Blake J Lesselroth1,2, Kathleen Adams1, Victoria L Church1, Stephanie Tallett1, Yelizaveta Russ3, Jack Wiedrick4, Christopher Forsberg5, David A Dorr2. 1. NorthWest Innovation Center, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States. 2. Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States. 3. Division of Primary Care, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States. 4. Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, Oregon, United States. 5. Center of Innovation, Veterans' Affairs Portland Healthcare System, Portland, Oregon, United States.
Abstract
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. Schattauer GmbH Stuttgart.
RCT Entities:
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. Schattauer GmbH Stuttgart.
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