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Resilience engineering for socio-technical risk analysis: Application in neuro-surgery

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  • Patriarca, Riccardo
  • Falegnami, Andrea
  • Costantino, Francesco
  • Bilotta, Federico

Abstract

Healthcare practitioners are generally forced to adapt their actions to cope with the complexity of daily conditions, taking advantage of their inherent potential for resilient performance. Following the principles of Resilience Engineering, this article details the application of the Functional Resonance Analysis Method (FRAM), for a semi-quantitative risk analysis of patient's pathway. The analysis adopts a holistic perspective on the complexity of everyday work in order to manage emergent behaviours, mainly related to iatrogenic disease associated with neuro-anesthesia treatment.

Suggested Citation

  • Patriarca, Riccardo & Falegnami, Andrea & Costantino, Francesco & Bilotta, Federico, 2018. "Resilience engineering for socio-technical risk analysis: Application in neuro-surgery," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 321-335.
    • Handle: RePEc:eee:reensy:v:180:y:2018:i:c:p:321-335
    DOI: 10.1016/j.ress.2018.08.001
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    References listed on IDEAS

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    1. Patriarca, Riccardo & Bergström, Johan & Di Gravio, Giulio, 2017. "Defining the functional resonance analysis space: Combining Abstraction Hierarchy and FRAM," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 34-46.
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    Citations

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    Cited by:

    1. Hussein Slim & Sylvie Nadeau, 2020. "A Mixed Rough Sets/Fuzzy Logic Approach for Modelling Systemic Performance Variability with FRAM," Sustainability, MDPI, vol. 12(5), pages 1-21, March.
    2. Steen, Riana & Ferreira, Pedro, 2020. "Resilient flood-risk management at the municipal level through the lens of the Functional Resonance Analysis Model," Reliability Engineering and System Safety, Elsevier, vol. 204(C).
    3. Huang, Wencheng & Yin, Dezhi & Xu, Yifei & Zhang, Rui & Xu, Minhao, 2022. "Using N-K Model to quantitatively calculate the variability in Functional Resonance Analysis Method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Maddah, Negin & Heydari, Babak, 2024. "Building back better: Modeling decentralized recovery in sociotechnical systems using strategic network dynamics," Reliability Engineering and System Safety, Elsevier, vol. 246(C).
    5. Li, Jue & Wang, Hongwei, 2023. "Modeling and analyzing multiteam coordination task safety risks in socio-technical systems based on FRAM and multiplex network: Application in the construction industry," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    6. Kaya, Gulsum Kubra & Hocaoglu, Mehmet Fatih, 2020. "Semi-quantitative application to the Functional Resonance Analysis Method for supporting safety management in a complex health-care process," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    7. Nogal, M. & Honfi, D., 2019. "Assessment of road traffic resilience assuming stochastic user behaviour," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 72-83.
    8. Alessandro Annarelli & Cinzia Battistella & Fabio Nonino, 2020. "A Framework to Evaluate the Effects of Organizational Resilience on Service Quality," Sustainability, MDPI, vol. 12(3), pages 1-15, January.
    9. Kaya, Gulsum Kubra & Ozturk, Fatih & Sariguzel, Emine Emel, 2021. "System-based risk analysis in a tram operating system: Integrating Monte Carlo simulation with the functional resonance analysis method," Reliability Engineering and System Safety, Elsevier, vol. 215(C).

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