VARIABILITY MANAGEMENT OF EFFICIENT VEHICLE OPERATION BASED ON FUNCTIONAL RESONANCE ANALYSIS METHOD

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.

Systemic approaches to incident analysis in coal mines: Comparison of the STAMP, FRAM and “2–4” models

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.

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 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

ObjectivesElastic compression stocking (ECS) therapy is an important treatment for patients with deep venous thrombosis (DVT) and chronic venous insufficiency (CVI). This study aimed to provide insight into the structure...

The Functional Resonance Analysis Method (FRAM) involves mapping out interrelated functions to gain an understanding of how to monitor and optimize system performance. A FRAM model was applied to study the application of predictive, human-centered design review of an asphalt job truck for road construction. The model provided for a constructive means to communicate, reflect upon, and strategize methods to support ongoing human-centered design practice in the business. Practical recommendations were made with regard to resource allocation and management activity. This paper will describe the development of the model, the application, and the findings.

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

A Qualitative-Quantitative Resilience Assessment Approach for Socio-technical Systems

The Aim of this paper is to develop the methods of analysis and simulation of the processes of occurrence and development of emergencies at complex railway infrastructure facilities. It cites analysis data on the threats, causes and consequences of sudden emergencies at complex railway infrastructure facilities. For the purpose of ensuring reliable operation of technical objects, as well as timely identification of faults, it is proposed to use the indicator-based approach that allows diagnosing and formally analyzing the processes of occurrence and propagation of malfunctions across the elements of complex technical systems. For the purpose of simulating the processes of propagation of the disturbances (hazards of emergencies) that occur as the result of malfunctions, it is proposed to use the theoretic graph approach that involves model and visual representation of the structure of a technical system under consideration in the form of a directed graph that shows the correlations between its elements. Each node and edge of a graph is assigned certain parameters or functionals that reflect the processes of correlated operation of the elements of the simulated system. The propagation of disturbances within a system is simulated with pulse processes initiated in one or several nodes. The paper refers to the developed formalized models of disturbance propagation in a technical system based on the construction of structural components and correlation matrices. The authors introduce the concept of critical element of a technical system that helps identify the event of its failure. Two basic criteria of technical system failure, i.e. the exclusive (a system is considered to have failed if the disturbance has reached any of the critical elements) and absolute criterion (failure occurs if the disturbance has reached the specified subset of critical elements) are defined. The paper provides an analytical example that illustrates the capabilities of the proposed model of disturbance propagation within the structure of a technical system. For the purpose more efficient diagnostics of the hazard of emergencies in railway infrastructure facilities the paper proposes a model of application of structurally integrated indicators that consists in the integration of indicators within the structure of a technical system that would immediately deliver the required and sufficient information in case of emergency. The main task would be to identify a set of indicators with the primary purpose of reducing the information-related stress and concentration of dispatchers’ or operators’ attention on the processes within a technical system that are most relevant in terms of accident-free and safe operation. Basic criteria are identified for the generation of the set of indicators within a complex technical system: maximum of reliability of the disturbance consequences estimate, maximum of accuracy of emergency causes identification, minimum of emergency identification time, minimum of nonrecurrent and current costs. A modified graph model of disturbance propagation in a complex technical system is provided that is the prerequisite for solving the multicriterion problems of optimal location of indicators within the structure of a technical system in terms of completeness, accuracy and timeliness of detection of failures of various types. Automation of the processes of generation of indicator sets using models of disturbance propagation in technical systems will allow using the proposed methods as part of further development of the URRAN methodology in terms of improvement of the decision support in railway infrastructure facilities management.

A structured approach for resilience-oriented human performance assessment and prediction in offshore safety-critical operations

The air traffic controllers in the control tower at an airport are responsible for the safe and efficient movements of aircraft on the taxiways and runways of the airport, and of aircraft in the airspace near the airport. In aerodrome Air Traffic Control (ATC), controllers always face inevitable disturbances, such as changing traffic situations, variability in the performance of pilots, variable wind conditions, and so on. However, controllers deal with these challenging situations to provide safe and efficient ATC services continuously. The aim of this study is to determine the resilience of controllers during their normal daily ATC operations. Multiple interviews with tower controllers and observations in the control tower at Chubu Centrair International Airport in Japan were conducted to investigate their working processes, including cognitive aspects. Their answers to the interview questions were analyzed from the perspective of Safety-II, that is, how they manage disturbances in their working environment to achieve successful ATC operations. The analysis results are described using the functional resonance analysis method (FRAM), modified for the description of three types of functions and situational factors: (1) basic functions for meeting the separation standards between aircraft, (2) threat factors, which could disturb the basic functions, and (3) response functions for managing possible negative effects caused by the threat factors. The FRAM model obtained visualizes the complex interactions among the functions and the situational factors in aerodrome ATC tasks, and it shows the essential role of performance adjustments of controllers for the proper functioning of ATC.

The article presents a methodology for presenting initial data using descriptive matrices characterizing the object of observation in order to algorithmize procedures for processing the results of measured indicators and making decisions based on them about the state of the object or system. The construction of descriptive matrices is based on graph theory, which is used to synthesize a descriptive-time model. Two types of models are used for a formalized description of the process under study: a discrete-time model and models based on the theory of random graphs. The main stage in the development of a descriptive-temporal model of the process is the construction of a hierarchical tree, for which the apparatus of graph theory is used, which makes it possible to visually represent the interrelated elements of the system, taking into account their functional dependence and time indicators, as well as the distribution of elements by some classes. The final result of the performed representations is a reference matrix of states for each stage of the functioning of the process under study, which is a set of string vectors describing the states of the system taking into account binary values. The approach used makes it possible to simplify the procedure for determining the state of a complex organizational and technical system and to present the essence of the phenomenon under study when considering technological processes or evaluating the state of operability of technical means. The proposed method of presenting the initial data for constructing descriptive matrices in order to assess the state of an object (a complex organizational and technical system) can significantly reduce the time spent on processing the results characterizing the state of the process under study, increase the reliability and objectivity of the result of such an assessment, as well as create a database for predicting trends in the development of the process. The application of the proposed approach is possible in decision support systems on the current state of a complex organizational and technical system and forecasting its development based on automated processing of measurement results.