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

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. Enhancing the traditional FRAM approach, the paper presents a structured systemic analysis for a case study in the neurosurgery peri‑operative patient's pathway. A multi-layer semi-quantitative framework is used as a basis for a novel decision support tool: the Variability Impact Matrix (VIM). The VIM aids to prioritize corrective measures and the need for functional performance indicators. A combination of social and technical perspectives is confirmed being necessary to cope with complex work environments, such as healthcare practices. journal homepage: www.elsevier.com/locate/ress Resilience engineering for socio-technical risk analysis: Application in neuro-surgery Riccardo Patriarca a,⁎, Andrea Falegnami a , Francesco Costantino a , Federico Bilotta b a Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, Italy b Department of Anesthesiology, Critical Care and Pain Medicine, Sapienza University of Rome, Italy A R T I C L E I N F O Keywords: System safety Complex systems Resilience engineering Resilience indicators Healthcare management Healthcare engineering Patient pathway A B S T R A C T Healthcare practitioners are generally forced to adapt their actions to cope with the complexity of daily con- ditions, 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. Enhancing the traditional FRAM approach, the paper presents a structured systemic analysis for a case study in the neurosurgery peri‑operative patient's pathway. A multi-layer semi-quantitative framework is used as a basis for a novel decision support tool: the Variability Impact Matrix (VIM). The VIM aids to prioritize corrective measures and the need for functional performance indicators. A combination of social and technical perspectives is confirmed being necessary to cope with complex work environments, such as healthcare practices.