A process-mining framework for the detection of healthcare fraud and abuse

Expert Systems with Applications

Volumn 31, Issue 1, 2006, Pages 56-68

  Yang, Wan Shiou ^a   Hwang, San Yih ^b  

^a NATIONAL CHANGHUA UNIVERSITY OF EDUCATION   (Taiwan)

^b NATIONAL SUN YAT SEN UNIVERSITY   (Taiwan)

Author keywords

Classification model; Clinical pathways; Data mining; Healthcare abuse; Healthcare fraud

Indexed keywords

HEALTH CARE; HEALTH INSURANCE; MATHEMATICAL MODELS; XML;

CLASSIFICATION MODEL; CLINICAL PATHWAYS; HEALTHCARE ABUSE; HEALTHCARE FRAUD;

DATA MINING;

EID: 33644780888     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2005.09.003     Document Type: Article

References (31)

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. Proceedings of the international conference on very large data bases, Santiago, Chile, June (pp. 487-499).
2. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. Proceedings of international conference on data engineering, Taipei, Taiwan, March (pp. 3-14).
3. H. Almuallim, and T. Dietterich Learning Boolean concepts in the presence of many irrelevant features Artificial Intelligence 69 1/2 1994 297 305
4. A. Blum, and P. Langley Selection of relevant features and examples in machine learning Artificial Intelligence 97 1/2 1997 245 271
5. Caruana, R., & Freitag, D. (1994). Greedy attribute selection. Proceedings of international conference on machine learning, New Brunswick, NJ (pp. 28-36).
6. Chan, C. L., & Lan, C. H. (2001). A data mining technique combining fuzzy sets theory and Bayesian classifier - An application of auditing the health insurance fee. Proceedings of the international conference on artificial intelligence (pp. 402-408).
7. E. Cox A fuzzy system for detecting anomalous behaviors in healthcare provider claims Intelligent system for finance and business 1995 Wiley New York, NY pp. 111-134
8. J. Frieden Health care fraud detection enters the information age Business and Health 1992 29 32 June
9. C.P. Friedman, and J.C. Wyatt Evaluation methods in medical informatics 1997 Springer Berlin
10. Guidelines to health care fraud Guidelines to health care fraud National Health Care Anti-Fraud Association (NHCAA), NHCAA Board of Governors 1991 http://www.nhcaa.org
11. C. Hall Intelligent data mining at IBM: New products and applications Intelligent Software Strategies 7 5 1996 1 16
12. H. He, J. Wang, W. Graco, and S. Hawkins Application of neural networks to detection of medical fraud Expert Systems with Applications 13 4 1997 329 336
13. Health care fraud Health care fraud: A serious and costly reality for all Americans National Health Care Anti-Fraud Association (NHCAA), REPORT all_about_hcf 2002 http://www.nhcaa.org
14. W.L. Healy, M.E. Ayers, R. Iorio, D.A. Patch, D. Appleby, and B.A. Pfeifer Impact of a clinical pathway and implant standardization on total hip arthroplasty: A clinical and economic study of short-term patient outcome The Journal of Arthroplasty 13 3 1998 266 276
15. W. Herb, and M. Tom A scientific approach for detecting fraud Best's Review 95 4 1995 78 81
16. S.Y. Hwang, C.P. Wei, and W.S. Yang Process mining: Discovery of temporal patterns from process instances Computers in Industry 53 3 2004 345 364
17. C.L. Ireson Critical pathways: Effectiveness in achieving patient outcomes The Journal of Nursing Administration 27 6 1997 16 23
18. John, G., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. Proceedings of international conference on machine learning (pp. 121-129).
19. Kira, K., & Rendell, L. (1992). The feature selection problem: traditional methods and a new algorithm. Proceedings of the conference on artificial intelligence, San Jose, CA (pp. 129-134).
20. Koller, D., & Sahami, M. (1996). Toward optimal feature selection. Proceedings of international conference on machine learning, Bari, Italy, July (pp. 282-292).
21. Langley, P., & Sage, S. (1994). Induction of selective Bayesian classifiers. Proceedings of the AAAI symposium on relevance, Seattle, WA (pp. 399-406).
22. M. Lassey, W. Lassey, and M. Jinks Health care systems around the world: Characteristics, issues, reforms 1997 Prentice-Hall Englewood Cliffs, NJ
23. N. Lavrac, and N. Lavrac Selected techniques for data mining in medicine Artificial Intelligence in Medicine 16 1 1999 3 23
24. Liu, B., Hsu, W., & Ma, Y. (1998). Integrating classification and association rule mining. Proceedings of international conference on knowledge discovery and data mining, New York (pp. 80-86).
25. J. Major, and D. Riedinger EFD: Heuristic statistics for insurance fraud detection Intelligent system for finance and business 1995 Wiley New York, NY pp. 145-164
26. J. Pearl Probabilistic reasoning in intelligent systems 1988 Morgan Kaufmann Los Altos, CA
27. B.B. Pflaum, and J.S. Rivers Employer strategies to combat health care plan fraud Benefits Quarterly 7 1 1991 6 14
28. L. Sokol, B. Garcia, J. Rodriguez, M. West, and K. Johnson Using data mining to find fraud in HCFA health care claims Top Health Information Management 22 1 2001 1 13
29. Sokol, L., Garcia, B., West, M., Rodriguez, J., & Johnson, K. (2001). Precursory steps to mining HCFA health care claims. Proceedings of the Hawaii international conference on system sciences, Hawaii (pp. 6019).
30. United States General Accounting Office (1992). Health insurance: Vulnerable payers lose billions to fraud and abuse. GAO-HRD-92-69.
31. C.P. Wei, S.Y. Hwang, and W.S. Yang Mining frequent temporal patterns in process databases Proceedings of international workshop on information technologies and systems 2000 Brisbane Australia (pp. 175-180)