Multicriteria Risk Evaluation Model: Utilizing Fuzzy Logic for Improved Transparency and Quality of Risk Evaluation in Healthcare.

Abstract

Introduction: Risk management is essential for quality assurance in modern healthcare organizations. Risk matrices are widely used to evaluate risks in healthcare settings; however, this approach has noteworthy weaknesses and limitations. This paper introduces a novel risk evaluation model that utilizes multicriteria decision-making and fuzzy logic, to enhance the transparency and quality of the risk evaluation process in healthcare.

Methods: The Multicriteria Evaluation Model was developed using the Decision Expert method and expert knowledge integration. Fuzzy logic was integrated within the model, using partial degrees of membership and probabilistic analysis, to address uncertainties inherent to healthcare risk evaluation. The evaluation model was tested with healthcare professionals active in the field of risk management in clinical practice and compared with the risk matrix.

Results: The designed evaluation model utilizes multicriteria decision-making while encompassing the risk matrix framework to boost user understanding and enable meaningful comparison of results. Compared with the risk matrix, the model provided similar or marginally higher risk-level evaluations. The use of degrees of membership enables evaluators to articulate a wide range of plausible risk consequences, which are often overlooked or ambiguously addressed in the traditional risk matrix approach.

Discussion and conclusions: The evaluation model demonstrates increased transparency of the decision-making process and facilitates in-depth analysis of the evaluation results. The utilization of degrees of membership revealed distinct strategies for handling uncertainty among participants, highlighting the weaknesses of using single value evaluation approach for the presented and similar decision problems. The presented approach is not limited to healthcare-related risk evaluation, but has the capacity to improve risk evaluation practices in diverse settings.