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The integration of generative artificial intelligence into clinical documentation workflows promises substantial efficiency gains yet introduces persistent misalignment between AI-generated drafts and individual clinician judgment. This conceptual systems research article advances a novel human-in-the-loop adaptation theory that explicitly models clinician preferences as dynamic, context-sensitive inputs rather than static constraints. Drawing on peer-reviewed evidence, the manuscript synthesizes how preference elicitation, real-time adaptation, and closed-loop governance can transform AI-assisted drafting from a supplementary tool into a co-evolutionary clinical intelligence infrastructure.Central to the contribution is the introduction of the clinician preference orchestration and adaptation framework (CPOAF), a layered architectural model featuring four interdependent strata and a star-topology feedback mechanism that propagates preference drift signals radially from peripheral clinician nodes to a central orchestration engine. Three interpretive mathematical constructs—decision confidence, monitoring burden, and drift sensitivity—are formalized to guide theoretical deployment without empirical benchmarking.The framework addresses governance constraints, data-modality specificity, and deployment-environment heterogeneity while preserving clinician autonomy. By foregrounding preference modeling as the core adaptive mechanism, CPOAF offers a scalable infrastructural blueprint for next-generation AI-assisted drafting systems that remain clinically grounded, ethically defensible, and institutionally sustainable. Implications extend to health-system informatics, regulatory science, and human-centered AI design.

Septic shock, defined as sepsis with persistent hypotension despite adequate fluid resuscitation and requiring vasopressors, has a mortality rate of 30–50% despite modern treatment. Intravenous fluids remain the cornerstone of early therapy, with guidelines recommending at least 30 mL/kg of crystalloids within the first three hours. However, both insufficient and excessive fluid administration can be harmful, making individualized, data-driven management essential. Reinforcement learning (RL) has been proposed to optimize fluid and vasopressor dosing in sepsis using retrospective ICU data. While models such as the AI Clinician suggest potential survival benefits, they often prioritize long-term outcomes like mortality and overlook short-term harms such as fluid overload and organ injury, raising safety concerns. Safety constraints and harm-aware reward design are essential in RL systems for septic shock. Pure outcome optimization is insufficient, and clinical AI must include mechanisms to prevent unsafe actions and ensure adherence to safety limits. Offline RL is vulnerable to distributional shift and unsafe extrapolation. Reward functions focused only on survival ignore acute complications, leading to unsafe policies. Human-in-the-loop oversight is necessary to maintain clinical accountability and enable intervention. RL systems should include action constraints, conservative learning with uncertainty estimation, and reward penalties for fluid overload indicators. Regulatory bodies and journals should require safety validation, and clinicians must retain override authority and transparency in decision-making. RL in septic shock management must prioritize patient safety through constraints, harm-aware rewards, and clinical oversight. Without these safeguards, deployment risks patient harm and loss of trust in clinical AI.

Non-urgent patient portal messages consume a substantial fraction of clinician time and can shift attention away from higher-acuity care. As portal communication becomes a routine part of ambulatory medicine, inbox management increasingly functions as an additional clinical workload. Current inbox workflows often depend on manual review, interpretation, and free-text reply by clinicians. This creates delays, variability, and cognitive burden, especially for routine requests that could be answered through standardized guidance. This article proposes a large language model agent that classifies incoming message intent, identifies non-urgent queries suitable for templated responses, and drafts a structured reply. The draft is grounded in local clinical protocols, physician-approved templates, and patient-specific information from the electronic health record. The proposed agent includes a message intent classifier, a protocol-and-template retrieval module, a retrieval-augmented drafting model, a rule-based safety filter, and a human-verification interface. These components work together to keep generated text within a clinically approved and auditable workflow. By preparing protocol-adherent draft responses for clinician review, the agent could reduce routine documentation effort while preserving clinician judgment. Its value depends on safety boundaries, template quality, patient-data integration, and seamless fit within the existing portal workflow. The agent represents a pragmatic near-term use of large language models in clinical administration. It supports automation without removing human oversight from patient-facing communication.