Evaluation of Multimedia Medication Reconciliation Software: A Randomized Controlled, Single-Blind Trial to Measure Diagnostic Accuracy for Discrepancy Detection

• DOI: 10.1055/s-0038-1645889
• Publication Date: 2018-05-02T23:06:15Z
• Authors: Blake Lesselroth; Kathleen Adams; Victoria Church; Stephanie Tallett; Yelizaveta Russ; Jack Wiedrick; Christopher Forsberg; David Dorr
• Keywords: Male; Primary Health Care; Middle Aged; 16. Peace & justice; 3. Good health; 03 medical and health sciences; Medication Reconciliation; 0302 clinical medicine; Multimedia; Research Design; Electronic Health Records; Humans; Female; Single-Blind Method; Software; Aged

Background The Veterans Affairs Portland Healthcare System developed a medication history collection software that displays prescription names and medication images. Objective This 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. Study Design Randomized, controlled, single-blind trial. Setting Three 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. Participants Of 212 patients with primary care appointments, 209 patients fulfilled the study requirements. Intervention Patients 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. Main Outcome Measures The primary analysis compared the discrepancy detection rates between groups with respect to the health record and a best possible medication history. Results Of 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. Conclusion The 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. Trial Registration ClinicalTrials.gov Identifier: NCT02135731.