{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T15:07:27Z","timestamp":1769612847123,"version":"3.49.0"},"reference-count":29,"publisher":"Georg Thieme Verlag KG","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2016,4]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Health information exchange (HIE) facilitates the exchange of patient information across different healthcare organizations. To match patient records across sites, HIEs usually rely on a master patient index (MPI), a database responsible for determining which medical records at different healthcare facilities belong to the same patient. A single patient\u2019s records may be improperly split across multiple profiles in the MPI.<\/jats:p><jats:p>We investigated the how often two individuals shared the same first name, last name, and date of birth in the Social Security Death Master File (SSDMF), a US government database containing over 85 million individuals, to determine the feasibility of using exact matching as a split record detection tool. We demonstrated how a method based on exact record matching could be used to partially measure the degree of probable split patient records in the MPI of an HIE.<\/jats:p><jats:p>We calculated the percentage of individuals who were uniquely identified in the SSDMF using first name, last name, and date of birth. We defined a measure consisting of the average number of unique identifiers associated with a given first name, last name, and date of birth. We calculated a reference value for this measure on a subsample of SSDMF data. We compared this measure value to data from a functioning HIE.<\/jats:p><jats:p>We found that it was unlikely for two individuals to share the same first name, last name, and date of birth in a large US database including over 85 million individuals. 98.81% of individuals were uniquely identified in this dataset using only these three items. We compared the value of our measure on a subsample of Social Security data (1.00089) to that of HIE data (1.1238) and found a significant difference (t-test p-value &lt; 0.001).<\/jats:p><jats:p>This method may assist HIEs in detecting split patient records.<\/jats:p>","DOI":"10.4338\/aci-2015-11-ra-0158","type":"journal-article","created":{"date-parts":[[2016,5,11]],"date-time":"2016-05-11T07:07:04Z","timestamp":1462950424000},"page":"330-340","source":"Crossref","is-referenced-by-count":9,"title":["Measuring the Degree of Unmatched Patient Records in a Health Information Exchange Using Exact Matching"],"prefix":"10.4338","volume":"07","author":[{"given":"John","family":"Zech","sequence":"first","affiliation":[]},{"given":"Gregg","family":"Husk","sequence":"first","affiliation":[]},{"given":"Thomas","family":"Moore","sequence":"first","affiliation":[]},{"given":"Jason","family":"Shapiro","sequence":"additional","affiliation":[]}],"member":"194","published-online":{"date-parts":[[2017,12,16]]},"reference":[{"key":"10.4338\/ACI-2015-11-RA-0158-1","unstructured":"The National Alliance for Health Information Technology Report to the Office of the National Coordinator for Health Information Technology on Defining Key Health Information Technology Terms. 2008"},{"key":"10.4338\/ACI-2015-11-RA-0158-2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1969.10501049"},{"key":"10.4338\/ACI-2015-11-RA-0158-3","unstructured":"Grannis SJ, Overhage JM, McDonald CJ. 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