Abstract
Fraud in the healthcare system is a major problem whose rampant growth has deeply affected the US government. In addition to financial losses incurred due to this fraud, patients who genuinely need medical care suffer because of unavailability of services which in turn incur due lack of funds. Healthcare fraud is committed in different ways at different levels, making the fraud detection process more challenging. The data used for detecting healthcare fraud, primarily provided by insurance companies, is massive, making it impossible to audit manually for fraudulent behavior. Data-mining and Machine-Learning techniques holds the promise to provide sophisticated tools for the analysis of fraudulent patterns in these vast health insurance databases. Among the data mining methodologies, supervised classification has emerged as a key step in understanding the activity of fraudulent and non-fraudulent transactions as they can be trained and adjusted to detect complex and growing fraud schemes. This chapter provides a comprehensive survey of those data-mining fraud detection models based on supervised machine-learning techniques for fraud detection in healthcare.
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References
CMS (2011) Research, statistics, data and systems: national health expenditure data. NHE fact sheet
CMS (2011) Medicare: HCPCS–general information
FBI (2009) Reports and publications: 2009 financial crimes report
NHCAA (2007) The NHCAA fraud fighter’s handbook: a guide to health care fraud investigations and SIU operations
IMF (2008) World economic and financial surveys: world economic outlook
Database NHCAA (2010) Combating health care fraud in a post-reform world: seven guiding principles for policymakers
NHCAA The problem of health care fraud, consumer alert: the impact of health care fraud on you, report of national health care anti-fraud association (NHCAA)
Koh H, Tan G (2005) Data mining applications in healthcare. j healthc inf mgmt 19(2):64–72
OIG (2011) Medical fraud cases: OIG most wanted fugitive
He H, Hawkins S, Graco W, Yao X (2000) Application of genetic algorithms and k-nearest neighbor method in real world medical fraud detection problem. J Adv Comput Intell Intell Inf 4(2):130–137
Chan CL, Lan CH (2001) A data mining technique combining fuzzy sets theory and bayesian classifier—an application of auditing the health insurance fee. In: Proceedings of the International conference on artificial intelligence, pp 402–408
Ormerod T, Morley N, Ball L, Langley C, Spenser C (2003) Using ethnography to design a mass detection tool (MDT) for the early discovery of insurance fraud. In: Proceedings of the ACM CHI conference, 650–651
Ortega PA, Figueroa CJ, Ruz GA (2006) A medical claim fraud/abuse detection system based on data mining: a case study in chile. In: Proceedings of international conference on data mining, 224–231
Viveros MS, Nearhos JP, Rothman MJ (1996) Applying data mining techniques to a health insurance information system. In: Proceedings of the 22nd VLDB conference, Mumbai, India, pp 286–294
Yang WS, Hwang SY (2006) A process-mining framework for the detection of healthcare fraud and abuse. Expert Syst Appl 31:56–68
Liou F, Tang Y, Chen J (2008) Detecting hospital fraud and claim abuse through diabetic outpatient services. Health Care Manage Sci, 353–358
Shan Y, Jeacocke D, Murray D, Sutinen (2008) A mining medical specialist billing patterns for health service management. In: Roddick J, Li J, Christen P, Kennedy P, (eds) Proceeding 7th Australasian data mining conference (AusDM 2008), Glenelg, South Australia. CRPIT, 87. ACS 105–110
Sokol L, Garcia B, West M, Rodriguez J, Johnson K (2001) Precursory steps to mining HCFA health care claims. In: Proceedings of the 34th Hawaii International conference on system sciences
Yang WS (2002) Process analyzer and its application on medical care. In: Proceedings of 23rd International conference on information systems (ICIS02), Spain
Li J, Huang K, Jin J, Shi J (2008) A survey on statistical methods for health care fraud detection. Health Care Manage Sci, 275–287
Table F, Raineri A, Maturana S, Kaempffer A (2008) Fraud in the health systems of chile: a detection model. Am J Public Health, pp 56–61
Ghahramani Z (2004) Unsupervised learning
Rosella (2011) Predictive knowledge and data mining: healthcare fraud detection
Hall C (1996) Intelligent data mining at IBM: new products and applications. Intell Softw Strateg 7(5):1–11
Report on the use of health information technology to enhance and expand health care anti-fraud activities. Foundation of research and education of AHIMA
FBI (2011) Scams and Safety: common fraud schemes
London: The Guardian (2007) The mystery of John Darwin
Herb Denenberg (2005) The denenberg report: the insurance commissioners, other government agencies, and the insurance companies focus on insurance fraud committed by policyholders, but nothing is done about the multi-billion dollar racket of insurance fraud committed by insurance companies
Bhuvaneswari R, Kalaiselvi K (2012) naive bayesian classification approach in healthcare applications. Int j comput sci telecommun, 3(1):106–112
Silver M, Sakata T, Su HC, Herman C, Dolins SB, O’Shea MJ (2001) Case study: how to apply data mining techniques in a healthcare dataware house. J Healthcare Inf Manage 15(2):155–164
Relles D, Ridgeway G, Carter G (2002) Data mining and the implementation of a prospective payment system for inpatient rehabilitation. Health Serv Outcomes Res Method 3(3–4):247–266
Anonymous (1999) Texas medicaid fraud and abuse detection system recovers $2.2 million, wins national award. Health Manag Technol 20(10):8
Tu J (1995) Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol pp 1225–1231
Lewis R (2000) An introduction to classification and regression tree (CART) analysis. Presented at annual meeting of the society for academic emergency medicine
Nayak J, Cook D (2001) Approximate association rule mining. In: Proceedings of the 14th International florida artificial intelligence research society conference
Cunningham P, Delany S (2007) k-Nearest neighbour classifiers. Technical report, UCD-CSI-2007-4
Russel S, Norvig P (2003) Artificial intelligence: a modern approach. Prentice-Hall, 2nd edition
Vose D (1995) The simple genetic algorithm: foundations and theory
Berger J (2006) The case for objective bayesian analysis. Bayesian Anal 1(3):385–402
Vick K (2009) As rescissions spawn outrage, health insurers cite fraud control. The Washington post, http://www.washingtonpost.com/wp-dyn/content/article/2009/09/07/AR2009090702455.html, Information Accessed on May 2012
Jeffries D, Zaidi I, Jong B, Holland M, Miles D (2008) Analysis of flow cytometry data using an automatic processing tool. Cytometry Part A 73A:857–867
Larose D (2005) Discovering knowledge in data, An introduction to data mining. Wiley InterScience
Niedermaye D (2008) An introduction to bayesian networks and their contemporary applications, innovations in bayesian networks. Springer, pp 117–130
De Jong KS, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach learn 13:161–188
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Dua, P., Bais, S. (2014). Supervised Learning Methods for Fraud Detection in Healthcare Insurance. In: Dua, S., Acharya, U., Dua, P. (eds) Machine Learning in Healthcare Informatics. Intelligent Systems Reference Library, vol 56. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40017-9_12
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