Research on the Optimization of Surgical Performance Grading Based on K-means Clustering
Yu Jiali
Shen Siyuan
Wu Luying
Wang Hansong
Chen Yingyao
Abstract：Objective To optimize the surgical performance grading system by utilizing the K-means clustering algorithm,thereby enhancing the precision and scientific validity of the grading.Method The methodology involves determining the K value through a combination of the silhouette coefficient,WCSS,and hospital management requirements,with further verification conducted using the Calinski-Harabasz index and the Davies-Bouldin index.Subsequently,cluster analysis is performed on surgical data based on the K values determined for each surgical level and the SDI values of individual surgeries,to elucidate the distribution characteristics of each surgical level and refine traditional surgical grading.Result The K-means clustering algorithm can further subdivide the original surgical classification into four levels with nine grades,facilitating scientific classification of surgeries and providing a more accurate foundation for surgical performance management and evaluation.Conclusion Hospitals should adopt a more scientific surgical performance grading method to differentiate resource consumption among surgeries of the same level within the four-level classification,offering guidance for rational performance allocation and control,and driving the refinement of hospital performance management.
Keywords：K-means clusteringsurgical performance gradingsilhouette coefficient
Publication Date：2026-02-05
Online Publishing Date：2026-03-12(First online date of this platform, not the publication date of the document)
Pages：7( 11-17 )