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

Summary

This protocol presents a scalable experimental framework for doctor-patient matching in online medical consultation platforms. By jointly integrating patient-centered clinical compatibility and physician-side service capacity into a hybrid scoring model, this method optimizes matching accuracy, minimizes patient wait times, and ensures a balanced distribution of physician workload.

Abstract

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.

Introduction

Online medical consultation has become an increasingly important mode of healthcare delivery, extending access to medical services beyond conventional face-to-face encounters¹. In China, the expansion of internet telemedicine has been reinforced by policy development, market growth, and deeper integration with the broader healthcare system, making digital consultation an increasingly important component of healthcare organization². Large-scale evidence from internet hospitals further indicates that online consultation services are now operating at substantial scale, functioning as a meaningful part of routine service pro....

Protocol

The evaluation dataset used in this protocol was computationally simulated from aggregated, de-identified operational distributions and contained no actual or identifiable human participant data. Consequently, the study was deemed exempt from full ethical review by the Institutional Review Board (IRB) of NingboTech University (Exemption No. NT9918276).

1. Data preparation and cohort definition

1. Establish the physician pool containing 240 active online physicians across eight consultation specialties: Internal Medicine, Cardiology, Neurology, Dermatology, Pediatrics, Gynecology, Orthopedics, and Psychiatry.
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Results

Baseline characteristics of the physician pool and patient request pool

The simulated analytic dataset comprised 240 physicians and 3,600 patient consultation requests. The baseline characteristics of both sides of the matching system are summarized in Table 2, and the platform-side distribution patterns are illustrated in Figure 3.

Among the 240 physicians, 128 (53.3%) were female. The cohort demonstra.......

Discussion

The present protocol demonstrates a compatibility- and capacity-aware doctor-patient matching framework in a large-scale online medical consultation setting. The hybrid strategy achieved the most balanced overall performance across matching accuracy, waiting time, capacity utilization, patient satisfaction, and workload distribution. This result is crucial because digital consultation not only expands access but also shifts clinical work into after-hours activity, increasing the hidden workload when allocation logic over.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Candidate doctor-patient pairs	Study-generated	N/A	Pairwise variable derivation and match selection (9,785 candidate pairs)
IBM SPSS Statistics	IBM Corp.	https://www.ibm.com/products/spss-statistics	Statistical analysis (Version 27)
Microsoft Excel	Microsoft Corporation	https://www.microsoft.com/microsoft-365/excel	Data management and descriptive summary (Version 2021)
Patient request records	Study-generated	N/A	Baseline patient-side characteristics and preference parameters (3,600 consultation requests)
Physician records	Study-generated	N/A	Baseline physician-side characteristics and capacity parameters (240 active physicians)
Python	Python Software Foundation	https://www.python.org	Simulation, score construction, and benchmarking (Version 3.11)