Enhancing cybersecurity in healthcare systems through a novel ordinal fuzzy decision analysis framework

Abstract Healthcare is a crucial and multifaceted sector dedicated to maintaining and restoring human health through a comprehensive range of services, including preventive care, specialised treatments, and public health interventions. The integration of advanced digital technologies has transformed this sector, enhancing accessibility, efficiency, and service quality through the digitisation and centralisation of patient records. However, this transformation also introduces significant cybersecurity challenges, including risks of data breaches, unauthorised access, and cyberattacks. To address these challenges, this paper proposes a robust decision-making framework based on Multi-Criteria Decision-Making (MCDM) methodologies, specifically, the CRiteria Importance Through Intercriteria Correlation (CRITIC) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. These are extended into an ordinal fuzzy environment to develop Ordinal-CRITIC (O-CRITIC) and Ordinal-TOPSIS (O-TOPSIS), enabling effective evaluation of healthcare system alternatives under linguistic and qualitative criteria. Seven evaluation criteria are considered: Required Expertise (C1), Tool Availability (C2), Maturity Level (C3), Learnability (C4), Scope Width (C5), Completeness (C6), and Level of Detail (C7). The proposed methods were applied to rank 22 healthcare technologies. The results indicate that alternative A6 consistently achieved the highest rank across O-TOPSIS, SAW, and classic TOPSIS (C-TOPSIS) even after the comparative analysis, validating the robustness and reliability of the proposed framework. Conversely, alternatives such as A16 and A13 ranked lowest, highlighting their limitations against the established criteria. This was followed by sensitivity analysis further demonstrating the framework’s stability under varying scenarios either for the ranking settings or following for different weighting. These findings underscore the importance of structured, objective decision-making in selecting secure and efficient healthcare systems, promoting proactive threat mitigation and resilient digital healthcare infrastructures.