A Lightweight Multicriteria Recommendation Mechanism for Enhancing Doctor–Patient Matching Efficiency on Online Consultation Platforms

This study focuses on doctor recommendation mechanisms for online consultation platforms (OCPs), and aims to develop a lightweight multi-criteria decision framework that enhances doctor–patient matching efficiency by mitigating patients’ disproportionate preference for renowned physicians from tertiary hospitals. The proposed framework operates in two stages. In the first stage, symptom-related keywords provided by patients in their consultation comments are extracted using a text presentation model selected through comparative experiments. Based on these extracted features, a pool of candidate physicians is constructed by matching patient symptoms with physicians’ professional expertise, utilizing both historical consultation records and physicians’ background information. In the second stage, the recommendation priority is determined using an entropy-weighted TOPSIS model, which evaluates multiple criteria including the alignment between physician competencies and patient needs, while simultaneously promoting the utilization of underused medical resources. Experiments conducted on historical data from HaoDaiFu, a representative Chinese OCP, demonstrate that the proposed framework significantly increases the exposure opportunities for lower-usage and newly registered physicians, alleviates consultation congestion around highly reputed doctors, and maintains comparable recommendation accuracy. Consequently, the developed multi-criteria decision framework effectively improves doctor–patient matching efficiency and contributes to more balanced medical resource allocation and enhanced service accessibility.