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Alarm fatigue in healthcare settings poses significant risks to patient safety, arising from excessive, non-actionable alerts that desensitize clinicians. This conceptual manuscript introduces a novel framework for mitigating alarm fatigue through context-aware suppression mechanisms, while rigorously adhering to safety constraints. Drawing on theoretical principles from systems engineering, human factors, and artificial intelligence, we propose the safety-integrated context-aware suppression topology (SICAST), a multi-layered architecture designed to dynamically filter alarms based on real-time contextual data such as patient physiology, environmental factors, and clinician workload. The framework incorporates feedback loops for continuous adaptation, ensuring suppression decisions prioritize risk minimization without compromising vigilance. Key components include a context aggregation layer, a suppression decision engine governed by safety thresholds, and an audit trail for governance. Interpretive formulas model risk propagation under suppression and decision confidence amid constraints. By synthesizing recent literature, we highlight how SICAST addresses gaps in existing approaches, such as static thresholding and a lack of contextual integration. This work advances conceptual designs for AI-driven healthcare systems, emphasizing infrastructural resilience and ethical deployment. Implications for system orchestration in critical care underscore the need for balanced alarm management to enhance patient outcomes and reduce clinician burden.

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.