STAMPing out MRI Simulation Hazards with a System-Theoretic Accident Model and Processes Approach to Proactive Hazard Assessment

Abstract

Current approaches to MRI safety include 1) implementation of local policies that comply with national guidelines, and 2) utilization of failure mode and effects analysis to proactively identify hazards. Both approaches focus on the frontline clinical workflow, which is important for daily practice, but neglect the wider system. System-Theoretic Accident Model and Processes (STAMP) is a systems theory-based paradigm that has been successfully applied to other industries to study the safety of complex systems. We explored if STAMP-based techniques can be applied to the system of MRI simulator use in radiation oncology to develop a generalizable system model for use in a proactive hazard assessment. We hypothesized that taking this approach would proactively identify safety hazards beyond the frontline. The system surrounding a 3-Tesla MRI simulator was considered prior to clinical implementation of this technology in radiation oncology at a single institution in the United States. Utilizing a STAMP-based tool, System-Theoretic Process Analysis (STPA), the system was modeled at multiple levels of abstraction. Interactions between system components were modeled as control loops. The analysis was conducted by a multidisciplinary team with expertise in systems engineering, medical physics, MR physics, and clinical radiation oncology. A high-level system model in the form of a safety control structure (SCS) to enable MRI simulation was developed. Areas of inadequate control and feedback were identified at multiple levels (Table 1), including those beyond the frontline. 317 unsafe control actions (UCAs) were identified. The development of a SCS revealed areas of inadequate feedback and enabled the identification of over 300 UCAs, highlighting the complexity of the sociotechnical system surrounding MRI simulation. The majority of UCAs surrounded RT simulation and planning, aspects of the system with greater complexity. By identifying UCAs prior to clinical implementation, this analysis enabled a proactive approach to MRI safety. By utilizing abstraction, the model can be applied to other centers. Future work is needed to mitigate circumstances leading to UCAs.Abstract 2459; TableIdentified control actions and unsafe control actionsControllerControl actionsUnsafe control actionsDevice manufacturers5 (e.g., “Install MRI”, “Refill helium”)24Executive, Management15 (e.g., “Approve hiring”, “Approve access”)94Radiotherapy (RT) simulation, planning37 (e.g., “Immobilize”, “Apply RF coil”)199 Open table in a new tab