Developing priority strategies for ensuring data security: the case of healthcare services

Ensuring healthcare data security is a critical challenge as digital transformation accelerates across the sector. This study aims to prioritize strategies for improving data security in healthcare organizations using a multi-criteria decision-making (MCDM) approach. Expert opinions were obtained from professionals with extensive experience in health informatics and decision-making authority. In this context, hospital administrators, academics, and IT specialists with at least 10 years of decision-making experience constitute the expert selection criteria. Ten experts meeting these criteria have been identified and contacted for their feedback. The study applied the Simple Weight Calculation (SIWEC) and Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) methods, both integrated with Fermatean fuzzy numbers, to assess and rank criteria influencing data security. Euclidean-based expert weighting was used to account for variations in expertise and experience. Our findings highlight technical technology (IT) infrastructure as the most critical determinants of data security, with a weighting score of 0.203. Among the proposed strategies, multi-factor authentication (w = 0.8596) and scope of specific legal regulations for health data (w = 0.8004) were prioritized as key interventions. Our study contributes a novel methodological application of fuzzy MCDM techniques to healthcare data security and provides practical guidance for developing robust, context-sensitive governance and protection strategies. This novelty marks the first-ever integration of SIWEC, MARCOS, and Euclidean-based expert weighting in a Fermatean fuzzy environment, and it is the first application of this integration in the healthcare field. Practical guide outlines the priority actions for managers and decision-makers seeking to improve data security. Therefore, in healthcare organizations where scarce resources need to be managed effectively, it will determine where available resources should be prioritized.