A Statistical Model to Detect DRG Outliers

IEEE Access

Shuguang Lin¹  Ying-Ming Wang¹  Paul Rouse²  Fan Zhang³ 

[1] Decision Sciences Institute, Fuzhou University, Fuzhou, China;Department of Accounting and Finance, The University of Auckland, Auckland, New Zealand;Fuzhou First Hospital Affiliated of Fujian Medical University, Fuzhou, China;

关键词: Outlier detection;    diagnosis-related group (DRG);    statistical model;    DRG distributions;   

DOI  :  10.1109/ACCESS.2022.3156589

来源: DOAJ

【 摘 要 】

This study aims to develop a statistical model to detect both high and low outlier cases in terms of diagnosis-related group (DRG) distributions. A data set containing five DRGs with 458 patient cases was selected for the study. The distributions of DRG cost and length of stay (LOS) are examined firstly, and all the distributions of DRG costs are lognormal whereas all the distributions of LOS are not lognormal or normal. A statistical model referred to as LM is set out for outlier detection in terms of the lognormal distributions of DRG costs. The LM algorithm is compared with the geometric mean (GM), Inter-quartile (IQ) and L3H3 algorithms. LM has the highest statistics for the Accuracy, Kappa coefficient, Sensitivity and Youden’s index. In addition, LM has the largest area under the ROC curve (AUC). We find that LM is a superior method to detect both low and high outliers for DRG costs, thereby improving the efficiency and effectiveness of DRG prospective payment systems and equity of healthcare.

【 授权许可】

Unknown