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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.