利用连续变量的 Rasch 模型和在线云计算技术在台湾地区检测 DRGs 中的医院编码上调行为。

Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan.

机构信息

Medical Research Department, Chi Mei Medical Center, Tainan, Taiwan.

Department of Medical Affairs Chi Mei Medical Center, Tainan, Taiwan.

出版信息

BMC Health Serv Res. 2019 Sep 4;19(1):630. doi: 10.1186/s12913-019-4417-2.

DOI:10.1186/s12913-019-4417-2

PMID:31484551

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6727501/

Abstract

BACKGROUND

This work aims to apply data-detection algorithms to predict the possible deductions of reimbursement from Taiwan's Bureau of National Health Insurance (BNHI), and to design an online dashboard to send alerts and reminders to physicians after completing their patient discharge summaries.

METHODS

Reimbursement data for discharged patients were extracted from a Taiwan medical center in 2016. Using the Rasch model of continuous variables, we applied standardized residual analyses to 20 sets of norm-referenced diagnosis-related group (DRGs), each with 300 cases, and compared these to 194 cases with deducted records from the BNHI. We then examine whether the results of prediction using the Rasch model have a high probability associated with the deducted cases. Furthermore, an online dashboard was designed for use in the online monitoring of possible deductions on fee items in medical settings.

RESULTS

The results show that 1) the effects deducted by the NHRI can be predicted with an accuracy rate of 0.82 using the standardized residual approach of the Rasch model; 2) the accuracies for drug, medical material and examination fees are not associated among different years, and all of those areas under the ROC curve (AUC) are significantly greater than the randomized probability of 0.50; and 3) the online dashboard showing the possible deductions on fee items can be used by hospitals in the future.

CONCLUSION

The DRG-based comparisons in the possible deductions on medical fees, along with the algorithm based on Rasch modeling, can be a complementary tool in upgrading the efficiency and accuracy in processing medical fee applications in the discernable future.

摘要

背景

本研究旨在应用资料侦测演算法预测健保署可能扣款之项目，并设计线上警示系统，于医师完成出院病历后，即时寄发警示与提醒。

方法

自 2016 年某医学中心资料库撷取离院病患之资料，应用连续变数之拉氏模式（Rasch model），针对 20 套常态参照诊断相关群（diagnosis-related groups，DRGs）各 300 笔个案与健保署扣款纪录之 194 笔个案进行标准化残差分析，判断拉氏模式预测扣款结果与实际扣款是否有高度关联性。另设计线上警示系统，应用于医疗院所即时监控费用项目可能扣款情形。

结果

研究结果显示：1. 以拉氏模式之标准化残差分析，可正确预测健保署扣款项达 0.82；2. 药品、卫材、检查费之扣款项间无互相关联，且各年之受试者操作特性曲线（receiver operating characteristic curve，ROC）下面积（area under the curve，AUC）皆显著大于随机机率 0.50；3. 未来医院可使用费用项目可能扣款之线上警示系统。