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. 2020 Oct;11(5):725-732.
doi: 10.1055/s-0040-1718374. Epub 2020 Nov 4.

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

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

Mindy K Ross et al. Appl Clin Inform. 2020 Oct.

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.

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Conflict of interest statement

Epic played no role in the planning, conduct, analysis or interpretation of these results. D.S.B reports grants from National Center for Advancing Translational Sciences, during the conduct of the study. D.S.B. reports grants from National Center for Advancing Translational Sciences, during the conduct of the study. B.T. reports grants from NIH CTSI grant, during the conduct of the study. S.S. reports grants from National Institutes of Health, during the conduct of the study.

Figures

Fig. 1
Fig. 1
Data storage flow chart for Care Everywhere queries. “Nondetailed outcome report” references records that do not include additional details to explain the outcome. For “unsuccessful” queries, it refers to the lack of further classification to explain why the matching attempt was not successful. For “successful” queries, it refers to the absence of patient identifiers included in the original request that was sent across institutions. “Query record” refers to the metadata related to the query itself (time stamp, outcome, institution ID, etc.), not patient information, that is recorded in the database system.

References

    1. Cebul R D, Rebitzer J B, Taylor L J, Votruba M E. Organizational fragmentation and care quality in the U.S healthcare system. J Econ Perspect. 2008;22(04):93–113. - PubMed
    1. Frandsen B R, Joynt K E, Rebitzer J B, Jha A K. Care fragmentation, quality, and costs among chronically ill patients. Am J Manag Care. 2015;21(05):355–362. - PubMed
    1. Pellegrin K, Chan F, Pagoria N, Jolson-Oakes S, Uyeno R, Levin A. A Statewide Medication Management System: Health Information Exchange to Support Drug Therapy Optimization by Pharmacists across the Continuum of Care. Appl Clin Inform. 2018;9(01):1–10. - PMC - PubMed
    1. Haug P J, Narus S P, Bledsoe J, Huff S. Promoting national and international standards to build interoperable clinical applications. AMIA Annu Symp Proc. 2018;2018:555–563. - PMC - PubMed
    1. DeSalvo K B, Dinkler A N, Stevens L. The US Office of the National Coordinator for Health Information Technology: Progress and Promise for the Future at the 10-Year Mark. Ann Emerg Med. 2015;66(05):507–510. - PubMed

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