An adaptive and interpretable case grouping framework for healthcare payment systems

作者：Gaocheng Cai, Yuda Wang, Ning Liu, Mengjie Wan, Zhimei Zeng, Ben Niu · 发表于：International Journal for Equity in Health · 年份：2026 · DOI：10.1186/s12939-026-02813-2 · 被引用次数：1 · 研究领域：Artificial Intelligence in Healthcare、Financial Distress and Bankruptcy Prediction、Imbalanced Data Classification Techniques

PURPOSE: The diagnosis-intervention packet is an innovative medical insurance reimbursement system based on case-mix, developed in China to facilitate more precise medical service management. Despite the intended role of the auxiliary catalog in subdividing case groups, its formulation remains exploratory and lacks a systematic methodological foundation. METHODS: To bridge this gap, this study proposes a case grouping framework that synthesizes optimization modeling, intelligent algorithms, and structured representation techniques. Specifically, a mathematical optimization model is developed to minimize intragroup variation, and case severity is quantified using nonnegative LASSO regression and the natural breaks method. To improve the self-adaptive and interpretable capacity of the algorithm for solving the model, an adaptive learning differential evolution algorithm based on Q-learning and a hypercube learning strategy is designed, and the case grouping pathways are represented using tree structures. RESULTS: Empirical analysis demonstrates that the proposed framework effectively enhances intragroup homogeneity and intergroup heterogeneity of the case groups under established constraints, thereby providing a robust foundation for the development of the auxiliary catalog of diagnosis-intervention packet. Furthermore, its generalizability is validated by extending the framework to subdivide diagnosis-related groups. CONCLUSION: The proposed framework offers a theoretically so...