A scoring model to detect abusive billing patterns in health insurance claims
DC Field | Value | Language |
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dc.contributor.author | Shin, Hyunjung | - |
dc.contributor.author | Park, Hayoung | - |
dc.contributor.author | Lee, Junwoo | - |
dc.contributor.author | Jhee, Won Chul | - |
dc.date.accessioned | 2021-12-02T04:42:55Z | - |
dc.date.available | 2021-12-02T04:42:55Z | - |
dc.date.created | 2021-11-29 | - |
dc.date.issued | 2012-06-15 | - |
dc.identifier.issn | 0957-4174 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/18941 | - |
dc.description.abstract | 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. (C) 2012 Elsevier Ltd. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | PERGAMON-ELSEVIER SCIENCE LTD | - |
dc.subject | CARE FRAUD | - |
dc.subject | PHYSICIANS | - |
dc.title | A scoring model to detect abusive billing patterns in health insurance claims | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jhee, Won Chul | - |
dc.identifier.doi | 10.1016/j.eswa.2012.01.105 | - |
dc.identifier.scopusid | 2-s2.0-84862811225 | - |
dc.identifier.wosid | 000302032600078 | - |
dc.identifier.bibliographicCitation | EXPERT SYSTEMS WITH APPLICATIONS, v.39, no.8, pp.7441 - 7450 | - |
dc.relation.isPartOf | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.title | EXPERT SYSTEMS WITH APPLICATIONS | - |
dc.citation.volume | 39 | - |
dc.citation.number | 8 | - |
dc.citation.startPage | 7441 | - |
dc.citation.endPage | 7450 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Operations Research & Management Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Operations Research & Management Science | - |
dc.subject.keywordPlus | CARE FRAUD | - |
dc.subject.keywordPlus | PHYSICIANS | - |
dc.subject.keywordAuthor | Health insurance claims | - |
dc.subject.keywordAuthor | Medical abuse detection | - |
dc.subject.keywordAuthor | Fraud detection | - |
dc.subject.keywordAuthor | Degrees of anomaly | - |
dc.subject.keywordAuthor | Data mining | - |
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