Analysis of Health Care Billing via Quantile Variable Selection Models
Date
2021-09-27
Authors
Ekin, Tahir
Damien, Paul
Journal Title
Journal ISSN
Volume Title
Publisher
Multidisciplinary Digital Publishing Institute
Abstract
Fraudulent billing of health care insurance programs such as Medicare is in the billions of dollars. The extent of such overpayments remains an issue despite the emerging use of analytical methods for fraud detection. This motivates policy makers to also be interested in the provider billing characteristics and understand the common factors that drive conservative and/or aggressive behavior. Statistical approaches to tackling this problem are confronted by the asymmetric and/or leptokurtic distributions of billing data. This paper is a first attempt at using a quantile regression framework and a variable selection approach for medical billing analysis. The proposed method addresses the varying impacts of (potentially different) variables at the different quantiles of the billing aggressiveness distribution. We use the mammography procedure to showcase our analysis and offer recommendations on fraud detection.
Description
Keywords
health care fraud, medicare, quantile regression, upcoding, Bayesian information criterion, Information Systems and Analytics
Citation
Ekin, T., & Damien, P. (2021). Analysis of health care billing via quantile variable selection models. Healthcare, 9(10), 1274.
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Rights License
This work is licensed under a Creative Commons Attribution 4.0 International License.