An Experimental Framework for Compatibility- and Capacity-Aware Doctor-Patient Matching in Online Consultation

Large-scale online medical consultation platforms require effective algorithm-assisted doctor-patient matching to balance patient-centered fit and operational efficiency. Existing approaches often optimize either clinical compatibility or physician capacity independently, leading to prolonged waiting times and uneven workloads. This protocol presents a scalable experimental framework to jointly incorporate compatibility and capacity into a hybrid matching score. Using a simulated dataset comprising 240 physicians, 3,600 patient requests, and 9,785 candidate pairs, the method quantifies compatibility (e.g., specialty alignment, mode fit) and capacity (e.g., load ratio, estimated waiting time). The framework evaluates candidate pairs through a hybrid score and compares performance against rule-based, compatibility-only, and capacity-only benchmark strategies across 12 evaluation folds. Representative results demonstrate that the hybrid framework achieves superior matching quality, including a mean precision at rank 1 of 0.740 and an F1 score of 0.714. Furthermore, it yields high operational efficiency with a capacity fill rate of 0.825 and a short mean estimated waiting time of 5.52 h. By maximizing mean patient satisfaction (4.43/5.00) and minimizing load imbalance (0.195), this framework provides a practical protocol for intelligent consultation routing and platform management.