Description
Statistics and Health Care Fraud: How to Save Billions helps the public to become more informed citizens through discussions of real world health care examples and fraud assessment applications. The author presents statistical and analytical methods used in health care fraud audits without requiring any mathematical background. The public suffers from health care overpayments either directly as patients or indirectly as taxpayers, and fraud analytics provides ways to handle the large size and complexity of these claims.
The book starts with a brief overview of global healthcare systems such as U.S. Medicare. This is followed by a discussion of medical overpayments and assessment initiatives using a variety of real world examples. The book covers subjects as:
• Description and visualization of medical claims data
• Prediction of fraudulent transactions
• Detection of excessive billings
• Revealing new fraud patterns
• Challenges and opportunities with health care fraud analytics
Dr. Tahir Ekin is the Brandon Dee Roberts Associate Professor of Quantitative Methods in McCoy College of Business, Texas State University. His previous work experience includes a working as a statistician on health care fraud detection. His scholarly work on health care fraud has been published in a variety of academic journals including International Statistical Review, The American Statistician, and Applied Stochastic Models in Business and Industry. He is a recipient of the Texas State University 2018 Presidential Distinction Award in Scholar Activities and the ASA/NISS y-Bis 2016 Best Paper Awards. He has developed and taught courses in the areas of business statistics, optimization, data mining and analytics. Dr. Ekin also serves as Vice President of the International Society for Business and Industrial Statistics.
Table of Contents
1 Health Care Systems and Fraud
Overview
Health care systems
Worldwide health care insurance programs
Medical overpayments
Why health care fraud? Why now?
Impact and importance of fraud assessment
Types and examples of health care fraud
General fraud assessment framework and initiatives
Key takeaways
Additional resources
2 Describing Health Care Claims Data
Overview
Health care data
Understanding health care claims data
Data pre-processing
Descriptive statistical analysis
Discussion
Key takeaways
Additional resources
3 Sampling and Overpayment Estimation
Overview
Sampling and overpayment estimation
Sampling procedures
A closer look at stratified sampling
Overpayment estimation
Discussion
Key takeaways
Additional resources
4 Predicting Health Care Fraud
Overview
Health care fraud analytics
Predictive methods
Prediction of overpayment amount and fraud probability
Classification of health care claims
Accuracy and validation
Discussion
Key takeaways
Additional resources
5 Discovery of New Fraud Patterns
Overview
Outlier detection: Finding excessive billings
Clustering: Grouping health care claims
Association: Finding links among claims
Effectiveness of the analytical methods
Deployment via rules
Current efforts
Key takeaways
Additional resources
6 Challenges, Opportunities and Future Directions
Overview
Shareholders: putting a face on fraudsters and victims
Challenges with payment and fraud control systems
Organizational issues: "No news is good news!"
Evolution of fraud and adaptive fraudsters
Different sides of the coin: Data as a blessing, data as a curse
Legal concerns: Embracing uncertainty
A take on future
Key takeaways
Additional resources
Bibliography
Index
