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Potentially fatal incidents: identification, classification and human factor analysis

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dc.contributor.authorLezdkalne, J.
dc.date.accessioned2026-04-28T08:02:59Z
dc.date.available2026-04-28T08:02:59Z
dc.date.issued2026
dc.descriptionReceived: January 19th, 2026 ; Accepted: March 30th, 2026 ; Published: April 21st, 2026 ; Correspondence: jelena.lezd@inbox.lveng
dc.description.abstractPotentially fatal incidents (PFIs) are increasingly used as leading indicators in high-risk industries, yet their definitions, classification criteria, and investigative depth vary widely across organisations, limiting. their preventive value and comparability. Human factors (HF) play a critical role in determining whether incidents escalate into PFIs and must be considered together with technical and organisational barrier performance. This research aims to examine the role of human and organisational factors in PFI identification, analyse misclassification patterns, and propose a human-factors-based model to improve PFI classification consistency and learning value. A retrospective document analysis was conducted using incident reports from a heavy-industry organisation covering the period from 2020 to 2024. The dataset was systematically reviewed and PFI classifications were re-evaluated using a structured framework integrating hazardous energy and exposure assessment, barrier performance evaluation based on Bow-Tie logic, and human and organisational factor coding using an HFACS-based structure. Analysis revealed inconsistency in PFI classification, including overclassification and under-classification linked to limited recognition of human and organisational factors. Number of incidents were labelled as PFIs despite lacking credible fatal energy exposure, while other events with systemic and human-factor contributors associated with fatal risk were not recognised as PFIs. The HF-PFI Model demonstrated improved classification reliability by integrating energy exposure, barrier status, human factor categories, and systemic indicators. Integrating human-factors analysis into PFI identification can strengthen serious injury and fatality prevention in high-risk industrial environments.eng
dc.identifier.citationLezdkalne, J. (2026). Potentially fatal incidents: identification, classification and human factor analysis. Estonian University of Life Sciences. https://doi.org/10.15159/AR.26.021en
dc.identifier.issn2228-4907
dc.identifier.publicationAgronomy Research, 2026, vol. 24, no. 1, pp. 362–372eng
dc.identifier.urihttp://hdl.handle.net/10492/10369
dc.identifier.urihttps://doi.org/10.15159/ar.26.021
dc.publisherEstonian University of Life Scienceseng
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)eng
dc.rightsinfo:eu-repo/semantics/openAccesseng
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectpotentially fatal incidentseng
dc.subjecthuman factorseng
dc.subjectaccident investigationeng
dc.subjectsafety cultureeng
dc.subjectheavy industryeng
dc.subjectsystemic safetyeng
dc.subjectrisk assessmenteng
dc.subjectarticleseng
dc.titlePotentially fatal incidents: identification, classification and human factor analysiseng
dc.typeArticleeng

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