伤中心,分别从EHR数据仓库(n = 1632)和儿科创伤登记处(n = 1829)获取就诊数据。尝试使用病历号(MRN)、就诊标识(ID)(就诊ID)、年龄、性别和急诊科(ED)到达日期的九种不同组合进行确定性链接。来自表现最佳的变量组合的真实匹配用于创建黄金标准,该标准用于评估每个变量组合的性能,并训练一种概率算法,该算法分别用于链接确定性链接未匹配的记录和整个队列。通过病历审查对概率性匹配的其他记录进行调查,并与确定性匹配的记录进行比较。

结果

在MRN、就诊ID、年龄、性别和ED到达日期中的任意三个进行精确匹配的确定性链接产生了1276个真实匹配的最佳产量,而在确定性链接之后进行的额外概率性链接步骤产生了110个真实匹配。与确定性匹配的记录相比,这些记录中的男孩数量显著更多,病因可归因于两个数据集中MRN之间的不匹配。整个队列的概率性链接产生了1363个真实匹配。

结论

确定性方法与额外概率性方法的结合代表了一种将EHR数据与创伤登记数据相链接的强大方法。这种方法可能适用于涉及其他登记处和数据库的研究。

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