Agentic AI for Clinical Decision Orchestration in Healthcare Systems

Abstract

Healthcare delivery remains fundamentally human-driven, with clinicians responsible for coordinating decisions across time, specialties, and operational constraints. While predictive analytics and decision support tools are increasingly available, the burden of sequencing actions, revisiting decisions, managing escalation, and reconciling competing clinical and operational priorities continues to rest largely on human judgment. As care becomes more data-intensive and longitudinal, this reliance on manual coordination introduces growing challenges related to timeliness, consistency, cognitive load, and system-level resilience. Agentic artificial intelligence introduces a complementary capability that addresses these challenges by supporting goal-directed decision orchestration within human-driven healthcare systems. Rather than replacing clinical judgment, agentic AI enables structured planning, monitoring, and adaptation of decisions over time, operating within explicitly defined clinical, ethical, and regulatory boundaries. In this role, agentic systems act as a coordinating layer that augments human decision-making by maintaining decision context, tracking evolving conditions, and prompting timely reassessment or escalation when warranted. A constrained agentic framework is presented in which autonomy is carefully bounded by clinical scope, human oversight, and safety requirements. An accompanying evaluation perspective emphasizes decision trajectories, timeliness, escalation correctness, coordination efficiency, and safety adherence, reflecting dimensions of performance that matter in real-world, human-centered care delivery. By framing agentic AI as an augmentation to existing human-driven processes, this work positions agentic systems as a practical catalyst for improving coherence and adaptability in healthcare decision-making while preserving accountability and trust.

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