A randomized simulation trial evaluating ABiMed, a clinical decision support system for medication reviews and polypharmacy management

TL;DR

ABiMed, a clinical decision support system for medication reviews, based on the implementation of the STOPP/START v2 guidelines and on the visual presentation of aggregated drug knowledge using tables, graphs and flower glyphs can help pharmacists when they perform medication reviews.

Abstract

Background: Medication review is a structured interview of the patient, performed by the pharmacist and aimed at optimizing drug treatments. In practice, medication review is a long and cognitively-demanding task that requires specific knowledge. Clinical practice guidelines have been proposed, but their application is tedious. Methods: We designed ABiMed, a clinical decision support system for medication reviews, based on the implementation of the STOPP/START v2 guidelines and on the visual presentation of aggregated drug knowledge using tables, graphs and flower glyphs. We evaluated ABiMed with 39 community pharmacists during a randomized simulation trial, each pharmacist performing a medication review for two fictitious patients without ABiMed, and two others with ABiMed. We recorded the problems identified by the pharmacists, the interventions proposed, the response time, the perceived usability and the comments. Pharmacists' medication reviews were compared to an expert-designed gold standard. Results: With ABiMed, pharmacists found 1.6 times more relevant drug-related problems during the medication review (p=1.1e-12) and proposed better interventions (p=9.8e-9), without needing more time (p=0.56). The System Usability Scale score is 82.7, which is ranked "excellent". In their comments, pharmacists appreciated the visual aspect of ABiMed and its ability to compare the current treatment with the proposed one. A multifactor analysis showed no difference in the support offered by ABiMed according to the pharmacist's age or sex, in terms of percentage of problems identified or quality of the proposed interventions. Conclusions: The use of an intelligent and visual clinical decision support system can help pharmacists when they perform medication reviews. Our main perspective is the validation of the system in clinical conditions.

Figures (6)

• Figure 1: Screenshots of ABiMed's tabs for decision support. A: table summarizing current dosages and recommended ones, taking into account age, renal and hepatic status, indications and drug associations; B: adverse effects profile expected with the entire drug order, presented as flower glyphs; C: drug interactions, presented as a radial graph; D: STOPP/START rules detected.
• Figure 2: Bar plot showing the percentage of problems identified, for each age class. Bar width is proportional to the number of pharmacists.
• Figure 3: Bar plot showing the CLEO score ratio, without or with ABiMed (left and right), for each clinical case (A-D).
• Figure 4: Bar plot showing the response time, without or with ABiMed, for each age class. Bar width is proportional to the number of pharmacists.
• Figure 5: Bar plot showing the mean percentage of problems identified according to the order of passage of the four clinical cases.