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.
Highlights
In the most recent healthcare reform, China has made substantial progress in improving equal access to care and enhancing financial protection
We describe and access five typical classification algorithms in this paper: Naive Bayes, Support Vector Machines (SVM), Classification and Regression Trees (CART), Extreme Gradient Boosting (XGBoost), and random forest, which is a reference for users to select algorithms
Performance of groupings We would like to show that the rule generation method and the multiclass classification method can design a new grouping with similar performance to the original expertoriented grouping
Summary
In the most recent healthcare reform, China has made substantial progress in improving equal access to care and enhancing financial protection. Aiming 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. Treatment trajectory encoding information about a patient and their clinical treatment is put through a large formal decision tree—the grouping, which consists of thousands of decision rules, each evaluating to either true or false. By traversing these decision rules, a care product is defined and determined [10]. 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. Expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high cost
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