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Nursing workload has long been recognized as a critical but under-theorized determinant of patient safety. This conceptual systems article reframes workload not as a static staffing metric but as a dynamic, measurable safety signal whose temporal and structural characteristics can be modeled to detect emerging risk states before adverse events materialize. Drawing exclusively on peer-reviewed literature published, the manuscript synthesizes evidence that elevated workload correlates with missed care, falls, medication errors, and burnout, yet existing approaches remain fragmented across isolated predictive models or retrospective acuity tools.To address this architectural gap, the article introduces the TASK-RISK framework—a novel, task-structured orchestration infrastructure that decomposes clinical activities into granular, temporally anchored units, fuses them into composite safety signals, and propagates those signals through a closed-loop detection topology. The framework is purely conceptual, specifying layer definitions, feedback mechanisms, and interpretive mathematical formalisms without empirical training or performance claims. Its five-layer architecture—task acquisition, workload quantification, signal generation, risk propagation, and governance feedback—operates entirely within existing electronic health record and sensor infrastructures, thereby offering a scalable blueprint for proactive safety governance. Theoretical implications for clinical deployment, ethical oversight, and system drift management are delineated. The manuscript establishes workload as a first-class safety signal and supplies the infrastructural scaffolding required for its integration into next-generation healthcare analytics platforms.