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Fire is one of the most dangerous phenomena causing major casualties and financial losses in hospitals and healthcare settings. In order to prevent and control the fire sources, first risk assessment should be conducted. Failure Mode and Effect Analysis (FMEA) is one of the techniques widely used for risk assessment. However, Risk Priority Number (RPN) in this technique does not take into account the weight of the risk parameters. In addition, indirect relationships between risk parameters and expert opinions are not considered in decision making in this method. The aim is to conduct fire risk assessment of healthcare setting using the application of FMEA combined with Multi‐Criteria Decision Making (MCDM) methods. First, a review of previous studies on fire risk assessment was conducted and existing rules were identified. Then, the factors influencing fire risk were classified according to FMEA criteria. In the next step, weights of fire risk criteria and subcriteria were determined using Intuitionistic Fuzzy Multiplicative Best-Worst Method (IFMBWM) and different wards of the hospital were ranked using Interval-Valued Intuitionistic Fuzzy Combinative Distance-based Assessment (IVIFCODAS) method. Finally, a case study was performed in one of the hospitals of Shiraz University of Medical Sciences. In this study, fire alarm system (0.4995), electrical equipment and installations (0.277), and flammable materials (0.1065) had the highest weight, respectively. The hospital powerhouse also had the highest fire risk, due to the lack of fire extinguishers, alarms and fire detection, facilities located in the basement floor, boilers and explosive sensitivity, insufficient access, and housekeeping. The use of MCDM methods in combination with the FMEA method assesses the risk of fire in hospitals and health centers with great accuracy.

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Background Some researchers state that they are not yet able to provide a deep understanding of the underlying causes of unsafe behaviors (UBs). Therefore, the present study was conducted to investigate the attitudes and experiences of Iranian workers of UBs. Methods This present study was conducted in 35 industries using a semistructured interview based on grounded theory. Forty participants were interviewed, including 13 industrial safety and health experts and 27 workers and supervisors. The analysis of the present study consisted of a three-step coding process including open, axial, and selective coding. Results The results showed that the factors affecting UBs could be classified into three categories: organizational, individual, and socioeconomic factors. Organizational factors were divided into 6 parts: procedure and environmental conditions, communications, monitoring, organizational safety culture, resource allocation, and human resources. Socioeconomic factors had three subcategories: community safety culture, type of organizational ownership, and economic problems. Finally, the individual factors were classified into two categories of personality traits and individual competence. Conclusion The results showed that organizational factors were the most categorized, and it is estimated that this factor has a more important role in the UBs. Of course, to better understand the close relationship between these factors and find the weight and importance of each factor, it needs to measure it with multicriteria decision systems.