Applying clustering and AHP methods for evaluating suspect healthcare claims
•A model for analyzing healthcare claims and auditing suspect entities is proposed.•The model is based on clustering techniques and the AHP method.•Clustering claims data reduced the chances of evaluating false positive records.•The AHP method provided objective criteria for ranking suspect entities...
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| Published in: | Journal of computational science Vol. 19; pp. 97 - 111 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01-03-2017
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | •A model for analyzing healthcare claims and auditing suspect entities is proposed.•The model is based on clustering techniques and the AHP method.•Clustering claims data reduced the chances of evaluating false positive records.•The AHP method provided objective criteria for ranking suspect entities.
This paper seeks to present a model for the analysis of suspicious claims data from healthcare providers with the use of different clustering algorithms, and the application of the AHP multicriteria method for prioritizing the identified suspect entities for subsequent auditing. We begin with a brief overview of related works that have covered the application of the aforementioned techniques for investigating suspicious entities in the context of internal auditing and healthcare. After presenting the steps for the construction of our own model, we discuss our results. We determine that the application of clustering algorithms to our initial variables resulted in the automatic detection of almost all entities initially classified as suspect. Our AHP model then provided us with rational criteria for effectively and objectively ranking these entities for further investigation |
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| ISSN: | 1877-7503 1877-7511 |
| DOI: | 10.1016/j.jocs.2017.02.007 |