THE IMPACT OF HEALTHCARE FRAUD AND ABUSE REGULATIONS ON ADMINISTRATION PRACTICES
Main Article Content
Keywords
healthcare, data mining, knowledge discovery in databases (KDD), Business Intelligence (BI), insurance claim, fraud detection
Abstract
Insurance companies or third party payers make inappropriate payments due to mistakes, misuse, and fraud. The magnitude of this problem warrants its classification as a high-priority concern for health systems. Conventional approaches to identifying instances of healthcare fraud and abuse are laborious and ineffective. The integration of automated techniques with statistical expertise has resulted in the development of a new multidisciplinary field known as Knowledge Discovery from Databases (KDD). Data mining is a fundamental component of the Knowledge Discovery in Databases (KDD) process. Data mining enables third-party payers, such as health insurance companies, to extract valuable insights from a large volume of claims and select a smaller fraction of claims or claimants that need further evaluation. We conducted a comprehensive analysis of research that used data mining techniques to identify instances of health care fraud and abuse. These studies included both supervised and unsupervised data mining methods. The majority of existing research have mostly concentrated on algorithmic data mining, neglecting the specific application of fraud detection in the field of health care supply or health insurance policy. Further research is required to establish a correlation between sound and evidence-based methods of diagnosing and treating fraudulent or abusive activities. Based on the existing research, we ultimately suggest following seven broad methods for data mining of health care claims.
References
1. Aral K. D, Güvenir H. A, Sabuncuoğlu İ, Akar A. R. A prescription fraud detection model. Computer Methods and Programs in Biomedicine. 2012;106(1):37–46.
2. Bolton R. J, Hand D. J. Statistical fraud detection:A review. Statistical Science. 2002. pp. 235–249.
3. Breunig M. M, Kriegel H-P, Ng R. T, Sander J. LOF:Identifying Density-Based Local Outliers. in Proceedings of the ACM SIGMOD 2000. Dallas, Texas: International Conference on Management of Data; 2000. pp. 93–104.
4. Busch R. S. Healthcare fraud:auditing and detection guide. New Jersey: John Wiley and Sons, Inc; 2007.
5. Capelleveen G. C. Outlier based predictors for health insurance fraud detection within US Medicaid. 2013. Retrieved June 16, 2014 from http://purl.utwente.nl/essays/64417 .
6. Centers for Medicare and Medicaid Services. 2014. Retrieved May 21, 2014 from http://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/Fraud_and_Abuse.Pdf .
7. Chaix-Couturier C, Durand-Zaleski I, Jolly D, Durieux P. Effects of financial incentives on medical practice:results from a systematic review of the literature and methodological issues. International Journal for Quality in Health Care. 2000;12(2):133–142.
8. Chee T, Chan L. K, Chuah M. H, Tan C. S, Wong S. F, Yeoh W. Business intelligence systems:state-of-the-art review and contemporary applications. In Symposium on Progress in Information and Communication Technology. 2009;2(4):16–3.
9. Copeland L, Edberg D, Panorska A. K, Wendel J. Applying business intelligence concepts to Medicaid claim fraud detection. Journal of Information Systems Applied Research. 2012;5(1):51.
10. Ekina T, Leva F, Ruggeri F, Soyer R. Application of Bayesian Methods in Detection of Healthcare Fraud. In Chemical Engineering Transaction. 2013;33
11. Esfandiary N, Babavalian M. R, Moghadam A. M. E, Tabar V. K. Knowledge Discovery in Medicine:Current Issue and Future Trend. Expert Systems with Applications. 2014.
12. Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI magazine. 1996;17(3):37.
13. Fisher C, Lauría E, Chengalur-Smith S. Introduction to information quality. Bloomington: Authorhouse; 2012.
14. Furlan Š, Bajec M. Holistic approach to fraud management in health insurance. Journal of Information and Organizational Sciences. 2008;32(2):99–114.
15. Gee J, Button M, Brooks G, Vincke P. The financial cost of healthcare fraud (Internet) Portsmouth: University of Portsmouth, MacIntyre Hudson, Milton Keynes; 2010. Available:Retrieved December 20, 2010
16. He H, Graco W, Yao X. Simulated Evolution and Learning. Springer Berlin Heidelberg; 1999. Application of genetic algorithm and k-nearest neighbour method in medical fraud detection; pp. 74–81.
