Safety model borrowed from industry reveals why surgical schedules collapse

Every morning in hospitals around the world, a quiet battle unfolds over the operating room schedule. Surgeries are moved, postponed, or canceled outright as staff shortages, delayed patients, missing test results, and shifting surgeon preferences collide in real time. The scale of the problem is striking: between 10 and 40 percent of scheduled procedures are delayed or canceled, driving up patient waiting times, staff overtime, and hospital costs. Now a research team at the Technical University of Munich has taken an unusual approach to understanding why these disruptions happen, borrowing a modeling method originally developed to analyze industrial disasters and applying it, for the first time in quantitative form, to the daily choreography of surgical scheduling.

The method is called the Functional Resonance Analysis Method, or FRAM, and it was created by safety researcher Erik Hollnagel to study complex sociotechnical systems in which humans, organizations, and technologies interact in ways that no flowchart can fully capture. Rather than describing a process as a fixed sequence of steps, FRAM represents it as a network of interdependent functions that can activate simultaneously and influence one another. Its central insight is that small, everyday variations in timing, resources, or behavior are normal and usually harmless, but when several of these variations align, they can resonate and amplify into major failures, much like structural resonance in engineering. In surgery, that resonance looks like a canceled operation caused by a sick nurse, a late pre-operative test, and an overrunning morning list all landing on the same day.

Traditional surgical process models, the formal or semi-formal representations used in computer-assisted surgery research, have struggled to capture this reality. Most focus narrowly on what happens inside the operating room, describe work as it is imagined in official protocols rather than work as it is actually done, and assume that workflows proceed sequentially. Real operating room management is nothing of the sort. It is a parallel, adaptive, multi-agent endeavor in which patients, residents, attending surgeons, and central patient management continuously negotiate and renegotiate the plan. The Munich team, led by Sidra Rashid of the MITI Research Group, argued that any scheduling model realistic enough to support AI-assisted planning must first capture this genuine complexity, and that FRAM offered a way to do it.

To build their model, the researchers spent months embedded in the visceral surgery department of University Hospital rechts der Isar. They conducted semi-structured interviews with twelve staff members, including central patient management staff, surgeons, residents, schedulers, coordinators, assistants, anesthetists, and nursing management, and complemented the interviews with direct shadowing during morning meetings, in operating rooms, and in informal staff interactions. This combination proved essential: the observations revealed informal practices and operational challenges that never surfaced in the formal interviews, such as surgeon preferences and meeting-based coordination, exactly the work-as-done details that conventional models omit.

From this evidence, the team constructed a FRAM model containing 98 unique interconnected functions spanning four central human agents, the patient, the medical resident, the attending surgeon, and central patient management, along with a fifth systemic agent representing the scheduling software infrastructure. Each function is described by six aspects: the input that triggers it, the output it produces, the preconditions that must be met, the resources it requires, the controls or rules that govern it, and the timing constraints that bound it. Functions are linked through these aspects, so that one function’s output becomes another’s input, resource, or precondition. This web of couplings is precisely how variability propagates through the system. The model was organized into four temporal phases, from the initial patient visit through the day of surgery, and validated iteratively with the interviewed staff.

The genuinely novel step came next. Using the FRAM Model Interpreter, a simulation tool that performs cycle-by-cycle analysis of which functions can activate, the team ran the model through ten simulation cycles, mapping each cycle onto the temporal phases of the scheduling process. They also introduced a structured interpretation protocol that assigns each function a profile specifying which inputs, preconditions, resources, and timing requirements must be satisfied for activation, using logical rules such as all, any, or none. Clinical functions like examining and consulting a patient demanded complete information before activating, while coordination functions like planning the surgical schedule used flexible profiles, reflecting the improvisational nature of real scheduling work. The researchers say this cycle-mapping interpretation approach had not been used in prior qualitative applications of FRAM.

The simulation results were revealing. The very first cycle activated 58 functions simultaneously, fourteen entry functions and forty-four background functions, underscoring how much of surgical scheduling begins in parallel rather than in sequence. Coordination functions such as planning the schedule and rescheduling surgeries activated repeatedly across multiple cycles, demonstrating that scheduling is iterative and adaptive rather than a one-time decision. Notably, a four-cycle gap emerged between the initial planning of the schedule in cycle four and the final provision of the daily schedule in cycle eight, a window filled with repeated team allocation, discussion, and adaptation that the analysis identified as the primary bottleneck in finalizing the schedule.

The simulation also exposed how disruptions compound. A disruptive entry function representing a staff member calling in sick recurred in cycles two, four, six, and eight, and each time it coincided with rescheduling and cancellation functions, tracing a direct causal pathway from staff unavailability to schedule upheaval. Three separate delay functions, originating with surgeons, patients, and residents, co-activated in multiple cycles, indicating that delays from different sources stack and amplify one another rather than occurring independently. This is functional resonance made visible: ordinary variability in several places at once producing an outsized systemic effect.

To move beyond qualitative description, the team applied FRAMalyse, an open-source analysis tool, to compute quantitative measures of variability across the coupling network. The Overall Functional Coupling Variability metric integrates network centrality measures, including Katz, in-closeness, out-closeness, and betweenness centrality, to rank functions by their potential to contribute to resonance. The results were unambiguous: allocating the operating room team showed the highest variability of any function in the entire model, burdened by a dense web of upstream dependencies including staff availability, surgery characteristics, team qualifications, documentation, and meeting outcomes, combined with constraints on training, skills, preferences, and shift composition. Attending surgeons performed the most functions overall, while central patient management showed the highest coupling density, reflecting its role in implementing decisions made by clinicians and patients. The patient visit phase emerged as the most functionally dense stage, where key surgical decisions are made through direct patient-surgeon interaction.

The authors are careful to note the limits of this first quantitative application. The model covers a single department in one hospital, the simulation represents just one possible instantiation of system behavior, and the weighting factors within the variability metric are not yet empirically validated, so the bottleneck ranking should be read as indicative rather than definitive. No external validation against observed disruption data has yet been performed. But the roadmap is ambitious: the team plans to validate the candidate bottlenecks against historical scheduling data, compare FRAM against alternative modeling formalisms such as BPMN and Petri nets, extend the model across the full perioperative pathway, integrate AI-based scheduling systems, and ultimately develop dynamic long-term simulations that could evolve into a digital twin of hospital operations. If that vision is realized, a safety method born in industrial risk analysis could become a predictive decision-support tool, helping hospitals design schedules that bend with reality instead of breaking under it.

Subject of Research: Quantitative application of the Functional Resonance Analysis Method to model surgical scheduling complexity and identify process bottlenecks

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

Article References: Rashid, S., Weber, R., Geiger, A., Wagner, L., Bernhard, L., Spicker, E., Jell, A., Fottner, J., Wilhelm, D., & Grabbe, N. (2026). Introducing FRAM as a tool for surgical process modeling: a case study for understanding surgical scheduling complexity. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-026-03800-2

Image Credits: AI Generated

DOI: 10.1007/s11548-026-03800-2

Keywords: FRAM, surgical scheduling, operating room management, surgical process modeling, functional resonance, hospital operations, variability propagation, bottleneck identification, sociotechnical systems, digital twin, computer-assisted surgery, patient safety