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.
Peritoneal Dialysis (PD) is a home-based therapy for kidney failure that requires patients to independently perform detailed sterile procedures, often several times per day. Even minor deviations in technique can lead to serious complications, including peritonitis and catheter failure. Although structured education programs are typically available, variations in training quality, health literacy, home environments, and patient confidence continue to contribute to preventable harm. Immersive Virtual Reality (VR) and Artificial Intelligence (AI) present promising opportunities to enhance PD education. However, their implementation must be grounded in patient safety principles and Systems Engineering approaches rather than driven solely by technological advancement. This paper presents a patient-focused, systems-based framework for AI-enhanced VR training in home PD, informed by human-centereddesign. The framework integrates realistic procedural simulations with AI-driven feedback on sequencing and sterile technique, while modeling the complete PD workflow within the home as a safety-critical care environment. Core elements include co-design with patients and PD nurses, identification of high-risk procedural steps, adaptation to varying literacy levels, transparent AI feedback mechanisms, and structured processes for ongoing monitoring and evaluation. Interdisciplinary collaboration among clinicians, human factors experts, AI developers, and patient representatives is essential to ensure safe, effective, and scalable implementation aimed at reducing preventable complications and strengthening patient confidence.
Sarah Ahmed Alkindi, Saed Amer, Mecit Can Emre Simsekler et al.· AHFE International· 0 citations