Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ACCESS.2022.3156589 | IEEE ACCESS |
Keywords | DocType | Volume |
Costs, Hospitals, Anomaly detection, Standards, Mathematical models, Predictive models, Gaussian distribution, Outlier detection, diagnosis-related group (DRG), statistical model, DRG distributions | Journal | 10 |
ISSN | Citations | PageRank |
2169-3536 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuguang Lin | 1 | 0 | 0.34 |
| Paul Rouse | 2 | 0 | 0.34 |
| Ying-Ming Wang | 3 | 3256 | 166.96 |
| Fan Zhang | 4 | 0 | 9.13 |