A novel multi-criteria doctor ranking method considering patient risk attitudes and compensatory relationships

In the post-pandemic era, the demand for Internet-based healthcare services has soared, with an increasing number of patients turning to online consultations. However, the overwhelming volume of information on online healthcare platforms often makes it challenging for patients to quickly identify the most suitable doctors, and uniform recommendation lists provided by these platforms fail to consider patients’ different risk attitudes. These challenges highlight the need for effective decision-support tools to help patients navigate the complex landscape of online consultations. This paper proposes a novel multi-criteria decision-making approach that incorporates patients’ risk attitudes and compensatory relationships among criteria. The method employs reference-dependent utility functions to model patients’ varying risk attitudes towards gains and losses. It also captures and expresses the ambiguity and hesitation inherent in online reviews through a proposed word representation model, while accounting for compensatory relationships among criteria. A case study on Haodf platform demonstrates the potential of the proposed method to help patients make informed decisions when selecting healthcare providers. Comparative and sensitivity analyses further highlight its practicality and effectiveness. This study provides valuable insights for healthcare providers and platform operators in the rapidly growing field of online medical consultations.