Assessment of a hybrid decision support system using machine learning with artificial intelligence to safely rule out prescriptions from medication review in daily practice

Background Medication review is time-consuming and not exhaustive in most French hospitals. We routinely use an innovative hybrid decision support system using Artificial Intelligence to prioritize medication review by scoring prescriptions by their risk of containing at least one drug related probl...

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Bibliographic Details
Published in:International journal of clinical pharmacy 2022-04, Vol.44 (2), p.459-465
Main Authors: Levivien, Clara, Cavagna, Pauline, Grah, Annick, Buronfosse, Anne, Courseau, Romain, Bézie, Yvonnick, Corny, Jennifer
Format: Article
Language:English
Subjects:
Analysis
Artificial Intelligence
Decision support systems
Deep learning
Drug-Related Side Effects and Adverse Reactions
Humans
Internal Medicine
Machine Learning
Medication Review
Medicine
Medicine & Public Health
Neonatology
Pharmacists
Pharmacy
Prescription writing
Prescriptions
Research Article
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Summary:Background Medication review is time-consuming and not exhaustive in most French hospitals. We routinely use an innovative hybrid decision support system using Artificial Intelligence to prioritize medication review by scoring prescriptions by their risk of containing at least one drug related problem (DRP). Aim Our aim was to attest that the prescriptions with low risk of DRPs ruled out by the tool in everyday practice were effectively free of any DRPs with potentially severe clinical impact. Methods We conducted a randomized single-blinded study to compare the rate of pharmaceutical interventions (PI) between low and high-risk prescriptions defined by the tool’s calculated score. Prescriptions were reviewed daily by a clinical pharmacist. Proportion of prescriptions with at least one severe DRP was calculated in both groups. Severe DRPs were characterized through a multidisciplinary approach. Results Four hundred and twenty (107 low score and 313 high score) prescriptions were analyzed. The percentage of prescriptions with severe DRPs was dramatically decreased in low score prescriptions (2.8% vs. 15.3% for high-risk; p  = 0.0248). A significant difference was found (94% vs. 20%; p  
ISSN:2210-7703
2210-7711
DOI:10.1007/s11096-021-01366-4