Subscribe to RSS
DOI: 10.1055/s-0040-1718374
Accuracy of an Electronic Health Record Patient Linkage Module Evaluated between Neighboring Academic Health Care Centers
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).![](https://www.thieme-connect.de/media/10.1055-s-00035026/202005/lookinside/thumbnails/10-1055-s-0040-1718374_200115ra-1.jpg)
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/methodsProtection 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
© 2020. Thieme. All rights reserved.
Georg Thieme Verlag KG
Stuttgart · New York
-
References
- 1 Cebul RD, Rebitzer JB, Taylor LJ, Votruba ME. Organizational fragmentation and care quality in the U.S healthcare system. J Econ Perspect 2008; 22 (04) 93-113
- 2 Frandsen BR, Joynt KE, Rebitzer JB, Jha AK. Care fragmentation, quality, and costs among chronically ill patients. Am J Manag Care 2015; 21 (05) 355-362
- 3 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
- 4 Haug PJ, Narus SP, Bledsoe J, Huff S. Promoting national and international standards to build interoperable clinical applications. AMIA Annu Symp Proc 2018; 2018: 555-563
- 5 DeSalvo KB, Dinkler AN, 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
- 6 Duggal R, Khatri SK, Shukla B. Improving patient matching: single patient view for Clinical Decision Support using Big Data analytics. Paper presented at: 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO; Trends and Future Directions); 2015
- 7 Harron K, Goldstein H, Dibben C. , eds. Methodological Developments in Data Linkage. West Sussex, United Kingdom: John Wiley & Sons; 2015
- 8 Martin TJ, Ranney ML, Dorroh J, Asselin N, Sarkar IN. Health Information Exchange in Emergency Medical Services. Appl Clin Inform 2018; 9 (04) 884-891
- 9 Kruse CS, Marquez G, Nelson D, Palomares O. The Use of Health Information Exchange to Augment Patient Handoff in Long-Term Care: A Systematic Review. Appl Clin Inform 2018; 9 (04) 752-771
- 10 Cummins MR, Ranade-Kharkar P, Johansen C. et al. Simple Workflow Changes Enable Effective Patient Identity Matching in Poison Control. Appl Clin Inform 2018; 9 (03) 553-557
- 11 Parker CJ, Adler-Milstein J. Errors related to health information exchange. In: Agarwal A. , ed. Safety of Health IT: Clinical Case Studies. Cham, Switzerland: Springer; 2016: 153-165
- 12 Care Everywhere 101: Epic Systems Care Everywhere Training Companion 2015. Available at: https://galaxy.epic.com/?#Browse/page=8400!35!240!3729927 (access via login)
- 13 Grannis SJ, Overhage JM, McDonald CJ. Analysis of identifier performance using a deterministic linkage algorithm. Proc AMIA Symp 2002; 305-309
- 14 Kho AN, Cashy JP, Jackson KL. et al. Design and implementation of a privacy preserving electronic health record linkage tool in Chicago. J Am Med Inform Assoc 2015; 22 (05) 1072-1080
- 15 Vest JR, Kaushal R, Silver MD, Hentel K, Kern LM. Health information exchange and the frequency of repeat medical imaging. Am J Manag Care 2014; 20 (11 Spec No. 17): eSP16-eSP24
- 16 Williams C, Mostashari F, Mertz K, Hogin E, Atwal P. From the Office of the National Coordinator: the strategy for advancing the exchange of health information. Health Aff (Millwood) 2012; 31 (03) 527-536
- 17 Siljander B. Assessing the impact of the medicare access and CHIP reauthorization act: the repeal of the SGR and beyond. Health Law. 2014; 27: 26
- 18 Eden KB, Totten AM, Kassakian SZ. et al. Barriers and facilitators to exchanging health information: a systematic review. Int J Med Inform 2016; 88: 44-51
- 19 Holmgren AJ, Patel V, Adler-Milstein J. Progress in interoperability: measuring us hospitals' engagement in sharing patient data. Health Aff (Millwood) 2017; 36 (10) 1820-1827
- 20 Everson J, Kocher KE, Adler-Milstein J. Health information exchange associated with improved emergency department care through faster accessing of patient information from outside organizations. J Am Med Inform Assoc 2017; 24 (e1): e103-e110
- 21 Winden TJ, Boland LL, Frey NG, Satterlee PA, Hokanson JS. Care everywhere, a point-to-point HIE tool: utilization and impact on patient care in the ED. Appl Clin Inform 2014; 5 (02) 388-401
- 22 Ruley M, Walker V, Studeny J, Coustasse A. The nationwide health information network: the case of the expansion of health information exchanges in the United States. Health Care Manag (Frederick) 2018; 37 (04) 333-338
- 23 Just BH, Marc D, Munns M, Sandefer R. Why patient matching is a challenge: research on master patient index (MPI) data discrepancies in key identifying fields. Perspect Health Inf Manag 2016; 13 (Spring): 1e
- 24 Churches T, Christen P. Some methods for blindfolded record linkage. BMC Med Inform Decis Mak 2004; 4: 9
- 25 Dusserre L, Quantin C, Bouzelat H. A one way public key cryptosystem for the linkage of nominal files in epidemiological studies. Medinfo 1995; 8 (Pt 1): 644-647
- 26 Adam N, White T, Shafiq B, Vaidya J, He X. Privacy preserving integration of health care data. AMIA Annu Symp Proc 2007; 2007: 1-5
- 27 Neubauer T, Heurix J. A methodology for the pseudonymization of medical data. Int J Med Inform 2011; 80 (03) 190-204
- 28 Kuzu M, Kantarcioglu M, Durham EA, Toth C, Malin B. A practical approach to achieve private medical record linkage in light of public resources. J Am Med Inform Assoc 2013; 20 (02) 285-292
- 29 Vatsalan D, Christen P. Privacy-preserving matching of similar patients. J Biomed Inform 2016; 59: 285-298
- 30 Toth C, Durham E, Kantarcioglu M, Xue Y, Malin B. SOEMPI: a secure open enterprise master patient index software toolkit for private record linkage. AMIA Annu Symp Proc 2014; 2014: 1105-1114
- 31 Randall SM, Ferrante AM, Boyd JH, Bauer JK, Semmens JB. Privacy-preserving record linkage on large real world datasets. J Biomed Inform 2014; 50: 205-212
- 32 Brown AP, Borgs C, Randall SM, Schnell R. Evaluating privacy-preserving record linkage using cryptographic long-term keys and multibit trees on large medical datasets. BMC Med Inform Decis Mak 2017; 17 (01) 83
- 33 Hersh W, Totten A, Eden K. et al. Health information exchange. Evidence report/technology assessment 2015; 220: 1-465