![]() ![]() The grouping, an exhaustive patient case classification system, is the core design characteristic of a DRG-based payment system. However, as in many other countries, the basic DRGs structure has undergone numerous revisions since its creation, leading to a less stable, more complex, and often confusing process. Īiming at allowing for more ‘outside’ control on hospital expenditure, several pieces of common grouping software have been developed to standardise and facilitate hospital payments in China. In the DRGs system, excessive drugs and treatment provided by hospitals will not be paid for, which improves healthcare quality and stabilises costs. The medical expenses that patients and medical insurance need to pay are only related to the results of grouping. Originating from Yale University and first implemented in the United States in 1983, DRGs is a payment system that can gather patients with similar clinical symptoms and similar resource consumption patterns into the same group. Until 2016, two national DRG groupings, CN-DRGs and C-DRGs, were developed and tested in Sanming, Shenzhen and Karamay, and nearly twenty of the thirty-two provinces in mainland China implemented the simplified DRGs. In 2009, the Chinese government announced the initiation of the prospective DRG-based payment reform. One of the core measures is provider payment reform, in which diagnosis-related groups (DRGs) payment is perceived as a valuable alternative to the conventional fee-for-service (FFS) payment method. With the enhancement and standardisation of medical information systems and clinical pathways, the Chinese government has paid closer attention to payment reform and enhanced supervision of the quality of medical care in the new round of healthcare reform, hoping to curb soaring medical expenditures. However, gaps remain in efficiency in the delivery and control of health expenditures. In the most recent healthcare reform, China has made substantial progress in improving equal access to care and enhancing financial protection. ConclusionsĪs 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. 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. 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. Resultsīased 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 kappa coefficients were reported to evaluate the performance of grouping. Five typical classification algorithms were assessed and some suggestions were made for algorithm choice. It inherits the decision-making rules from the expert-oriented grouping and improves performance by incorporating continuous updates at low cost. ![]() This 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. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost. Diagnosis-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. ![]()
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