Validation of a novel Artificial Pharmacology Intelligence (API) system for the management of patients with polypharmacy

Abstract

ObjectiveMedication management of patients with polypharmacy is highly complex. We aimed to validate a novel Artificial Pharmacology Intelligence (API) algorithm to optimize the medication review process in a comprehensive, personalized, and scalable way. Materials and methodsThe study was conducted on anonymized retrospective electronic health records (EHR) of 49 patients. Each patient's file was reviewed by the API system, a clinical pharmacist, and a judging committee. Validation was assessed by comparing the overall agreement of the judging committee (as the gold standard, blinded to the identity of the analyzer) to both the API system and clinical pharmacists' conclusions. Five medication-related problem (MRP) categories were assessed: duplication of therapy, age-related issues, incorrect dose, current side effects and future side effects' risk. For each category the overall validity parameters, agreement, positive predictive value (PPV), negative predictive value (NPV), sensitivity and specificity were analyzed. ResultsThe agreement between the API system and the judging committee was 93.5 % (95 % CI 92.7–94.4), while the agreement between the clinical pharmacists and the judging committee was 73.9 % (95 % CI 72.5–75.3). The PPV was 92.2 % (90.9–93.5) and NPV was 94.2 % (93.1–95.2) for the API system and 76.3 % (69.8–82.8) and 73.5 % (72.3–74.8) respectively for the clinical pharmacists. DiscussionAI systems can equip clinicians with sophisticated tools and scale manual processes such as comprehensive medication reviews, thus reducing MRPs and drug-related hospitalizations related to multidrug treatments. The API system validated in this study provided comprehensive, multidrug, multilayered analysis intended to bridge the innate complexity of personalized polypharmacy treatment. ConclusionsThe API system was validated as a tool for providing actionable clinical insights non-inferior to a manual clinical review of a clinical pharmacist. The API system showed promising results in reducing MRPs.