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Appl Clin Inform 2020; 11(05): 725-732
DOI: 10.1055/s-0040-1718374
Research Article

Accuracy of an Electronic Health Record Patient Linkage Module Evaluated between Neighboring Academic Health Care Centers

Mindy K. Ross
1   Department of Pediatrics, University of California Los Angeles, Los Angeles, United States
,
Javier Sanz
2   Department of Medicine, Clinical and Translational Science Institute, University of California Los Angeles, Los Angeles, United States
,
Brian Tep
3   Department of Enterprise Information Services, Advanced Analytic Services, Cedars-Sinai Medical Center, Los Angeles, United States
,
Rob Follett
2   Department of Medicine, Clinical and Translational Science Institute, University of California Los Angeles, Los Angeles, United States
,
Spencer L. Soohoo
4   Department of Biomedical Sciences, Division of Informatics, Cedars-Sinai Medical Center, Los Angeles, United States
,
Douglas S. Bell
5   Department of Medicine, University of California Los Angeles, Los Angeles, United States
› Author Affiliations Funding This research was supported by U.S. Department of Health and Human Services, National Institutes of Health, National Center for Advancing Translational Sciences, (grant no.: UL1TR001881).
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Abstract

Background Patients often seek medical treatment among different health care organizations, which can lead to redundant tests and treatments. One electronic health record (EHR) platform, Epic Systems, uses a patient linkage tool called Care Everywhere (CE), to match patients across institutions. To the extent that such linkages accurately identify shared patients across organizations, they would hold potential for improving care.

Objective This study aimed to understand how accurate the CE tool with default settings is to identify identical patients between two neighboring academic health care systems in Southern California, The University of California Los Angeles (UCLA) and Cedars-Sinai Medical Center.

Methods We studied CE patient linkage queries received at UCLA from Cedars-Sinai between November 1, 2016, and April 30, 2017. We constructed datasets comprised of linkages (“successful” queries), as well as nonlinkages (“unsuccessful” queries) during this time period. To identify false positive linkages, we screened the “successful” linkages for potential errors and then manually reviewed all that screened positive. To identify false-negative linkages, we applied our own patient matching algorithm to the “unsuccessful” queries and then manually reviewed a sample to identify missed patient linkages.

Results During the 6-month study period, Cedars-Sinai attempted to link 181,567 unique patient identities to records at UCLA. CE made 22,923 “successful” linkages and returned 158,644 “unsuccessful” queries among these patients. Manual review of the screened “successful” linkages between the two institutions determined there were no false positives. Manual review of a sample of the “unsuccessful” queries (n = 623), demonstrated an extrapolated false-negative rate of 2.97% (95% confidence interval [CI]: 1.6–4.4%).

Conclusion We found that CE provided very reliable patient matching across institutions. The system missed a few linkages, but the false-negative rate was low and there were no false-positive matches over 6 months of use between two nearby institutions.

Keywords

electronic health records/standards - health information exchange/standards - medical record linkage/methods

Protection of Human and Animal Subjects

No human subjects were involved in the project.




Publication History

Received: 03 June 2020

Accepted: 27 August 2020

Article published online:
04 November 2020

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