{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T21:29:19Z","timestamp":1771190959030,"version":"3.50.1"},"reference-count":0,"publisher":"Georg Thieme Verlag KG","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2018,4]]},"abstract":"\n Background\u2003The Veterans Affairs Portland Healthcare System developed a medication history collection software that displays prescription names and medication images.<\/jats:p>\n Objective\u2003This article measures the frequency of medication discrepancy reporting using the medication history collection software and compares with the frequency of reporting using a paper-based process. This article also determines the accuracy of each method by comparing both strategies to a best possible medication history.<\/jats:p>\n Study Design\u2003Randomized, controlled, single-blind trial.<\/jats:p>\n Setting\u2003Three community-based primary care clinics associated with the Veterans Affairs Portland Healthcare System: a 300-bed teaching facility and ambulatory care network serving Veteran soldiers in the Pacific Northwest United States.<\/jats:p>\n Participants\u2003Of 212 patients with primary care appointments, 209 patients fulfilled the study requirements.<\/jats:p>\n Intervention\u2003Patients randomized to a software-directed medication history or a paper-based medication history. Randomization and allocation to treatment groups were performed using a computer-based random number generator. Assignments were placed in a sealed envelope and opened after participant consent. The research coordinator did not know or have access to the treatment assignment until the time of presentation.<\/jats:p>\n Main Outcome Measures\u2003The primary analysis compared the discrepancy detection rates between groups with respect to the health record and a best possible medication history.<\/jats:p>\n Results\u2003Of 3,500 medications reviewed, we detected 1,435 discrepancies. Forty-six percent of those discrepancies were potentially high risk for causing an adverse drug event. There was no difference in detection rates between treatment arms. Software sensitivity was 83% and specificity was 91%; paper sensitivity was 81% and specificity was 94%. No participants were lost to follow-up.<\/jats:p>\n Conclusion\u2003The medication history collection software is an efficient and scalable method for gathering a medication history and detecting high-risk discrepancies. Although it included medication images, the technology did not improve accuracy over a paper list when compared with a best possible medication history.<\/jats:p>\n Trial Registration\u2003ClinicalTrials.gov Identifier: NCT02135731.<\/jats:p>","DOI":"10.1055\/s-0038-1645889","type":"journal-article","created":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T23:06:15Z","timestamp":1525302375000},"page":"285-301","source":"Crossref","is-referenced-by-count":12,"title":["Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Measure Diagnostic Accuracy for Discrepancy Detection"],"prefix":"10.1055","volume":"09","author":[{"given":"Blake","family":"Lesselroth","sequence":"additional","affiliation":[{"name":"NorthWest Innovation Center, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States"},{"name":"Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States"}]},{"given":"Kathleen","family":"Adams","sequence":"additional","affiliation":[{"name":"NorthWest Innovation Center, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States"}]},{"given":"Victoria","family":"Church","sequence":"additional","affiliation":[{"name":"NorthWest Innovation Center, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States"}]},{"given":"Stephanie","family":"Tallett","sequence":"additional","affiliation":[{"name":"NorthWest Innovation Center, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States"}]},{"given":"Yelizaveta","family":"Russ","sequence":"additional","affiliation":[{"name":"Division of Primary Care, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States"}]},{"given":"Jack","family":"Wiedrick","sequence":"additional","affiliation":[{"name":"Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, United States"}]},{"given":"Christopher","family":"Forsberg","sequence":"additional","affiliation":[{"name":"Center of Innovation, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States"}]},{"given":"David","family":"Dorr","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States"}]}],"member":"194","published-online":{"date-parts":[[2018,5,2]]},"container-title":["Applied Clinical Informatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/s-0038-1645889.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,6]],"date-time":"2019-05-06T20:21:08Z","timestamp":1557174068000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/s-0038-1645889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4]]},"references-count":0,"journal-issue":{"issue":"02","published-online":{"date-parts":[[2018,4,4]]},"published-print":{"date-parts":[[2018,4]]}},"URL":"https:\/\/doi.org\/10.1055\/s-0038-1645889","relation":{},"ISSN":["1869-0327"],"issn-type":[{"value":"1869-0327","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,4]]}}}