A Predict → Confirm → Act Framework for AI-Augmented MDRO Control Across Hospitals and Long-Term Care Facilities

Multidrug-resistant organism (MDRO) control depends on identifying colonized patients and emerging transmission events early enough to intervene, without overwhelming infection prevention and control (IPC) teams or unnecessarily isolating patients. Predictive models using electronic health records, microbiology data, patient movement, and genomic information may support this task. We propose a practical Predict → Confirm → Act framework for artificial intelligence (AI)-augmented infection control across hospitals and long-term care facilities. In this framework, AI generates risk signals; microbiology, genomic data, and epidemiological review provide confirmation; and IPC teams apply proportionate actions such as targeted screening, preemptive precautions, cohorting, environmental investigation, or de-escalation. The framework is model-agnostic and is intended to shift attention away from algorithmic performance alone toward bedside and unit-level decisions. Safe implementation requires local threshold calibration, named alert ownership, microbiological confirmation pathways, de-escalation rules, prospective monitoring, and evaluation using IPC-relevant outcomes, including additional carriers detected, time to isolation, MDRO acquisition, isolation days avoided, alert burden, and unintended consequences.