School of Public Affairs, Zhejiang University, Zijingang Campus, Hangzhou, 310058, Zhejiang Province, China.
Centre of Social Welfare and Governance, Zhejiang University, Hangzhou, China.
BMC Med Inform Decis Mak. 2021 Nov 9;21(1):312. doi: 10.1186/s12911-021-01676-7.
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. However, expert-oriented DRG grouping is a black box with the drawbacks of upcoding and high 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. 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.
Based 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.
As 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.
诊断相关分组（DRGs）是一种支付系统，可以有效解决医疗费用过度增长的问题，这是中国医疗改革的主要措施。然而，面向专家的 DRG 分组是一个黑箱，存在编码过度和成本高的缺点。
本研究提出了一种基于数据的分组方法，由机器学习算法设计和更新，可以通过真实案例甚至模拟案例进行训练。它继承了面向专家的分组决策规则，并通过低成本的持续更新来提高性能。评估了五种典型的分类算法，并对算法选择提出了一些建议。报告了kappa 系数以评估分组性能。
基于十折交叉验证，实验表明，在选择合适的算法时，基于数据的分组与面向专家的分组具有相似的分类性能。用真实案例测试由模拟案例训练的分组时，准确性较低，但最佳模型的 kappa 系数仍高于 0.6。当在新的 DRGs 系统中进行分组时，通过更新，平均 kappa 系数从 0.1534 显著提高到 0.6435；并且有足够的计算资源，更新过程可以在很短的时间内完成。
作为一种新的潜在选择，基于数据的分组满足 DRGs 系统的要求，在设计和更新过程中具有高透明度和低成本的优势。