17. Kirlidog M, Asuk C. A Fraud Detection Approach with Data Mining in Health Insurance. Procedia-Social and Behavioral Sciences. 2012;62:989–994.
18. Kumar M, Ghani R, Mei Z. S. Data mining to predict and prevent errors in health insurance claims processing. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. 2010. Jul, pp. 65–74.
19. Li J, Huang K. Y, Jin J, Shi J. A survey on statistical methods for health care fraud detection. Health Care Management Science. 2008;11(3):275–287.
20. Lin C, Lin C. M, Li S. T, Kuo S. C. Intelligent physician segmentation and management based on KDD approach. Expert Systems with Applications. 2008;34(3):1963–1973.
21. Liou F. M, Tang Y. C, Chen J. Y. Detecting hospital fraud and claim abuse through diabetic outpatient services. Health Care Management Science. 2008;11(4):353–358.
22. Liu Q, Vasarhelyi M. In 29th world continuous auditing and reporting symposium. Brisbane, Australia: 29WCARS; 2013. Healthcare fraud detection:A survey and a clustering model incorporating Geo-location information.
23. Maimon O.Z, Rokach L. Data mining and knowledge discovery handbook. 2nd ed. New York: Springer; 2005
24. Major J. A, Riedinger D. R. EFD:A hybrid knowledge/statistical based system for the detection of fraud. International Journal of Intelligent Systems. 1992;7(7):687–703.
25. Major J. A, Riedinger D. R. EFD:A Hybrid Knowledge/StatisticalBased System for the Detection of Fraud. Journal of Risk and Insurance. 2002;69(3):309–324.
26. Musal R. M. Two models to investigate Medicare fraud within unsupervised databases. Expert Systems with Applications. 2010;37(12):8628–8633.
27. Ngufor C, Wojtusiak J. Unsupervised labeling of data for supervised learning and its application to medical claims prediction. Computer Science. 2013;14(2):191.
28. Ogwueleka F. N. Medical fraud detection system in health insurance schemes using link and basket analysis algorithm. Journal of Sciences, Technology, Mathematics and Education. 2012:70.
29. Ormerod T, Morley N, Ball L, Langley C, Spenser C. Using ethnography to design a Mass Detection Tool (MDT) for the early discovery of insurance fraud. In CHI’03 Extended Abstracts on Human Factors in Computing Systems. ACM. 2003. Apr, pp. 650–651.
30. Ortega P. A, Figueroa C. J, Ruz G. A. A Medical Claim Fraud/Abuse Detection System based on Data Mining:A Case Study in Chile. DMIN. 2006;6:26–29.
31. Phua C, Lee V, Smith K, Gayler R. A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119. 2010
32. Rashidian A, Joudaki H, Vian T. No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse:A Systematic Review of Literature. PlOS One. 2012;7(8):e41988.
33. Shan Y, Jeacocke D, Murray D. W, Sutinen A. J. F Roddick, J Li, P Christen, P Kennedy., editors. Mining medical specialist billing patterns for health service management. Conferences in Research and Practice in Information Technology. 2008. pp. 105–110
34. Shan Y, Murray D. W, Sutinen A. In Proceedings of the Eighth Australasian Data Mining Conference. Vol. 101. Australian Computer Society, Inc; 2009. Discovering inappropriate billings with local density based outlier detection method; pp. 93–98.
35. Shin H, Park H, Lee J, Jhee W. C. A scoring model to detect abusive billing patterns in health insurance claims. Expert Systems with Applications. 2012;39(8):7441–7450.
36. Sokol L, Garcia B, Rodriguez J, West M, Johnson K. Using data mining to find fraud in HCFA health care claims. Topics in Health Information Management. 2001;22(1):1–13.
37. Sparrow M. K. Health care fraud control:understanding the challenge. Journal of insurance medicine- New York- 1996;28:86–96.
