Healthcare systems struggle with incomplete, inaccurate medication lists across settings, creating safety and workflow challenges. The article highlights emerging AI approaches—natural language processing, machine learning and interoperability platforms—that aggregate pharmacy, EHR and patient-reported data to reconcile medication histories, surface dosing and adherence issues, and identify discrepancies. Vendors and health systems are piloting models that extract information from free-text notes, prescriptions and claims to build consolidated medication inventories and reduce manual reconciliation burden.
Proponents say AI-driven reconciliation can lower adverse drug events and free clinician time by prioritizing likely errors and suggesting corrections while enabling real-time decision support at the point of care. Experts caution that success requires rigorous validation, governance, transparent algorithms, high-quality data sources, patient consent and human oversight to avoid propagating errors and to ensure interoperability and measurable clinical impact before broad deployment.
