{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:42:46Z","timestamp":1780461766878,"version":"3.54.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T00:00:00Z","timestamp":1652572800000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100001018","name":"California HealthCare Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001018","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>This study sought both to support evidence-based patient identity policy development by illustrating an approach for formally evaluating operational matching methods, and also to characterize the performance of both referential and probabilistic patient matching algorithms using real-world demographic data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>We assessed matching accuracy for referential and probabilistic matching algorithms using a manually reviewed 30\u00a0000 record gold standard reference dataset derived from a large health information exchange containing over 47 million patient registrations. We applied referential and probabilistic algorithms to this dataset and compared the outputs to the gold standard. We computed performance metrics including sensitivity (recall), positive predictive value (precision), and F-score for each algorithm.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The probabilistic algorithm exhibited sensitivity, positive predictive value (PPV), and F-score of .6366, 0.9995, and 0.7778, respectively. The referential algorithm exhibited corresponding sensitivity, PPV, and F-score values of 0.9351, 0.9996, and 0.9663, respectively. Treating discordant and limited-data records as nonmatches increased referential match sensitivity to 0.9578. Compared to the more traditional probabilistic approach, referential matching exhibits greater accuracy.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Referential patient matching, an increasingly popular method among health IT vendors, demonstrated notably greater accuracy than a more traditional probabilistic approach without the adaptation of the algorithm to the data that the traditional probabilistic approach usually requires. Health IT policymakers, including the Office of the National Coordinator for Health Information Technology (ONC), should explore strategies to expand the evidence base for real-world matching system performance, given the need for an evidence-based patient identity strategy.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocac068","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T19:18:48Z","timestamp":1652210328000},"page":"1409-1415","source":"Crossref","is-referenced-by-count":15,"title":["Evaluation of real-world referential and probabilistic patient matching to advance patient identification strategy"],"prefix":"10.1093","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8093-6639","authenticated-orcid":false,"given":"Shaun J","family":"Grannis","sequence":"first","affiliation":[{"name":"Department of Family Medicine, Indiana University School of Medicine , Indianapolis, Indiana, USA"},{"name":"Regenstrief Institute, Center for Biomedical Informatics , Indianapolis, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer L","family":"Williams","sequence":"additional","affiliation":[{"name":"Regenstrief Institute, Center for Biomedical Informatics , Indianapolis, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suranga","family":"Kasthuri","sequence":"additional","affiliation":[{"name":"Regenstrief Institute, Center for Biomedical Informatics , Indianapolis, Indiana, USA"},{"name":"Department of Pediatrics, Indiana University School of Medicine , Indianapolis, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Molly","family":"Murray","sequence":"additional","affiliation":[{"name":"Pew Charitable Trust , Baltimore, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiping","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, IU Richard M. Fairbanks School of Public Health , Indianapolis, Indiana, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,5,14]]},"reference":[{"issue":"15","key":"2022071310035111200_ocac068-B1","doi-asserted-by":"crossref","first-page":"1325","DOI":"10.1001\/jama.280.15.1325","article-title":"Canopy computing: using the Web in clinical practice","volume":"280","author":"McDonald","year":"1998","journal-title":"JAMA"},{"key":"2022071310035111200_ocac068-B2","first-page":"409","article-title":"All health care is not local: an evaluation of the distribution of Emergency Department care delivered in Indiana","volume":"2011","author":"Finnell","year":"2011","journal-title":"AMIA Annu Symp Proc"},{"issue":"10\u201311","key":"2022071310035111200_ocac068-B3","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.clinbiochem.2017.02.004","article-title":"Managing the patient identification crisis in healthcare and laboratory medicine","volume":"50","author":"Lippi","year":"2017","journal-title":"Clin Biochem"},{"issue":"11","key":"2022071310035111200_ocac068-B4","doi-asserted-by":"crossref","first-page":"e23353","DOI":"10.2196\/23353","article-title":"Universal patient identifier and interoperability for detection of serious drug interactions: retrospective study","volume":"8","author":"Sragow","year":"2020","journal-title":"JMIR Med Inform"},{"issue":"7","key":"2022071310035111200_ocac068-B5","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1111\/j.1553-2712.2008.00148.x","article-title":"Providers do not verify patient identity during computer order entry","volume":"15","author":"Henneman","year":"2008","journal-title":"Acad Emerg Med"},{"issue":"4","key":"2022071310035111200_ocac068-B6","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1136\/amiajnl-2010-000068","article-title":"Minimizing electronic health record patient-mismatches","volume":"18","author":"Wilcox","year":"2011","journal-title":"J Am Med Inform Assoc"},{"key":"2022071310035111200_ocac068-B7","year":"2021"},{"key":"2022071310035111200_ocac068-B8","year":"2018"},{"issue":"1","key":"2022071310035111200_ocac068-B9","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1038\/s41746-020-0289-4","article-title":"Better patient identification could help fight the coronavirus","volume":"3","author":"Moscovitch","year":"2020","journal-title":"NPJ Digit Med"},{"key":"2022071310035111200_ocac068-B10","author":"VanHouten","year":"2021"},{"key":"2022071310035111200_ocac068-B11","first-page":"1e","article-title":"Why patient matching is a challenge: research on master patient index (MPI) data discrepancies in key identifying fields","volume":"13","author":"Just","year":"2016","journal-title":"Perspect Health Inf Manag"},{"key":"2022071310035111200_ocac068-B12","year":"2020"},{"key":"2022071310035111200_ocac068-B13","author":"Hackett","year":"2020"},{"key":"2022071310035111200_ocac068-B14","author":"Urahn","year":"2018"},{"issue":"1","key":"2022071310035111200_ocac068-B15","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1055\/s-0040-1701984","article-title":"Patient identification techniques \u2013 approaches, implications, and findings","volume":"29","author":"Riplinger","year":"2020","journal-title":"Yearb Med Inform"},{"issue":"1","key":"2022071310035111200_ocac068-B16","doi-asserted-by":"crossref","first-page":"e43","DOI":"10.1542\/peds.2005-0291","article-title":"Patient misidentification in the neonatal intensive care unit: quantification of risk","volume":"117","author":"Gray","year":"2006","journal-title":"Pediatrics"},{"issue":"2","key":"2022071310035111200_ocac068-B17","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1055\/s-0040-1705175","article-title":"Effect of an alternative newborn naming strategy on wrong-patient errors: a quasi-experimental study","volume":"11","author":"Pfeifer","year":"2020","journal-title":"Appl Clin Inform"},{"issue":"5","key":"2022071310035111200_ocac068-B18","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1197\/jamia.M3186","article-title":"An empiric modification to the probabilistic record linkage algorithm using frequency-based weight scaling","volume":"16","author":"Zhu","year":"2009","journal-title":"J Am Med Inform Assoc"},{"issue":"5","key":"2022071310035111200_ocac068-B19","doi-asserted-by":"crossref","first-page":"1214","DOI":"10.1377\/hlthaff.24.5.1214","article-title":"The Indiana Network for Patient Care: a working local health information infrastructure. 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