A tree-structured multiobjective optimization framework for constructing diagnosis-related groups.

The effectiveness of diagnosis-related groups (DRG) system is pivotal to the implementation of medical insurance payment standards. However, existing methods for constructing DRGs face challenges such as violations of grouping rules and imbalances in multiobjective optimization, which limit their ability to support payment standards that accurately reflect clinical complexities. To address these challenges, this paper proposes a multiconstraint multiobjective optimization model and a tree-structured multiobjective optimization framework, grounded in interpretability theory. The model mathematically defines the dual objectives of enhancing intragroup homogeneity and intergroup heterogeneity, subject to grouping rules. The framework utilizes nonnegative adaptive LASSO regression to accurately quantify clinical complexity, while integrating tree structures with multiobjective optimization algorithms to generate Pareto-optimal DRG sets by solving the model. Empirical results demonstrate that the proposed framework satisfies the grouping constraints and effectively reflects clinical complexity. This framework is expected to provide a paradigm for constructing DRGs, offering decision-makers efficient DRG sets.