38. Tang M, Mendis B. S. U, Murray D. W, Hu Y, Sutinen A. In Proceedings of the Ninth Australasian Data Mining Conference. Vol. 121. Australian Computer Society, Inc; 2011. Dec, Unsupervised fraud detection in Medicare Australia; pp. 103–110.
39. Thornton D, Mueller R. M, Schoutsen P, van Hillegersberg J. Predicting Healthcare Fraud in Medicaid:A Multidimensional Data Model and Analysis Techniques for Fraud Detection. Procedia Technology. 2013;9:1252–1264.
40. Travaille P, Müller R. M, Thornton D, Hillegersberg J. Electronic fraud detection in the US medicaid healthcare program:lessons learned from other industries. Proceedings of the Seventeenth Americas Conference on Information System s, Detroit, Michigan August 4th-7th 2011. 2011
41. Williams G. J, Huang Z. Springer Berlin Heidelberg; 1997. Mining the knowledge mine. In Advanced Topics in Artificial Intelligence; pp. 340–348.
42. Yamanishi K, Takeuchi J. I, Williams G, Milne P. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery. 2004;8(3):275–300.
43. Yang W. S, Hwang S. Y. A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications. 2006;31(1):56–68.
44. Yoo I, Alafaireet P, Marinov M, Pena-Hernandez K, Gopidi R, Chang J. F, Hua L. Data mining in healthcare and biomedicine:a survey of the literature. Journal of medical systems. 2012;36(4):2431–2448
45. Zeng L, Xu L, Shi Z, Wang M, Wu W. Techniques, Process, and Enterprise Solutions of Business Intelligence. In SMC. 2006 Oct;:4722–4726.
2. Bolton R. J, Hand D. J. Statistical fraud detection:A review. Statistical Science. 2002. pp. 235–249.
3. Breunig M. M, Kriegel H-P, Ng R. T, Sander J. LOF:Identifying Density-Based Local Outliers. in Proceedings of the ACM SIGMOD 2000. Dallas, Texas: International Conference on Management of Data; 2000. pp. 93–104.
4. Busch R. S. Healthcare fraud:auditing and detection guide. New Jersey: John Wiley and Sons, Inc; 2007.
5. Capelleveen G. C. Outlier based predictors for health insurance fraud detection within US Medicaid. 2013. Retrieved June 16, 2014 from http://purl.utwente.nl/essays/64417 .
6. Centers for Medicare and Medicaid Services. 2014. Retrieved May 21, 2014 from http://www.cms.gov/Outreach-and-Education/Medicare-Learning-Network-MLN/MLNProducts/downloads/Fraud_and_Abuse.Pdf .
7. Chaix-Couturier C, Durand-Zaleski I, Jolly D, Durieux P. Effects of financial incentives on medical practice:results from a systematic review of the literature and methodological issues. International Journal for Quality in Health Care. 2000;12(2):133–142.
8. Chee T, Chan L. K, Chuah M. H, Tan C. S, Wong S. F, Yeoh W. Business intelligence systems:state-of-the-art review and contemporary applications. In Symposium on Progress in Information and Communication Technology. 2009;2(4):16–3.
9. Copeland L, Edberg D, Panorska A. K, Wendel J. Applying business intelligence concepts to Medicaid claim fraud detection. Journal of Information Systems Applied Research. 2012;5(1):51.
10. Ekina T, Leva F, Ruggeri F, Soyer R. Application of Bayesian Methods in Detection of Healthcare Fraud. In Chemical Engineering Transaction. 2013;33
11. Esfandiary N, Babavalian M. R, Moghadam A. M. E, Tabar V. K. Knowledge Discovery in Medicine:Current Issue and Future Trend. Expert Systems with Applications. 2014.
12. Fayyad U, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. AI magazine. 1996;17(3):37.
13. Fisher C, Lauría E, Chengalur-Smith S. Introduction to information quality. Bloomington: Authorhouse; 2012.
14. Furlan Š, Bajec M. Holistic approach to fraud management in health insurance. Journal of Information and Organizational Sciences. 2008;32(2):99–114.
