Online doctor-patient matching method based on patient consensus and doctor fairness

With the rapid development of Internet and smart medicine, online medical platform has gradually become an important channel to meet the basic medical needs of the public. How to meet the needs of patients and achieve precise matching between doctors and patients is an urgent problem to be solved. At the same time, it is necessary to ensure the fairness of doctors and avoid wasting medical resources in the process of doctor-patient matching. Therefore, we propose an online doctor-patient matching method based on patient consensus and doctor fairness to assist online medical platforms in making decisions. Firstly, we build the consensus degree of patients based on the feature information of doctors and the patient demands. Then, we use the Rawlsian maximin fairness to measure doctor fairness and construct a doctor-patient matching model with minimizing patient risk losses and maximizing doctor fairness. This model can obtain the doctor-patient matching results for the online medical platform. Finally, a case study of the HaoDaiFu online medical platform verify the effectiveness of the proposed doctor-patient matching method.