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 ab...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:NPJ digital medicine Ročník 8; číslo 1; s. 674 - 12
Hlavní autoři: Cai, Gaocheng, Zeng, Zhimei, Wan, Mengjie, Liu, Ning, Wang, Yang, Niu, Ben
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 17.11.2025
Nature Publishing Group
Nature Portfolio
Témata:
692/700/1538
692/700/3934
Biomedicine
Biotechnology
Costs
Decision making
Decision trees
Genetic algorithms
Health insurance
Length of stay
Machine learning
Medicine
Medicine & Public Health
Methods
Mutation
Optimization
Pareto optimum
Payment systems
China
ISSN:2398-6352, 2398-6352
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-025-02038-7