15. Gee J, Button M, Brooks G, Vincke P. The financial cost of healthcare fraud (Internet) Portsmouth: University of Portsmouth, MacIntyre Hudson, Milton Keynes; 2010. Available:Retrieved December 20, 2010
16. He H, Graco W, Yao X. Simulated Evolution and Learning. Springer Berlin Heidelberg; 1999. Application of genetic algorithm and k-nearest neighbour method in medical fraud detection; pp. 74–81.
17. Kirlidog M, Asuk C. A Fraud Detection Approach with Data Mining in Health Insurance. Procedia-Social and Behavioral Sciences. 2012;62:989–994.
18. Kumar M, Ghani R, Mei Z. S. Data mining to predict and prevent errors in health insurance claims processing. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. 2010. Jul, pp. 65–74.
19. Li J, Huang K. Y, Jin J, Shi J. A survey on statistical methods for health care fraud detection. Health Care Management Science. 2008;11(3):275–287.
20. Lin C, Lin C. M, Li S. T, Kuo S. C. Intelligent physician segmentation and management based on KDD approach. Expert Systems with Applications. 2008;34(3):1963–1973.
21. Liou F. M, Tang Y. C, Chen J. Y. Detecting hospital fraud and claim abuse through diabetic outpatient services. Health Care Management Science. 2008;11(4):353–358.
22. Liu Q, Vasarhelyi M. In 29th world continuous auditing and reporting symposium. Brisbane, Australia: 29WCARS; 2013. Healthcare fraud detection:A survey and a clustering model incorporating Geo-location information.
23. Maimon O.Z, Rokach L. Data mining and knowledge discovery handbook. 2nd ed. New York: Springer; 2005
24. Major J. A, Riedinger D. R. EFD:A hybrid knowledge/statistical based system for the detection of fraud. International Journal of Intelligent Systems. 1992;7(7):687–703.
25. Major J. A, Riedinger D. R. EFD:A Hybrid Knowledge/StatisticalBased System for the Detection of Fraud. Journal of Risk and Insurance. 2002;69(3):309–324.
26. Musal R. M. Two models to investigate Medicare fraud within unsupervised databases. Expert Systems with Applications. 2010;37(12):8628–8633.
27. Ngufor C, Wojtusiak J. Unsupervised labeling of data for supervised learning and its application to medical claims prediction. Computer Science. 2013;14(2):191.
28. Ogwueleka F. N. Medical fraud detection system in health insurance schemes using link and basket analysis algorithm. Journal of Sciences, Technology, Mathematics and Education. 2012:70.
29. Ormerod T, Morley N, Ball L, Langley C, Spenser C. Using ethnography to design a Mass Detection Tool (MDT) for the early discovery of insurance fraud. In CHI’03 Extended Abstracts on Human Factors in Computing Systems. ACM. 2003. Apr, pp. 650–651.
30. Ortega P. A, Figueroa C. J, Ruz G. A. A Medical Claim Fraud/Abuse Detection System based on Data Mining:A Case Study in Chile. DMIN. 2006;6:26–29.
31. Phua C, Lee V, Smith K, Gayler R. A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119. 2010
32. Rashidian A, Joudaki H, Vian T. No Evidence of the Effect of the Interventions to Combat Health Care Fraud and Abuse:A Systematic Review of Literature. PlOS One. 2012;7(8):e41988.
33. Shan Y, Jeacocke D, Murray D. W, Sutinen A. J. F Roddick, J Li, P Christen, P Kennedy., editors. Mining medical specialist billing patterns for health service management. Conferences in Research and Practice in Information Technology. 2008. pp. 105–110
34. Shan Y, Murray D. W, Sutinen A. In Proceedings of the Eighth Australasian Data Mining Conference. Vol. 101. Australian Computer Society, Inc; 2009. Discovering inappropriate billings with local density based outlier detection method; pp. 93–98.
35. Shin H, Park H, Lee J, Jhee W. C. A scoring model to detect abusive billing patterns in health insurance claims. Expert Systems with Applications. 2012;39(8):7441–7450.
36. Sokol L, Garcia B, Rodriguez J, West M, Johnson K. Using data mining to find fraud in HCFA health care claims. Topics in Health Information Management. 2001;22(1):1–13.
37. Sparrow M. K. Health care fraud control:understanding the challenge. Journal of insurance medicine- New York- 1996;28:86–96.
