Abstract:
To precisely match medical needs and optimize resource allocation, a doctor-patient matching model in two-way referral under the medical alliance model has been established. Firstly, the doctor evaluation framework was constructed based on the iceberg theory, and the doctor evaluation model of bidirectional referral was obtained by combining the evaluation indexes considered by up-and-down referral. The support vector machine algorithm was used to predict the patient's disease type, and the random forest algorithm was used to obtain the patient evaluation model after mining the risk factors, thus constructing a complete evaluation system for the doctor competency model and patient characteristic profile at the referral stage. Secondly, the first round of doctor-patient matching was conducted based on the Gale-Shapley algorithm, incorporating the patients' disease risks. Additionally, by integrating personalized rankings, patients′ requirements were fulfilled to enhance matching satisfaction. Ultimately, the dynamic multi-objective optimization algorithm Was employed to achieve precise matching for all patients. Finally, the feasibility and accuracy of the model were verified by using the real data of Haodaifu website and offline hospitals for numerical experiments.