Name
Abstract | ||
|---|---|---|
The rising bad debts for unpaid medical treatments in hospitals pose serious problems in many countries. Researchers have started to use computational intelligence methods to construct models to classify bad debt as an important first step in debt recovery. However, the academic research dealing with this issue has been scarce. Previous studies have examined bad debt situations where only a small number of independent attributes were available, thus leaving out many potentially relevant factors in bad debt recovery. In this study, we used a richer data set containing bad debt cases from a hospital. The objective of the study was to explore the effectiveness of using a Bayesian network to classify the bad debt through comparison with alternative methods in different scenarios. The results show that the Bayesian network-based models have the best classification accuracy rates and exhibit the best global performance at most probability cutoffs and significantly outperform other models. The conditional probability distribution generated by the Bayesian network models reveals the important attributes and their relationships. The results can help hospitals identify the related characteristics of patient-debtors, look for better potential solutions, and better manage medical bad debt. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/HICSS.2016.412 | PROCEEDINGS OF THE 49TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES (HICSS 2016) |
Field | DocType | ISSN |
Econometrics,Bad debt,Conditional probability distribution,Actuarial science,Computational intelligence,Computer science,Debt,Bayesian network,Healthcare industry,Bayesian probability | Conference | 1060-3425 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Donghui Shi | 1 | 6 | 3.93 |
| Jozef M. Zurada | 2 | 3 | 3.44 |
| Jian Guan | 3 | 12 | 4.01 |