An intelligent medical recommendation model based on big data-driven estimation of physician ability

Abstract

This study tackles the critical issue of medical resource allocation, with a particular focus on China, where the overutilization of top-tier hospitals exacerbates healthcare shortages. Given the challenges of expanding medical resources, optimizing their utilization becomes essential. We introduce a novel physician recommendation approach that estimates physician abilities by analyzing both external features derived from physician-disease interactions and internal characteristics, such as hospital levels and physician titles. By integrating these dimensions, we offer a comprehensive assessment of physician capabilities, considering both the complexity of diseases and the physician’s competence. Experimental results demonstrate that our method outperforms both traditional and state-of-the-art models, significantly improving the efficient distribution of limited medical resources. The effectiveness of the physician rankings in optimizing resource allocation is validated through the use of the Kendall Rank Correlation Coefficient (KRCC). Our approach holds considerable promise in enhancing healthcare resource utilization and alleviating resource constraints.