Human-Centered Sepsis Management in Clinical Work Systems: A Socio-Technical AI Framework for Patient Safety
TL;DR
A human-centered, systems-based agentic AI architecture for sepsis risk modeling and proactive clinical management that embeds predictive intelligence within the broader socio-technical work system, enabling closed-loop monitoring, coordination of safety-critical tasks, and feedback-driven adaptation.
Abstract
Sepsis is a time-critical condition associated with substantial morbidity and mortality, where delays in recognition and treatment markedly worsen outcomes. Although machine learning models show promise for early detection, their clinical impact has been constrained by poor integration into workflows, limited interpretability, and insufficient support for coordinated action. This study introduces a human-centered, systems-based agentic AI architecture for sepsis risk modeling and proactive clinical management. Rather than generating static risk scores, the system continuously interprets evolving patient data, situates risk within the clinical workflow, and supports timely, clinician-supervised interventions. Grounded in systems engineering and guided by the Systems Engineering Initiative for Patient Safety (SEIPS) framework, the architecture embeds predictive intelligence within the broader socio-technical work system, enabling closed-loop monitoring, coordination of safety-critical tasks, and feedback-driven adaptation. By reframing sepsis prediction as an adaptive, workflow-aware safety intervention, this approach advances AI from passive decision support toward an accountable, action-oriented partner in care delivery while preserving clinician oversight.