A dynamic version of the FRAM for capturing variability in complex operations

Raising of problem. The methodology presented in this study is aimed at modeling the variability of the technical condition when operating a car in a complex social and technical system "car − person − environment". This allows you to simulate the change in the operational properties of the car depending on its operation, affecting the change in the technical condition by the Functional Resonance Analysis Method (FRAM). Purpose. The variability of the technical operation of the car needs to be managed, and not reduced or eliminated, since this is an advantage, as well as conditions for the safety and functioning of complex socio-technical systems. To control it, you must first monitor the variability of the vehicle's technical operation. A set of relevant indicators needs to be identified and developed. Their interpretation makes it possible to draw conclusions about the level of technical condition, operational properties of the vehicle and traffic safety and supports the prediction of the occurrence of future events (failure occurrence). Conclusion. Thus, the basic principles of the FRAM for modeling the variability of the technical condition when operating a car in a complex social and technical system "car − person − environment" have been determined. The relationship has been established between the definition of traffic safety, the FRAM model and the performance indicators of the vehicle. The developed model of the FRAM for maintenance of the undercarriage of cars, consisting of a set of functions that describe the actions taken to maintain cars to ensure their good condition. A block diagram of the implementation of functionally oriented technologies for the maintenance and repair of cars has been developed, taking into account the design features and the level of technology for technical influences. This allows you to objectively determine the required scope of maintenance work, depending on the results of diagnostics and the availability of technological equipment.

The functional resonance analysis method (FRAM) is a system-based method to understand highly complex sociotechnical systems. Besides learning from safety occurrences or undesirable states, FRAM can be used to understand how things go well in a system, by identifying gaps between “work as imagined” (WAI) and “work as done” (WAD). FRAM is increasingly used in many domains and can enhance our understanding of a complex system and proposes strategies to refine the work design. This systematic review identified 108 FRAM research papers from 2006–2019. Most of these papers were conducted by European researchers and employed qualitative methods such as document analysis, interviews, and focus groups with subject matter experts (SMEs) and observations to develop WAI and WAD. Despite being used in healthcare, construction, and maritime sectors among others, aviation was the most commonly explored domain in FRAM studies. The 26 FRAM studies in aviation explored many aspects of the aviation industry, including Air Traffic Control (ATC) systems, cockpit operation, ground handling, maintenance, and a range of past safety incidents, like runway incursions. This paper also characterises the FRAM studies focused on aviation in terms of the common methods and steps used to build FRAM and the available software tools to build FRAM nets. Current FRAM illustrates its advantages in capturing the dynamic and nonlinear nature of complex systems and facilitates our understanding and continual improvement of complex systems. However, there are some critical issues in FRAM use and interpretation, such as the consistency of methods and complexity and reliability of data collection methods, which should be considered by researchers and FRAM users in industry.

A reinforcement learning development of the FRAM for functional reward-based assessments of complex systems performance

Quality improvements (QIs) in dynamic and complex health care contexts require resilience and take variability into account in quality improvement. The Functional Resonance Analysis Method (FRAM) helps us understand resilience and gain insight into (un)desirable variability in the complex system of daily practice. We explored how using FRAM in the Deming cycle of a QI project can help professionals and researchers learn from, reflect upon, and improve complex processes. We used FRAM in a Dutch hospital to study a QI: Critical Care Outreach Service (CCOS). The aim was to use FRAM before and after implementation to create a FRAM model and reflect to health care professionals the mismatch between Work As Imagined (WAI) and Work As Done (WAD). The WAI FRAM model was co-created with professionals before the implementation of CCOS. We used descriptions of tasks and processes for ICU nurses and verified them in 30-minute semistructured interviews (N = 2). WAD was created by input of semistructured interviews with key professionals in CCOS (N = 21) and 3 nonparticipant observations of trained CCOS nurses. We validated WAD in 2 dialogue sessions with key professionals (N = 11). Data collection continued until saturation. Juxtaposing the WAI and WAD models showed that WAD contained additional functions and highlighted unexpectedly complex functions. Reflecting on the application of FRAM with health care professionals revealed opportunities and challenges, especially time investment. FRAM helps professionals outline processes and tasks (WAI), learn from, and reflect upon their daily practice (WAD). FRAM models help professionals identify variability proactively to improve practices that enhance resilient performance.