38. Tang M, Mendis B. S. U, Murray D. W, Hu Y, Sutinen A. In Proceedings of the Ninth Australasian Data Mining Conference. Vol. 121. Australian Computer Society, Inc; 2011. Dec, Unsupervised fraud detection in Medicare Australia; pp. 103–110.
39. Thornton D, Mueller R. M, Schoutsen P, van Hillegersberg J. Predicting Healthcare Fraud in Medicaid:A Multidimensional Data Model and Analysis Techniques for Fraud Detection. Procedia Technology. 2013;9:1252–1264.
40. Travaille P, Müller R. M, Thornton D, Hillegersberg J. Electronic fraud detection in the US medicaid healthcare program:lessons learned from other industries. Proceedings of the Seventeenth Americas Conference on Information System s, Detroit, Michigan August 4th-7th 2011. 2011
41. Williams G. J, Huang Z. Springer Berlin Heidelberg; 1997. Mining the knowledge mine. In Advanced Topics in Artificial Intelligence; pp. 340–348.
42. Yamanishi K, Takeuchi J. I, Williams G, Milne P. On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. Data Mining and Knowledge Discovery. 2004;8(3):275–300.
43. Yang W. S, Hwang S. Y. A process-mining framework for the detection of healthcare fraud and abuse. Expert Systems with Applications. 2006;31(1):56–68.
44. Yoo I, Alafaireet P, Marinov M, Pena-Hernandez K, Gopidi R, Chang J. F, Hua L. Data mining in healthcare and biomedicine:a survey of the literature. Journal of medical systems. 2012;36(4):2431–2448
45. Zeng L, Xu L, Shi Z, Wang M, Wu W. Techniques, Process, and Enterprise Solutions of Business Intelligence. In SMC. 2006 Oct;:4722–4726.
Article Sidebar
Published
Oct 11, 2022
Article Details
How to Cite
Fayhan Theeb Fayhan Alqahtani, Omar Nafel Alharbi, Hassan Yahya Salem Khard, Youssef Muhammad Shadeed Al-Harbi, Mansour Muhammad Shadeed Al-Harbi, Suleiman Al-Hamidi Shadeed Al-Harbi, Abdullah Banaider Alharthi, Abdulrahman Muayjil Almutai, Mohammed Ibrahim Zanguti, & Falah Salem Falah Alqahtani. (2022). THE IMPACT OF HEALTHCARE FRAUD AND ABUSE REGULATIONS ON ADMINISTRATION PRACTICES. Journal of Population Therapeutics and Clinical Pharmacology, 29(04), 3810-3815. https://doi.org/10.53555/jptcp.v29i04.5999
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Author Biographies
Fayhan Theeb Fayhan Alqahtani
Ksa, ministry of health
Omar Nafel Alharbi
Ksa, ministry of health
Hassan Yahya Salem Khard
Ksa, ministry of health
Youssef Muhammad Shadeed Al-Harbi
Ksa, ministry of health
Mansour Muhammad Shadeed Al-Harbi
Ksa, ministry of health
Suleiman Al-Hamidi Shadeed Al-Harbi
Ksa, ministry of health
Abdullah Banaider Alharthi
Ksa, ministry of health
Abdulrahman Muayjil Almutai
Ksa, ministry of health
Mohammed Ibrahim Zanguti
Ksa, ministry of health
Falah Salem Falah Alqahtani
Ksa, ministry of health
How to Cite
Fayhan Theeb Fayhan Alqahtani, Omar Nafel Alharbi, Hassan Yahya Salem Khard, Youssef Muhammad Shadeed Al-Harbi, Mansour Muhammad Shadeed Al-Harbi, Suleiman Al-Hamidi Shadeed Al-Harbi, Abdullah Banaider Alharthi, Abdulrahman Muayjil Almutai, Mohammed Ibrahim Zanguti, & Falah Salem Falah Alqahtani. (2022). THE IMPACT OF HEALTHCARE FRAUD AND ABUSE REGULATIONS ON ADMINISTRATION PRACTICES. Journal of Population Therapeutics and Clinical Pharmacology, 29(04), 3810-3815. https://doi.org/10.53555/jptcp.v29i04.5999