Abstract
A medication error is an inadvertent failure in the drug therapy process that can cause serious harm to patients by increasing morbidity and mortality and are associated with significant economic costs to the healthcare system. Medication reconciliation is the most cost-effective intervention and can result in a 66% reduction in medication errors. To improve patient safety, we developed a machine learning-based tool that prioritizes patients at risk of medication errors upon admission to the hospital to ensure that they undergo medication reconciliation by clinical pharmacists. The data were collected from the electronic health records of patients admitted to Reims University Hospital who underwent medication reconciliation between 2017 and 2023. The data from 7200 patients were used to train four machine learning-based models based on 52 variables in the development dataset. These models were used to prioritize admitted patients according to their likelihood of being exposed to a medication error. Our models, particularly the voting classifier model, demonstrated good performance (a recall of 0.75, precision of 0.65, F1 score of 0.70, AUROC of 0.74 and AUCPR of 0.75). In a retrospective evaluation simulating real-life use, the voting classifier model successfully identified 45% of the total patients selected who were found to have at least one unintended discrepancy compared to 21% when using the existing tool. The positive experimental results of this tool showed a superior improvement of 113% over the existing tool by targeting patients at risk of medication errors upon admission to Reims University Hospital.