Objective To evaluate the pathways for improving the operational efficiency of medical teams, thereby providing micro-level empirical evidence for the refined management and high-quality development of public hospitals. MethodsBased on panel data from nine surgical teams in the Department of Thoracic Surgery at Sichuan Cancer Hospital from 2021 to 2024, this study employed the data envelopment analysis (DEA) with the BCC model to assess static efficiency, including technical efficiency (TE), scale efficiency (SE), and overall efficiency (OE). The Malmquist index was used to analyze the dynamic total factor productivity (TFP) and its decomposition into efficiency change (EC) and technology change (TC). Input indicators were the number of physicians and the number of open beds. Output indicators included the proportion of surgical patients, the proportion of grade Ⅳ surgeries, and the average length of stay (reciprocally transformed for positive orientation). Results The mean OE of all medical teams showed a continuous upward trend, while the mean SE exhibited a “V-shaped” pattern, initially decreasing and then increasing. The most significant growth was observed in mean TE, which was the primary driver of the OE improvement. All medical teams achieved positive TFP growth, with TC values greater than 1.000 across all teams, indicating that technological innovation was the core engine of efficiency enhancement. However, EC showed a divergent trend among the teams. Conclusion Public hospital performance appraisal policies effectively guide technological upgrading of medical teams through indicators such as “proportion of discharged patients undergoing surgery” and “proportion of grade Ⅳ surgeries”. However, issues of hospital resource mismatch and SE differentiation persist. It is necessary to establish specialized operation groups for dynamic resource monitoring and construct a “technological upgrading, scale adaptation, and management innovation” triangular balanced system to achieve a sustainable mechanism for maximizing healthcare resource input-output.