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.

When dealing with risks associated with complex sociotechnical systems, one needs to employ approaches that will make it possible to better understand the systems' complexity and analyze them more efficiently. Several approaches have been proposed and in the recent literature, the System-Theoretic Accident Model and Processes (STAMP) and Functional Resonance Analysis Method (FRAM) stand out. These have been applied both separately and integrated with other methods for risk analysis. This study aims to provide an overview of the literature related to the application of FRAM and STAMP integrated with other methods. Papers from various scientific resources, including Scopus, IEEE, Compendex and INSPEC, Google Scholar, and Espace ÉTS from 2004 to 2021, in English, were consulted. The keywords used to narrow our search were FRAM, STAMP, STPA, and risk analysis. The results show that FRAM and STAMP have been used in combination with other methods such as fuzzy logic, Monte Carlo Simulation, bow tie, and model checking. Their combination with other methods has enhanced their efficiency and capability in risk analysis and provides better and more precise outcomes for some specific contexts of study. These combined proposed approaches have been applied and validated for specific contexts in specific studies. Therefore, the generalization and validation of the combined methods in different contexts could be an outlook for future studies.

The Functional Resonance Analysis Method (FRAM) is a novel research methodology that uses qualitative data collection methods to map and model complex healthcare processes by identifying and depicting the cumulative activities required to produce an outcome. The FRAM aims to identify the variability that can emerge in a process when healthcare activities are performed under dynamic conditions. With this knowledge, health care system design, safety, and quality improvement recommendations can be developed with a greater understanding of everyday process functionality. Researchers interested in using the FRAM require both an understanding of the methodology itself, as well as an understanding of how to effectively plan and conduct qualitative research. The purpose of this paper is to provide practical guidance to researchers on planning and operationalizing qualitative data collection and analysis methods to inform the building of a FRAM model. A combination of literature and practical experience will be used to examine and suggest appropriate ways for researchers to carry out this work.

The Functional Resonance Analysis Method (FRAM) is extensively used to qualitatively analyze the risk of socio-technical systems. To quantitatively assess system risk, previous studies have explored various approaches to integrate FRAM with Bayesian Networks (BN). However, the process of mapping FRAM to BN relies heavily on subjective judgments, often lacking traceability and repeatability. In this paper, a standardized method for mapping FRAM to BN is proposed through introducing Accimap. Firstly, each FRAM’s functional hexagon is transformed into an Accimap, with the six aspects of functions corresponding to Accimap factors. In addition, their internal coupling relationships are obtained by cause and effect investigation in Accimap. Secondly, multiple Accimaps are connected based on the upstream-downstream couplings between functions in FRAM. Through this method, (i) the aspects of FRAM are transformed into Accimap factors and then BN nodes, and (ii) the couplings among the six aspects of functions and across multiple hexagons are converted into BN directed edges.

Complex industrial plants are characterized by digitalization and innovation. In this context it is strategic to ensure the systematic design, implementation, and continuous improvement of all processes (operations management). One of the most obvious ways to improve operations performance is to reduce the risk of accidents and human errors. In this pilot study the Functional Resonance Analysis Method (FRAM) is proposed to analyze the complexity of safety in industrial plants. This research integrates FRAM with Analytic Hierarchy Process (AHP), a multi criteria technique, to overcome the limits of the FRAM. The result is a proposal of an alternative approach to risk assessment based on principles of resilience engineering. A real case study in a petrochemical company is analyzed.

Was the Deepwater Horizon incident a “Normal” accident?

