AUTONOMOUS MULTI-AGENT AI FRAMEWORK FOR END-TO-END CLINICAL DECISION SUPPORT AND CARE COORDINATION

Abstract

Current clinical decision support (CDS) systems are usually integrated into electronic health record (EHR) applications and primarily focus on clinical diagnostics and treatment recommendations. In contrast, the developed autonomous multi-agent AI framework extends CDS capabilities to all aspects of patient candidate care across the entire care continuum. From a first-principles perspective, multi-agent systems (MASs) are defined, and the key applications of MASs in healthcare are outlined. CDS systems support every clinical decision that frontline clinicians must make during health system interactions, yet the sheer number of decisions constitutes an insurmountable barrier for human intelligence alone. Extensive decision support for every frontline interaction in a health system is provided by an autonomous MAS with the stated capabilities in a principled objective manner. Autonomous MASs represent a new paradigm for end-to-end CDS. The canonical two- to five-way integration by clinical service line forms the theoretical foundation linking application breadth and the need for such an autonomous end-to-end MAS. Autonomy extends efforts in centralized and multi-level support and achieves an entirely new level of CDS candidacy, covering every interaction with a health system throughout the health continuum. As an end-to-end solution utilizing every available EHR data source across the full data richness span, it closes the big data, explainability, and big data yet little intelligence per interaction chunks associated with complimentary integration. The design addresses safety, reliability, explainability, and trust—all elements of the human operating picture. Validation, while a near impossibility in another context, is the master fulcrum of root cause evidence-based maturity, independent of outcome and thus spare to the routine.