Summary of A Randomized Simulation Trial Evaluating Abimed, a Clinical Decision Support System For Medication Reviews and Polypharmacy Management, by Abdelmalek Mouazer et al.
A randomized simulation trial evaluating ABiMed, a clinical decision support system for medication reviews and polypharmacy management
by Abdelmalek Mouazer, Sophie Dubois, Romain Léguillon, Nada Boudegzdame, Thibaud Levrard, Yoann Le Bars, Christian Simon, Brigitte Séroussi, Julien Grosjean, Romain Lelong, Catherine Letord, Stéfan Darmoni, Karima Sedki, Pierre Meneton, Rosy Tsopra, Hector Falcoff, Jean-Baptiste Lamy
First submitted to arxiv on: 3 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning system called ABiMed has been designed to assist pharmacists in optimizing drug treatments through medication review. The system is based on implementation of clinical practice guidelines and visual presentation of aggregated drug knowledge using tables, graphs, and flower glyphs. In a randomized simulation trial, 39 community pharmacists used ABiMed to perform medication reviews for two fictitious patients, resulting in the identification of more relevant drug-related problems and better interventions. The system’s usability was also high, with an average score of 82.7 on the System Usability Scale. Pharmacists appreciated the visual aspect of ABiMed, which allows them to compare current treatments with proposed ones. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ABiMed is a special tool that helps pharmacists make better decisions when reviewing medications for patients. The system shows information about different drugs and lets pharmacists compare what they’re doing now with new ideas. In a test, 39 pharmacists used ABiMed to review two fake patient cases and found more important issues and came up with better solutions. People liked using the system because it was easy to understand and made their jobs easier. |
Keywords
* Artificial intelligence * Machine learning
