ArticleBMC medical informatics and decision making2021
DRG grouping by machine learning: from expert-oriented to data-based method.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- Diagnosis-Related group payment and its impact on hospitalization costs and patient financial protection for neurointerventional stroke care in a Chinese tertiary hospital: a retrospective analysis.Cost effectiveness and resource allocation : C/E · 2026Article
- An adaptive and interpretable case grouping framework for healthcare payment systems.International journal for equity in health · 2026Article
- The effect of diagnosis-related group policy on treatment cost and treatment efficiency of inpatients with coronary heart disease in Xinjiang: an interrupted time series analysis.Journal of global health · 2026Article
- A tree-structured multiobjective optimization framework for constructing diagnosis-related groups.NPJ digital medicine · 2025Article
- Comparative models on low multiplier DRG classification for advanced lung cancer.Frontiers in public health · 2025Article
- Optimization of diagnosis-related groups for patients with acute appendicitis using a machine learning model.Frontiers in public health · 2025Article
- Exploring the cost implications of different treatment modalities for uterine fibroids under the DRG system.Frontiers in public health · 2025Article
- Analysis and optimization of inpatient cost structure for fracture patients under the implementation of the DRG policy.Frontiers in public health · 2025Article
- Influencing factors and mechanism of physiciansZhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2024Article
- DRGKB: a knowledgebase of worldwide diagnosis-related groups' practices for comparison, evaluation and knowledge-guided application.Database : the journal of biological databases and curation · 2024Article
- Optimization of Diagnosis-Related Groups for 14,246 Patients with Uterine Leiomyoma in a Single Center in Western China Using a Machine Learning Model.Risk management and healthcare policy · 2024Article
- The influence of policy advocacy and education on medical staff's adaptation to diagnosis related groups payment reform in China: an analysis of the mediating effect of policy cognition.Frontiers in public health · 2024Article
- Reducing the Risk of Upcoding in DRG Grouping Through a Two-Stage DRG Grouper Based on Machine Learning.Inquiry : a journal of medical care organization, provision and financingArticle
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDiagnosis-related groups (DRGs) are a payment system that could effectively solve the problem of excessive increases in healthcare costs which are applied as a principal measure in the healthcare reform in China. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost.
methodsThis study proposes a method of data-based grouping, designed and updated by machine learning algorithms, which could be trained by real cases, or even simulated cases. It inherits the decision-making rules from the expert-oriented grouping and improves performance by incorporating continuous updates at low cost. Five typical classification algorithms were assessed and some suggestions were made for algorithm choice. The kappa coefficients were reported to evaluate the performance of grouping.
resultsBased on tenfold cross-validation, experiments showed that data-based grouping had a similar classification performance to the expert-oriented grouping when choosing suitable algorithms. The groupings trained by simulated cases had less accuracy when they were tested by the real cases rather than simulated cases, but the kappa coefficients of the best model were still higher than 0.6. When the grouping was tested in a new DRGs system, the average kappa coefficients were significantly improved from 0.1534 to 0.6435 by the update; and with enough computation resources, the update process could be completed in a very short time.
conclusionsAs a new potential option, the data-based grouping meets the requirements of the DRGs system and has the advantages of high transparency and low cost in the design and update process.
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