Abstract
Healthcare costs in the US, as well as in other countries, increase rapidly due to demographic, economic, social, and legal changes. This increase in healthcare costs impacts both government and private health insurance systems. Fraudulent behaviors of healthcare providers and patients have become a serious burden to insurance systems by bringing unnecessary costs. Insurance companies thus develop methods to identify fraud. This paper proposes a new multistage methodology for insurance companies to detect fraud committed by providers and patients. The first three stages aim at detecting abnormalities among providers, services, and claim amounts. Stage four then integrates the information obtained in the previous three stages into an overall risk measure. Subsequently, a decision tree based method in stage five computes risk threshold values. The final decision stating whether the claim is fraudulent is made by comparing the risk value obtained in stage four with the risk threshold value from stage five. The research methodology performs well on real-world insurance data.
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References
National health expenditure projections 2010–2020 (2010) Centers for Medicare & Medicaid. http://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/Downloads/proj2010.pdf. Accessed 21 September 2013
Sparrow MK (1998) Fraud control in the health care industry: assessing the state of the art. National Criminal Justice Reference Service. https://www.ncjrs.gov. Accessed 28 August 2013
Atkinson M, Gierlasinski N (2010) Fraud in healthcare organizations: profiteering at society’s expense. ASBBS Annual Conference: 471–474
Fraud survey 2003 by KPMG (2003) http://faculty.usfsp.edu/gkearns/Articles_Fraud/Fraud%20Survey_040855_R5.pdf. Accessed 31 August 2013
Li J, Huang KY, Jin J, Shi J (2008) A survey on statistical methods for health care fraud detection. Health Care Manag Sci 11(3):275–287
Guidelines to health care fraud (1991) National Health Care Anti-Fraud Association. http//www.nhcaa.org. Accessed 15 September 2013
Mukherjee S (2012) Medicare ‘upcoding’ has cost seniors $11 billion over the last decade. Think Progress Organization. http://thinkprogress.org. Accessed 15 October 2013
Wynia M, Cummins D, VanGeest J, Wilson I (2000) Physician manipulation of reimbursement rules for patients. J Am Med Assoc 283(14):1858–1865. doi:10.1001/jama.283.14.1858
Robbins DB, Anderson A (2011) Too much care? Stepped up medical necessity fraud litigation against hospitals. Washington Healthcare News. www.wahcnews.com. Accessed 15 September 2013
Price M, Norris DM (2009) Health care fraud: physicians as white collar criminals? J Am Acad Psychiatry Law 37(3):286–289
He H, Wang J, Graco W, Hawkins S (1997) Application of neural networks to detection of medical fraud. Expert Syst Appl 13(4):329–336
Viveros M, Nearhos J, Rothman M (1996) Applying data mining techniques to a health insurance information system. 22nd VLDB Conference: 286–294
Peng Y, Kou G, Sabatka A, Chen Z, Khazanchil D, Shi Y (2006) Application of clustering methods to health insurance fraud detection. Int Conf Serv Syst Serv Manag 1:116–120
Shin H, Park H, Lee J, Jhee WC (2012) A Scoring model to detect abusive billing patterns in health insurance claims. Expert Syst Appl 39(8):7441–7450
Major J, Riedinger D (2002) A hybrid knowledge/statistical-based system for the detection of fraud. J Risk Insur 69(3):309–324. doi:10.1111/1539-6975.00025
He H, Graco W, Yao X (1999) Application of genetic algorithm and k-nearest neighbour method in medical fraud detection. Simuluated Evalution and Learning: 74–81
Lin KC, Yeh CL (2012) Use of data mining techniques medical fraud in health insurance. http://sparc.nfu.edu.tw/~ijeti/download/V2-no2-126-137.pdf. Accessed 25 May 2013
Brockett PL, Derrig RA, Golden LL, Levine A, Alpert M (2002) Fraud classification using principal component analysis of ridits. J Risk Insur 69(3):341–371
Ortega PA, Figueroa CJ, Ruz G (2006) A medical claim fraud/abuse detection system based on data mining: a case study in Chile. International Conference on Data Mining: 224–231
Liou FM, Tang YC, Chen JY (2008) Detecting hospital fraud and claim abuse through diabetic outpatient services. Health Care Manag Sci 11(4):353–358
Yang WS, Hwang SY (2006) A process mining framework for the detection of healthcare fraud and abuse. Expert Syst Appl 31(1):56–68
Tagaris A, Mnimatidis P, Koutsouris D (2009) Implementation of a prescription fraud detection software using RDBMS tools and ATC coding. 9th International Conference on Information Technology and Applications in Biomedicine: 1–4. doi: 10.1109/ITAB.2009.5394458
Aral KD, Guvenir HA, Sabuncuoglu I, Akar AR (2012) A prescription fraud detection model. Comput Methods Programs Biomed 106:37–46. doi:10.1016/j.cmpb.2011.09.003
Iyengar VJ, Hermiz KB, Natarajan R (2013) Computer-aided auditing of prescription drug claims. Health Care Manag Sci. doi:10.1007/s10729-013-9247-x
Weiszfeld E (1937) “Sur le point par lequel la somme des distances den points donnés est minimum”. Tôhoku Math J 43:355–386
Evans J, Hwang Y, Nagarajan N (1995) Physicians’ response to length of stay. Med Care 33(11):1106–1119
Auerbach AD, Wachter RM, Katz P, Showstack J, Baron RB, Goldman L (2002) Implementation of a voluntary hospitalist service at a community teaching hospital: improved clinical efficiency and patient outcomes. Ann Intern Med 137(11):859–865
Ozcan YA (1998) Physician benchmarking: measuring variation in practice behavior in treatment of otitis media. Health Care Manag Sci 1(1):5–17
Greenfield S, Nelson EC, Zubkoff M, Manning W, Rogers W, Kravitz RL, Keller A, Tarlov AR, Ware JE Jr (1992) Variations in resource utilization among medical specialties and systems of care results from the medical outcomes study. J Am Med Assoc 267(12):1624–1630. doi:10.1001/jama.1992.03480120062034
Ren D, Wang B, Perrizo W (2004) RDF: a density-based outlier detection method using vertical data representation. 4th IEEE International Conference on Data Mining: 503–506
Shewhart WA (1931). Economic control of quality of manufactured product. ISBN 0-87389-076-0
Maass W (2000) On the computational power of winner-take-all. Neural Comput 12:11
Montgomery DC (2009) Design and analysis of experiments
Williams LJ, Herve A (2010) Fisher’s least significant difference test. https://www.utdallas.edu/~herve/abdi-LSD2010-pretty.pdf. Accessed 08/07/2014
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Johnson, M.E., Nagarur, N. Multi-stage methodology to detect health insurance claim fraud. Health Care Manag Sci 19, 249–260 (2016). https://doi.org/10.1007/s10729-015-9317-3
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DOI: https://doi.org/10.1007/s10729-015-9317-3