A fuzzy based hybrid approach for risk assessment of anesthesiologists using OPA and EDAS methods

Anesthesiologists are exposed to numerous occupational hazards due to the demanding nature of their profession and the complex environment in which they operate. Classical risk assessment approaches often fall short in addressing the multidimensional and uncertain nature of these risks. To overcome these limitations, this study introduces a novel hybrid risk assessment model that integrates the Ordinal Priority Approach (OPA) for criteria weighting and the Evaluation based on Distance from Average Solution (EDAS) method for risk prioritization. The model utilizes expert judgment and incorporates five key criteria-Consequence, Probability, Detectability, Exposure, and Risk Capacity-to ensure a more accurate and comprehensive risk evaluation. Data were collected through expert interviews and a literature review, and fuzzy logic (interval type-2 fuzzy sets) was employed to manage uncertainty in qualitative assessments. A case study involving 35 identified occupational risks was conducted to evaluate the model's applicability. Results revealed that needlestick injuries (R22) were the most critical risk, followed by exposure to bodily fluids (R21) and airborne transmission of infectious diseases (R10), while exposure to magnetic fields (R4) was ranked lowest. Sensitivity analysis using four alternative weight vectors confirmed the robustness of the model's outputs. The proposed framework not only addresses the drawbacks of classical assessment methods but also provides a transparent, structured, and adaptive approach suitable for complex healthcare environments. This method can serve as a valuable decision-support tool for risk managers and hospital administrators, enabling the development of effective preventive strategies that enhance workplace safety for anesthesiology professionals.