JMIR Publications

ABSTRACT

Violence risk assessment in forensic psychiatry is increasingly structured and evidence based, yet most approaches still organize information around the probability of a future violent outcome. This framing is useful for estimating risk but less explicit about how a patient moves from stability to deterioration, acute vulnerability, violence, and recovery. We propose a complementary framework based on Unified Modeling Language (UML) behavioral state machines. The model represents a patient through concurrent, orthogonal regions describing psychiatric illness phase, emotional and behavioral regulation, and physical or virtual context. At any moment, the patient is represented by an active state configuration composed of one state from each region. Events and continuously evaluated change conditions trigger transitions, allowing both escalation and recovery to be modeled explicitly. We further connect the state model to a risk management framework in which active state configurations constitute candidate risk items that can be assessed according to likelihood and impact, prioritized on a qualitative risk matrix, and linked to mitigation, contingency, ownership, and monitoring procedures. In a future informatics implementation, longitudinal electronic health record data and natural language processing could support state inference, transition prediction, and explainable artificial intelligence while preserving clinician oversight. The framework is not a validated violence risk instrument and is not intended to replace structured professional judgment. Its value is instead conceptual and computational: it changes the unit of analysis from a single adverse end point to an evolving trajectory that can be monitored and potentially interrupted. We outline a validation agenda for state definitions, transition mechanisms, risk prioritization, calibration, interpretability, and clinical utility.