A survey on statistical methods for health care fraud detection

Health Care Management Science

Volumn 11, Issue 3, 2008, Pages 275-287

  Li, Jing ^a   Huang, Kuei Ying ^b   Jin, Jionghua ^b   Shi, Jianjun ^b  

^a Arizona State University   (United States)

^b UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Fraud detection; Health care; Statistical methods

Indexed keywords

FORGERY; HEALTH CARE COST; HUMAN; INSURANCE; REVIEW; STATISTICAL MODEL; STATISTICS;

FRAUD; HEALTH CARE SECTOR; HUMANS; INSURANCE CLAIM REPORTING; INSURANCE CLAIM REVIEW; MODELS, STATISTICAL;

EID: 49549089070     PISSN: 13869620     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10729-007-9045-4     Document Type: Review

References (45)

1. Abbott DW, Matkovsky IP, Elder JF (1998) An evaluation of high-end data mining tools for fraud detection. In Proceedings of IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA
2. K Bennett A Demiriz 1998 Semi-supervised support vector machines Adv Neural Inf Process Syst 12 368 374
3. Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In Proceedings of the 11th Annual Conference on Computational Learning Theory
4. Bonchi F, Giannotti F, Mainetto G, Pedreschi D (1999) A classification-based methodology for planning auditing strategies in fraud detection. In Proceedings of SIGKDD99, 175-184
5. ME Borsuk CA Stow KH Reckhow 2004 A Bayesian network of eutrophication models for synthesis, prediction, and uncertainty analysis Ecol Model 173 219 239
6. 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, 402-408
7. Cooper C (2003) Turning information into action. Computer Associates: The Software That Manages eBusiness, Report, available at http://www.ca.com
8. Cox E (1995) A fuzzy system for detecting anomalous behaviors in healthcare provider claims. In: Goonatilake S, Treleaven P (eds) Intelligent systems for finance and business. Wiley, New York, pp 111-134
9. Dai H, Korb KB, Wallace CS, Wu X (1997) A study of casual discovery with weak links and small samples. In Proceeding of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI), San Francisco, CA, pp 1304-1309
10. M Dash H Liu 1997 Feature selection for classification IDA 1 131 156
11. Fawcett T, Provost F (1999) Activity monitoring: noticing interesting changes in behavior. In Proceedings of SIGKDD99, 53-62
12. N Friedman M Linial I Nachman D Pe'er 2000 Using Bayesian networks to analyze expression data J Comput Biol 7 601 620
13. GAO (1996) Health Care Fraud: Information-Sharing Proposals to Improve Enforcement Effects. Report of United States General Accounting Office
14. S Ghosh D Reilly 1994 Credit card fraud detection with a neural network Proceedings of 27th Hawaii International Conference on Systems Science 3 621 630
15. C Hall 1996 Intelligent data mining at IBM: new products and applications Intell Softw Strateg 7 5 1 11
16. H He S Hawkins W Graco X Yao 2000 Application of Genetic Algorithms and k-Nearest Neighbour method in real world medical fraud detection problem Journal of Advanced Computational Intelligence and Intelligent Informatics 4 2 130 137
17. H He J Wang W Graco S Hawkins 1997 Application of neural networks to detection of medical fraud Expert Syst Appl 13 329 336
18. Heckerman D (1998) A tutorial on learning with Bayesian networks. In Learning in Graphical Models. Kluwer Academic, Boston, pp 301-354
19. W Herb M Tom 1995 A scientific approach for detecting fraud Best's Review 95 4 78 81
20. Hubick KT (1992) Artificial neural networks in Australia. Department of Industry, Technology and Commerce, CPN Publications, Canberra
21. SY Hwang CP Wei WS Yang 2003 Discovery of temporal patterns from process instances Comp Ind 53 345 364
22. W Lam F Bacchus 1993 Learning Bayesian belief networks: an approach based on the MDL principle Comput Intell 10 269 293
23. 2 Decomposition for Multivariate Process Monitoring and Diagnosis. Journal of Quality Technology, to appear in January 2008.
24. J Li J Shi 2007 Knowledge Discovery from Observational Data for Process Control using Causal Bayesian Networks IIE Transactions 39 6 681 690
25. Lin J-H, Haug PJ (2006) Data preparation framework for preprocessing clinical data in data mining, AMIA Symposium Proceedings 489-493
26. Little RJA, Rubin DB (2002) Statistical analysis with missing data. Wiley, New York
27. JA Major DR Riedinger 2002 EFD: A hybrid knowledge/statistical-based system for the detection of fraud The Journal of Risk and Insurance 69 3 309 324
28. NHCAA (2005) The Problem of Health Care Fraud: A serious and costly reality for all Americans, report of National Health Care Anti-Fraud Association (NHCAA)
29. K Nigam A McCalum S Thrun T Mitchell 2000 Text classification from labeled and unlabeled documents using EM Machine Learning 39 103 134
30. 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
31. 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, Las Vegas, Nevada, USA
32. C Phua D Alahakoon V Lee 2004 Minority report in fraud detection: classification of skewed data SIGKEE Explorations 6 1 50 59
33. CL Ireson 1997 Critical pathways: effectiveness in achieving patient outcomes J Nurs Adm 27 6 16 23
34. AF Shapiro 2002 The merging of neural networks, fuzzy logic, and genetic algorithms Insurance: Mathematics and Economics 31 115 131
35. 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
36. Spirtes P, Glymour C, Scheines R (1993) Causation, Prediction and Search. Springer, New York
37. S Viaene R Derrig G Dedene 2004 A case study of applying boosting Naive Bayes to claim fraud diagnosis IEEE Trans Knowl Data Eng 16 5 612 620
38. 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, 286-294
39. Wei CP, Hwang SY, Yang WS (2000) Mining frequent temporal patterns in process databases. Proceedings of international workshop on information technologies and systems, Australia, 175-180
40. G Williams 1999 Evolutionary Hot Spots data mining: an architecture for exploring for interesting discoveries Lect Notes Comput Sci 1574 184 193
41. G Williams Z Huang 1997 Mining the knowledge mine: The Hot Spots methodology for mining large real world databases Lect Notes Comput Sci 1342 340 348
42. K Yamanishi J Takeuchi G Williams P Milne 2004 On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms Data Mining and Knowledge Discovery 8 275 300
43. WS Yang SY Hwang 2006 A process-mining framework for the detection of healthcare fraud and abuse Expert Syst Appl 31 56 68
44. Yang WS (2002) Process analyzer and its application on medical care. In Proceedings of 23rd International Conference on Information Systems (ICIS02), Spain
45. Yang WS (2003) A Process Pattern Mining Framework for the Detection of Health Care Fraud and Abuse, Ph.D. thesis, National Sun Yat-Sen University, Taiwan