Theoretical Model for Accident Prevention Based on Root Cause Analysis With Graph Theory.

INTRODUCTION: Despite huge investments in new technology and transportation infrastructure, terrible accidents still remain a reality of traffic. METHODS: Severe traffic accidents were analyzed from four prevailing modes of today's transportations: sea, air, railway, and road. Main root causes of all four accidents were defined with implementation of the approach, based on Flanagan's critical incident technique. In accordance with Molan's Availability Humanization model (AH model), possible preventive or humanization interventions were defined with the focus on technology, environment, organization, and human factors. RESULTS: According to our analyses, there are significant similarities between accidents. Root causes of accidents, human behavioral patterns, and possible humanization measures were presented with rooted graphs. It is possible to create a generalized model graph, which is similar to rooted graphs, for identification of possible humanization measures, intended to prevent similar accidents in the future. Majority of proposed humanization interventions are focused on organization. Organizational interventions are effective in assurance of adequate and safe behavior. CONCLUSIONS: Formalization of root cause analysis with rooted graphs in a model offers possibility for implementation of presented methods in analysis of particular events. Implementation of proposed humanization measures in a particular analyzed situation is the basis for creation of safety culture.

Harmonized definition of occupational burnout: A systematic review, semantic analysis, and Delphi consensus in 29 countries.

Objective A consensual definition of occupational burnout is currently lacking. We aimed to harmonize the definition of occupational burnout as a health outcome in medical research and reach a consensus on this definition within the Network on the Coordination and Harmonisation of European Occupational Cohorts (OMEGA-NET). Methods First, we performed a systematic review in MEDLINE, PsycINFO and Embase (January 1990 to August 2018) and a semantic analysis of the available definitions. We used the definitions of burnout and burnout-related concepts from the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) to formulate a consistent harmonized definition of the concept. Second, we sought to obtain the Delphi consensus on the proposed definition. Results We identified 88 unique definitions of burnout and assigned each of them to 1 of the 11 original definitions. The semantic analysis yielded a first proposal, further reformulated according to SNOMED-CT and the panelists` comments as follows: "In a worker, occupational burnout or occupational physical AND emotional exhaustion state is an exhaustion due to prolonged exposure to work-related problems". A panel of 50 experts (researchers and healthcare professionals with an interest for occupational burnout) reached consensus on this proposal at the second round of the Delphi, with 82% of experts agreeing on it. Conclusion This study resulted in a harmonized definition of occupational burnout approved by experts from 29 countries within OMEGA-NET. Future research should address the reproducibility of the Delphi consensus in a larger panel of experts, representing more countries, and examine the practicability of the definition.

Sustainable level of human performance with regard to actual availability in different professions.

BACKGROUND: In a real working environment, workers' performance depends on the level of competence, psychological and health condition, motivation, and perceived stress. These are the attributes of actual availability. It is crucial to identify the most influential attributes to develop an adequate level of worker's performance. OBJECTIVE: The purpose of this paper is to upgrade the Availability-Humanization-Model (AH-Model) with an implementation of the artificial intelligence classification tree to identify influencing factors of the well-being attributes on human performance, where the identified influencing factors are gripping points for maintaining sustainable performance in real-life conditions of different professions. METHODS: Well-being attributes are collected with the Questionnaire Actual Availability (QAA) from AH-Model and then analysed by implementation of the decision trees classification algorithms. An embedded clustering analysis of QAA ensures an efficient feature construction and selection. It negates the need of applying tree pruning or any other noise reduction algorithms. RESULTS: An implementation of the machine learning algorithms reflects the real conditions of working environments: (a) real performance of workers depends on the perception of well-being and availability and (b) the most influencing factors explicitly reflect the content of work in a specific domain (Fintech, health, forestry, traffic) with a high level of stress. CONCLUSIONS: The presented approach offers a possibility to identify the most important well-being attributes to determine an adequate efficiency and to improve the performance level in the real working conditions.