C4.5: programs for machine learning
C4.5: programs for machine learning
A classification-based methodology for planning audit strategies in fraud detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
An introduction to variable and feature selection
The Journal of Machine Learning Research
A Case Study of Applying Boosting Naive Bayes to Claim Fraud Diagnosis
IEEE Transactions on Knowledge and Data Engineering
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Data Mining and Knowledge Discovery
Bootstrap control charts in monitoring value at risk in insurance
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
We propose a scoring model that detects outpatient clinics with abusive utilization patterns based on profiling information extracted from electronic insurance claims. The model consists of (1) scoring to quantify the degree of abusiveness and (2) segmentation to categorize the problematic providers with similar utilization patterns. We performed the modeling for 3705 Korean internal medicine clinics. We applied data from practitioner claims submitted to the National Health Insurance Corporation for outpatient care during the 3rd quarter of 2007 and used 4th quarter data to validate the model. We considered the Health Insurance Review and Assessment Services decisions on interventions to be accurate for model validation. We compared the conditional probability distributions of the composite degree of anomaly (CDA) score formulated for intervention and non-intervention groups. To assess the validity of the model, we examined confusion matrices by intervention history and group as defined by the CDA score. The CDA aggregated 38 indicators of abusiveness for individual clinics, which were grouped based on the CDAs, and we used the decision tree to further segment them into homogeneous clusters based on their utilization patterns. The validation indicated that the proposed model was largely consistent with the manual detection techniques currently used to identify potential abusers. The proposed model, which can be used to automate abuse detection, is flexible and easy to update. It may present an opportunity to fight escalating healthcare costs in the era of increasing availability of electronic healthcare information.