Abstract
Abstract
Oral disease and malnutrition are common and closely linked problems in long-term care (LTC). Monthly weights, occasional diet reviews, and infrequent dental assessments can miss gradual decline. Recent tools, including computer-vision meal-intake estimation and smartphone-based gingival screening, create an opportunity for more timely clinical monitoring when limited data capture is embedded into routine care. This viewpoint proposes a nursing-led policy framework for AI-enabled oral and nutrition risk detection in nursing homes. The framework emphasizes protected oral health and nutrition champion roles, a practical 48-hour bedside assessment standard for high-priority operational alerts, standards-based electronic health record (EHR) integration, and prevention-oriented escalation pathways. We clarify that the proposed approach does not require a single black-box AI risk score. Instead, AI-derived measurements, such as estimated intake, plate-waste ratio, deviation from baseline, and image-based oral findings, can be combined with weight trends, EHR data, operational thresholds, and nurse review. The playbook specifies staged rollout, staff-facing alert outputs, fidelity checks for data capture, fallback documentation options for facilities with lower digital maturity, and key performance indicators for clinical outcomes, workflow burden, equity, and cost. Ethical safeguards include layered consent, minimum-necessary capture, opt-out recording, explainability for residents and proxies, and subgroup monitoring. AI-enabled clinical monitoring can support earlier action in LTC only if it is embedded in nursing workflows, auditable documentation, and accountable governance. Prospective, co-designed implementation studies are needed to test feasibility, workload, effectiveness, and equity across diverse LTC settings.