BackgroundIn complex critical neonatal and paediatric clinical practice, little is known about long-term patient outcomes and what follow-up care is most valuable for patients. Emma Children’s Hospital, Amsterdam UMC (Netherlands), implemented a follow-up programme called Follow Me for neonatal and paediatric patient groups, to gain more insight into long-term outcomes and to use such outcomes to implement a learning cycle for clinical practice, improve follow-up care and facilitate research. Three departments initiated re-engineering and change processes. Each introduced multidisciplinary approaches to long-term follow-up, including regular standardised check-ups for defined age groups, based on medical indicators, developmental progress, and psychosocial outcomes in patients and their families. This research evaluates the implementation of the three follow-up programmes, comparing predefined procedures (work-as-imagined) with how the programmes were implemented in practice (work-as-done).MethodsThis study was conducted in 2019–2020 in the outpatient settings of the neonatal intensive care, paediatric intensive care and paediatric surgery departments of Emma Children’s Hospital. It focused on the organisational structure of the follow-up care. The functional resonance analysis method (FRAM) was applied, using documentary analysis, semi-structured interviews, observations and feedback sessions.ResultsOne work-as-imagined model and four work-as-done models were described. The results showed vast data collection on medical, developmental and psychosocial indicators in all work-as-done models; however, process indicators for programme effectiveness and performance were missing. In practice there was a diverse allocation of roles and responsibilities and their interrelations to create a multidisciplinary team; there was no one-size-fits-all across the different departments. Although control and feedback loops for long-term outcomes were specified with respect to the follow-up groups within the programmes, they were found to overlap and misalign with other internal and external long-term outcome monitoring practices.ConclusionImplementing structured long-term follow-up may provide insights for improving daily practice and follow-up care, with the precondition of standardised measurements. Lessons learned from practice are (1) to address fragmentation in data collection and storage, (2) to incorporate the diverse ways to create a multidisciplinary team in practice, and (3) to include timely actionable indicators on programme effectiveness and performance, alongside medical, developmental and psychosocial indicators.

Introduction The bacterium Clostridioides difficile (C. difficile) frequently colonises the human intestine. In an adult who is healthy, this organism is relatively harmless. However, disruption of the normal gut flora, which is frequently brought on by the use of antibiotics, can result in Clostridioides difficile (C. difficile) infection (CDI). As a result, C. difficile can grow and produce a toxin that causes diarrhoea. Due to the ease with which C. difficile can be spread in healthcare environments, it is essential to recognise and treat CDI patients as soon as possible and in accordance with best practises. Aim This service evaluation aimed to explore and understand how adult inpatients presenting with diarrhoea where (CDI is suspected are managed according to Trust Clostridioides difficile guidelines. Methods Purposive sampling of up to 100 adult inpatients presenting with diarrhoea where C. difficile is suspected and the patient has been an in-patient for least 48 hours post sample request. A data collection tool was developed, pilot tested and used to collect patient data from the Oxford University Hospital's Electronic Prescribing Record. Data was analysed using descriptive statistics and the Functional Resonance Analysis Method (FRAM) that involved 1) Identifying and describing key functions relating to the management of patients 2) Identifying variability in performing key functions and 3) Determine the possibility of function resonance. Ethical approval was obtained from University of Reading. Results Fifty patient records were accessed. The results showed that many patients had a stool sample sent for review as soon as possible, a clinical review of their condition and where there was the absence of a strong suspicion or strong evidence of C. Difficile a delay in the initiation of empirical therapy. Documentation was an area with poor compliance to guidelines. 50 % had a Bristol stool chart completed before sample sent and after CDI suspected, 60 % had isolation of the patient documented, 47 % had placement of contact precautions documented, and 20 % had documentation of discontinuation of empirical therapy. A FRAM model was developed and a total of 14 functions were identified with three functions “Record Bristol stool charts”, “clinical review of the patient”, and “placement of contact precautions” being most connected to the other functions, demonstrating the significance of these functions in the management of patients who are suspected of having CDI. Discussion/Conclusion The three key tasks outlined by FRAM are important to ensure that provide patients with the best care. Understanding variability in how the functions identified in FRAM are performed is important to understand how work is performed in order to then design ways of working that benefits both patients and HCPs. Further work is required to explore this area.

A Monte Carlo evolution of the Functional Resonance Analysis Method (FRAM) to assess performance variability in complex systems

Accident models, analysis methods and emergency response plans affect what accident investigators and emergency response leaders look for, which contributing factors are found, and which recommendations are issued. This paper contrasts the Functional Resonance Analysis Method (FRAM) for an emergency response plan. FRAM illustrates the dynamic interactions within socio-technical systems and lets the analyst understand the how and why by describing non-linear dependencies, performance conditions, variability, and their resonance across functions. So, it defines a systemic framework to model complex systems from the perspective of function and views accidents and emergency response strategies as emergent phenomenon of function’s variability, It is playing an increasingly significant role in the development of systemic emergency response plan.

Safety of automated driving: The need for a systems approach and application of the Functional Resonance Analysis Method