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 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 < 0.001) in the percentage of severe DRPs detected by the hybrid approach compared to a CDSS. During the study period, the hybrid tool allowed to rule out 55% of all prescriptions in our hospital. Conclusion This hybrid decision support tool has shown to be accurate to detect DRPs in daily practice. Despite some limitations, it offers the best possible solution to prioritized medication review, considering the shortage of clinical pharmacists in France and considerably improves the safety of patients’ care.