{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,29]],"date-time":"2023-12-29T17:39:25Z","timestamp":1703871565297},"reference-count":15,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016,9,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p>Objective The objective of this project was to use statistical techniques to determine the completeness and accuracy of data migrated during electronic health record conversion.<\/jats:p>\n               <jats:p>Methods Data validation during migration consists of mapped record testing and validation of a sample of the data for completeness and accuracy. We statistically determined a randomized sample size for each data type based on the desired confidence level and error limits.<\/jats:p>\n               <jats:p>Results The only error identified in the post go-live period was a failure to migrate some clinical notes, which was unrelated to the validation process. No errors in the migrated data were found during the 12- month post-implementation period.<\/jats:p>\n               <jats:p>Conclusions Compared to the typical industry approach, we have demonstrated that a statistical approach to sampling size for data validation can ensure consistent confidence levels while maximizing efficiency of the validation process during a major electronic health record conversion.<\/jats:p>","DOI":"10.1093\/jamia\/ocv173","type":"journal-article","created":{"date-parts":[[2016,3,15]],"date-time":"2016-03-15T02:43:22Z","timestamp":1458009802000},"page":"991-994","source":"Crossref","is-referenced-by-count":10,"title":["A rational approach to legacy data validation when transitioning between electronic health record systems"],"prefix":"10.1093","volume":"23","author":[{"given":"Natalie M","family":"Pageler","sequence":"first","affiliation":[{"name":"Division of Critical Care Medicine Department of Pediatrics, Stanford University School of Medicine"},{"name":"Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA"},{"name":"Information Services Department, Stanford Children's Health, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Max Jacob","family":"Grazier G\u2019Sell","sequence":"additional","affiliation":[{"name":"Department of Statistics, Carnegie Mellon University, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Warren","family":"Chandler","sequence":"additional","affiliation":[{"name":"Information Services Department, Stanford Children's Health, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emily","family":"Mailes","sequence":"additional","affiliation":[{"name":"Information Services Department, Stanford Children's Health, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christine","family":"Yang","sequence":"additional","affiliation":[{"name":"Information Services Department, Stanford Children's Health, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher A","family":"Longhurst","sequence":"additional","affiliation":[{"name":"Departments of Biomedical Informatics and Pediatrics, University of California San Diego, CA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2016,3,14]]},"reference":[{"key":"2020110612382444400_ocv173-B1","doi-asserted-by":"crossref","first-page":"367","DOI":"10.4338\/ACI-2012-04-R-0014","article-title":"Transitioning from a legacy EHR to a commercial, vendor-supplied, EHR: one academic health system\u2019s experience","volume":"3","author":"Gettinger","year":"2012","journal-title":"Appl Clin Inform."},{"key":"2020110612382444400_ocv173-B2","first-page":"181","article-title":"Need versus cost: understanding EHR data migration options","volume":"29","author":"West","year":"2013","journal-title":"J Med Pract 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