CARE: A Consulting, Acquiring, Resource-aware, Explaining Agent for Clinical Decision Support

Clinical decision support is based on a record in progress: tests are ordered as the diagnosis evolves, each with medical resource consumption, and the diagnosis process is personal per patient condition. We present CARE, a generic framework for this setting. A reinforcement learning agent for predicting medical outcome, trained on the medical center's own patients, works in a human-like process, deciding per patient which test to order next, when to consult a medical language model, and when to stop and predict, under test-specific costs and turnaround times and within resource budget limits. The language model supplies medical knowledge and a learned fusion model weighs its assessment by the evidence behind it and fuses it into the locally trained prediction. Every episode is rendered into a clinical account of what was acquired and what it showed, so a clinician can see how the recommendation was reached. On MIMIC-IV, CARE reaches higher accuracy than dynamic acquisition agents and static test rankings at an earlier diagnosis stage, while keeping its workups individual to the patient. The framework is generic and can be applied to any medical data and any medical outcome, a holistic approach that combines local learning, medical knowledge, resource awareness, and self-explanation in one system.