Introducing FRAM as a tool for surgical process modeling: a case study for understanding surgical scheduling complexity.

introductionSurgical processes are inherently variable leading to unexpected delays and cancellations. Due to inefficient and outdated processes, there is a need to understand and improve potential bottlenecks. However, traditional surgical process models (SPMs) focus on intraoperative procedures and sequential workflows, but most cannot adequately capture parallel workflows, variability, and sociotechnical interactions. We propose the use of Functional Resonance Analysis Method (FRAM) in conjunction with FRAMalyse to enable systematic measurement of variability propagation and potential process bottleneck identification. Additionally, we introduce a structured FRAM Model Interpreter (FMI) cycle-mapping interpretation protocol that maps function activations across cycles to temporal process phases, an approach for interpreting FRAM simulation outputs that has not been used in prior qualitative applications of the method. MATERIAL AND

methodsBased on stakeholder interviews and workflow observations, we developed a FRAM model of the daily surgical scheduling process at University Hospital Technical University of Munich (TUM). FMI profiles for each function were determined based on expert interviews and function activations were recorded during model simulation. Additionally, the model was analyzed using FRAMalyse to identify functions with high variability potential arising from their coupling structure, thereby providing a basis for future bottleneck validation and surgical scheduling optimization.

resultsWe identified 98 interconnected FRAM functions across four agents and phases. Scheduling and rescheduling functions spanned multiple cycles, indicating continuous and reciprocal coordination. Surgical team allocation function showed the highest variability, while attending surgeons performed the most functions and central patient management (CPM) had the highest coupling density.

conclusionThis study introduces the first quantitative application of FRAM and FRAMalyse in the healthcare domain, systematically identifying candidate bottlenecks across the full surgical scheduling pathway as a foundation for process improvement.