Responsible AI for safer opioid risk management in older adults

Older adults face elevated opioid-related risks driven by multimorbidity, altered pharmacokinetics, and polypharmacy. We present a hybrid digital health framework that integrates a Long Short-Term Memory (LSTM) network for temporal risk prediction with a Retrieval-Augmented Generation (RAG) module for evidence-grounded clinical explanation tailored to adults aged ≥65 years. Using de-identified Prescription Drug Monitoring Program (PDMP) data from 2016 to 2022, the system predicts opioid risk classification, Beers medication safety level, and potentially inappropriate medication (PIM) status, achieving macro-F1 scores of 0.72, 0.77, and 0.84, respectively. To support interpretability and safety, explanations are generated from a curated clinical knowledge base incorporating the Centre for Disease Control (CDC) opioid guidance, the American Geriatrics Society (AGS) Beers Criteria, and geriatric pharmacotherapy literature. Retrieval-grounded reasoning, uncertainty signaling, and clinician-review cautions are embedded to mitigate unsupported inferences and automation bias. The framework is designed to align with PDMP-style data flows and incorporates Social Vulnerability Index (SVI) context to account for area-level determinants relevant to opioid stewardship. By coupling temporal prediction with verifiable, guideline-grounded explanations, this work illustrates an operational, responsible-AI design approach for transparent and clinician-centered decision support in opioid management for ageing populations.