Heidi S Kramer1, Bryan Gibson2, Yarden Livnat3, Iona Thraen4, Abraham A Brody5, Randall Rupper6. 1. HSR&D, George E Whalen Salt Lake City VA Medical Center, Salt Lake City, UT; Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT; Department of Biomedical Informatics, University of Utah, Salt lake City, UT. 2. Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT; IDEAS 2.0 Center George E Whalen VA Medical Center, Salt Lake City, UT. 3. Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT; Scientific Computing and Imaging Institute, University of Utah. 4. HSR&D, George E Whalen Salt Lake City VA Medical Center , Salt Lake City, UT. 5. James J Peters Bronx VA Medical Center GRECC, Bronx, NY; Hartford Institute for Geriatric Nursing at the NYU College of Nursing, New York, NY. 6. George E Wahlen Salt Lake VA Medical Center, Geriatrics Research Education and Clinical Center; Department of Geriatrics University of Utah School of Medicine, Salt Lake City, UT.
Abstract
OBJECTIVES: Transitions in patient care pose an increased risk to patient safety. One way to reduce this risk is to ensure accurate medication reconciliation during the transition. Here we present an evaluation of an electronic medication reconciliation module we developed to reduce the transition risk in patients referred for home healthcare. METHODS: Nineteen physicians with experience in managing home health referrals were recruited to participate in this within-subjects experiment. Participants completed medication reconciliation for three clinical cases in each of two conditions. The first condition (paper-based) simulated current practice - reconciling medication discrepancies between a paper plan of care (CMS 485) and a simulated Electronic Health Record (EHR). For the second condition (electronic) participants used our medication reconciliation module, which we integrated into the simulated EHR. To evaluate the effectiveness of our medication reconciliation module, we employed repeated measures ANOVA to test the hypotheses that the module will: 1) Improve accuracy by reducing the number of unaddressed medication discrepancies, 2) Improve efficiency by reducing the reconciliation time, 3) have good perceived usability. RESULTS: The improved accuracy hypothesis is supported. Participants left more discrepancies unaddressed in the paper-based condition than the electronic condition, F (1,1) = 22.3, p < 0.0001 (Paper Mean = 1.55, SD = 1.20; Electronic Mean = 0.45, SD = 0.65). However, contrary to our efficiency hypothesis, participants took the same amount of time to complete cases in the two conditions, F (1, 1) =0.007, p = 0.93 (Paper Mean = 258.7 seconds, SD = 124.4; Electronic Mean = 260.4 seconds, SD = 158.9). The usability hypothesis is supported by a composite mean ability and confidence score of 6.41 on a 7-point scale, 17 of 19 participants preferring the electronic system and an SUS rating of 86.5. CONCLUSION: We present the evaluation of an electronic medication reconciliation module that increases detection and resolution of medication discrepancies compared to a paper-based process. Further work to integrate medication reconciliation within an electronic medical record is warranted.
OBJECTIVES: Transitions in patient care pose an increased risk to patient safety. One way to reduce this risk is to ensure accurate medication reconciliation during the transition. Here we present an evaluation of an electronic medication reconciliation module we developed to reduce the transition risk in patients referred for home healthcare. METHODS: Nineteen physicians with experience in managing home health referrals were recruited to participate in this within-subjects experiment. Participants completed medication reconciliation for three clinical cases in each of two conditions. The first condition (paper-based) simulated current practice - reconciling medication discrepancies between a paper plan of care (CMS 485) and a simulated Electronic Health Record (EHR). For the second condition (electronic) participants used our medication reconciliation module, which we integrated into the simulated EHR. To evaluate the effectiveness of our medication reconciliation module, we employed repeated measures ANOVA to test the hypotheses that the module will: 1) Improve accuracy by reducing the number of unaddressed medication discrepancies, 2) Improve efficiency by reducing the reconciliation time, 3) have good perceived usability. RESULTS: The improved accuracy hypothesis is supported. Participants left more discrepancies unaddressed in the paper-based condition than the electronic condition, F (1,1) = 22.3, p < 0.0001 (Paper Mean = 1.55, SD = 1.20; Electronic Mean = 0.45, SD = 0.65). However, contrary to our efficiency hypothesis, participants took the same amount of time to complete cases in the two conditions, F (1, 1) =0.007, p = 0.93 (Paper Mean = 258.7 seconds, SD = 124.4; Electronic Mean = 260.4 seconds, SD = 158.9). The usability hypothesis is supported by a composite mean ability and confidence score of 6.41 on a 7-point scale, 17 of 19 participants preferring the electronic system and an SUS rating of 86.5. CONCLUSION: We present the evaluation of an electronic medication reconciliation module that increases detection and resolution of medication discrepancies compared to a paper-based process. Further work to integrate medication reconciliation within an electronic medical record is warranted.
Entities:
Keywords:
Medication reconciliation; electronic medical records; home health agency referrals; medical transition care; patient safety
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