Niklas Grabbe

Introduction: Surgical 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 Due to inefficient and outdated processes, there is a need to understand and improve potential bottlenecks.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 methods: Based 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. Results: We 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. Conclusion: This 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.

Zusammenfassung: Jüngste Fortschritte in der Fahrzeugautomatisierung versprechen mehr Sicherheit und Effizienz. Gleichzeitig macht die sogenannte „Freigabefalle“ bestehende Lücken im Sicherheitsnachweis für hochautomatisiertes Fahren deutlich. Umso dringlicher sind neue Ansätze zur Erprobung und Risikobewertung, insbesondere im gemischten Verkehr. In diesem Kapitel wird die Functional Resonance Analysis Method (FRAM) auf ein Überholszenario auf einer Landstraße angewandt, um die Interaktionen zwischen menschlichen Fahrern und automatisierten Fahrzeugen zu untersuchen. Durch die Identifikation von Mustern, die zu Unfällen führen oder diese verhindern, soll die Systemstabilität verbessert werden. Zudem werden methodische Weiterentwicklungen von FRAM vorgestellt, darunter neue Kennzahlen zur Erfassung von Komplexität und Interaktion, die für ein praxisorientiertes Fachpublikum aufbereitet sind. Praktische Relevanz : Die Ergebnisse bieten konkrete Ansatzpunkte für die Gestaltung sicherer Mensch–Automation-Interaktionen im Straßenverkehr. Durch die Identifikation wiederkehrender Muster, die sowohl Risiken als auch Resilienzfaktoren abbilden, lassen sich gezielt Hebelpunkte für technische Unterstützung und Funktionsallokation zwischen Fahrer und Automation bestimmen. Damit können Entwickler frühzeitig einschätzen, welche Automatisierungsfunktionen sinnvoll sind und wo der Mensch unverzichtbar bleibt. Für Praktiker in Industrie und Regulierung eröffnet dies die Möglichkeit, komplexe Verkehrsdynamiken systemisch zu bewerten und Sicherheitsnachweise jenseits rein empirischer Testkilometer fundierter zu gestalten.

Recent advances in vehicle automation promise improved safety and efficiency, but the “approval trap” highlights gaps in safety evidence for highly automated driving. New approaches for testing and risk identification are needed, especially as automated vehicles operate in mixed traffic. This chapter applies the Functional Resonance Analysis Method (FRAM) to an overtaking scenario on a rural road, examining interactions between human drivers and automated vehicles. By identifying patterns that lead to accidents or prevent them, the chapter aims to enhance system stability. Methodological advancements in the FRAM, with new metrics for complexity and interaction, are presented for a practitioner-focused audience.

Very high levels of automation in road traffic are accompanied by the need for teleoperation. If the driverless autonomous vehicle is not able to solve a complex driving situation, then the problem should be solved by a teleoperated intervention by getting a human into the loop. Such a system is highly complex, interacting in a socio-technical system. Therefore, in addition to the empirical research of user-centered development of teleoperated driving, an analytical and systemic approach is crucial in the first place to understand the interrelationships between the subsystems and to optimize a teleoperated driving system. This work provides a base for the systemic consideration by taking two different perspectives focusing on the work organization and vehicle operation dealing with how many vehicles can be monitored and serviced by one operator. First, a brief analytical perspective of human reliability is taken, emphasizing the appropriate definition of the operational design domain and knowledge of its variability. Second, a comprehensive systemic, socio-technical perspective is applied using the functional resonance analysis method (FRAM) for a reflexive and computational purpose highlighting effects on operator workload and traffic performance. Finally, the implications of the research results are discussed and integrated into socio-technical interactions in everyday teleoperated driving between blunt- and sharp-end factors from a safety and reliability perspective to demonstrate the complex leverage of the work organization. Overall, the insights contribute to an enhanced understanding of the complex socio-technical interactions in teleoperated driving.

Over the past two decades, systemic-based risk assessment methods have garnered more attention, and their use and popularity are growing. In particular, the functional resonance analysis method (FRAM) is one of the most widely used systemic methods for risk assessment and accident analysis. FRAM has been progressively evolved since its starting point and is considered to be the most recent and promising step in understanding socio-technical systems. However, there is currently a lack of any formal testing of the reliability and validity of FRAM, something which applies to Human Factors and Ergonomics research as a whole, where validation is both a particularly challenging issue and an ongoing concern. Therefore, this paper aims to define a more formal approach to achieving and demonstrating the reliability and validity of an FRAM model, as well as to apply this formal approach partly to an existing FRAM model so as to prove its validity. At the same time, it hopes to evaluate the general applicability of this approach to potentially improve the performance and value of the FRAM method. Thus, a formal approach was derived by transferring both the general understanding and definitions of reliability and validity as well as concrete methods and techniques to the concept of FRAM. Consequently, predictive validity, which is the highest maxim of validation, was assessed for a specific FRAM model in a driving simulator study using the signal detection theory. The results showed that the predictive validity of the FRAM model is limited and a generalisation with changing system conditions is impossible without some adaptations of the model. The applicability of the approach is diminished because of several methodological limitations. Therefore, the reliability and validity framework can be utilised to calibrate rather than validate an FRAM model.

Automated driving promises great possibilities in traffic safety advancement, frequently assuming that human error is the main cause of accidents, and promising a significant decrease in road accidents through automation. However, this assumption is too simplistic and does not consider potential side effects and adaptations in the socio-technical system that traffic represents. Thus, a differentiated analysis, including the understanding of road system mechanisms regarding accident development and accident avoidance, is required to avoid adverse automation surprises, which is currently lacking. This paper, therefore, argues in favour of Resilience Engineering using the functional resonance analysis method (FRAM) to reveal these mechanisms in an overtaking scenario on a rural road to compare the contributions between the human driver and potential automation, in order to derive system design recommendations. Finally, this serves to demonstrate how FRAM can be used for a systemic function allocation for the driving task between humans and automation. Thus, an in-depth FRAM model was developed for both agents based on document knowledge elicitation and observations and interviews in a driving simulator, which was validated by a focus group with peers. Further, the performance variabilities were identified by structured interviews with human drivers as well as automation experts and observations in the driving simulator. Then, the aggregation and propagation of variability were analysed focusing on the interaction and complexity in the system by a semi-quantitative approach combined with a Space-Time/Agency framework. Finally, design recommendations for managing performance variability were proposed in order to enhance system safety. The outcomes show that the current automation strategy should focus on adaptive automation based on a human-automation collaboration, rather than full automation. In conclusion, the FRAM analysis supports decision-makers in enhancing safety enriched by the identification of non-linear and complex risks.