Operationalizing AI-Assisted Polypharmacy Review in Post-Acute and Long-Term Care: A 4Ms-Based, NP-Led Workflow.

Polypharmacy in post-acute and long-term care (PA/LTC) is common and is associated with falls, delirium, hospitalization, functional decline, and mortality. In these settings, clinicians must identify drug-drug interactions (DDIs), potentially inappropriate medications (PIMs), renal dosing concerns, and medication contributors to new symptoms under time pressure and during frequent care transitions. We describe a nurse practitioner-led, 4Ms-aligned workflow in which artificial intelligence (AI) is used only for preliminary signal generation and triage, followed by mandatory verification using authoritative medication information resources and pharmacist review in predefined high-risk situations. The workflow is activated by routine clinical triggers, including new symptoms, new medication orders, hospital discharge reconciliation, initiation of potentially interacting therapies, geriatric syndromes, renal function change, and scheduled periodic medication review. AI-supported screening uses patient-specific inputs to generate a brief prioritized list of candidate medication safety issues, including possible DDIs, PIMs, adverse drug events, and renal dosing concerns. All clinically meaningful signals require human verification before action. High-risk scenarios, including QT-risk combinations, strong cytochrome P450 interactions, ritonavir-related interactions, renal dosing uncertainty, narrow therapeutic index medications, and extreme polypharmacy in frail patients, require pharmacist escalation. A combined clinical example demonstrated feasibility in 2 scenarios: identification of a likely medication contributor to hypersalivation and management of psychotropic interaction risk during nirmatrelvir/ritonavir treatment. This workflow offers a pragmatic and transferable model for integrating AI into medication safety in PA/LTC while preserving clinician accountability, interprofessional safeguards, and structured documentation.