Why life cycle management matters for health care fraud analysis


Due to the rapid changes in both the health care market and more specifically the amount of fraud being committed in it, it is even more important than ever to maintain some degree of life cycle management to update the analyses used to detect and identify aberrant activity.

However, many organizations do not have the resources or the expertise to maintain the performance of their analytical methods. Quite a few organizations build transactional rules or models and then continue to use them for years (in some cases four or five years). And even though the analysis has degraded (sometimes by as much as 60 percent), they continue to use the same analytical methods year after year.

Without proper tools and methodologies in place, it is very difficult to manually maintain fraud analytical methods.

One of the few statistical truisms is that all models and analytical methods degrade over time, and this is even more true for fraudulent models. This is largely due to fact that the entities committing fraud or abuse adjust their behavior to avoid detection by said models.

When the models or analytical methods become less effective many more false positives are created and more money is lost.

The whole scenario very mush becomes an issue of the cart before the horse: because organizations are so busy chasing the leads they have, they don’t have the time to improve the quality of the leads they detect and identify.

As mentioned previously, many organizations cannot address this issue with their current resources, hence organizations should seriously consider acquiring either more resources or technology that can evaluate the performance of their analytical methods on an on-going basis.

None of this is easy, and there will be pain in introducing new processes to make sure that detection methods are more accurate, but the alternative is far worse and ultimately, it defeats the purpose of the having the analytical methods in the first place (if the methods become wildly inaccurate).

Be honest: when was the last time you updated your fraud detection models? And if you have updated your models, what strategies did you use to help carve out the time?


About Author

Ross Kaplan

Principal Solutions Architect

Ross Kaplan serves as the Principal Global Solutions Architect for Health Care in the SAS Security Intelligence global practice. He supports health care cost containment (Payment Integrity) initiatives across the Health & Life Sciences, State and Local Government, and Federal Government verticals. He has been active across North America, Europe, Middle East, Asia Pacific and South Africa. Providing industry expertise and vision at conferences and directly to customers, Ross has been at SAS for over eight years Ross is a 16 year veteran in the health insurance industry, focusing on analytics in health and condition management, member retention, and provider profiling prior to specializing in health care. He has assisted health plans, federal and State and local government agencies in defining their requirements and providing guidance in their solution advancement. Ross is also trained and experience in Healthcare privacy laws. Prior to SAS, Ross served as a solutions architect at Computer Associates and Siebel Systems, working with the Fortune 1000. He has supported other industries such as Insurance, Banking, and Pharmaceutical. However, his primary focus has always been in health care, receiving training in HIPAA and having direct input in Siebel’s health care product development. Ross has been featured speaker at many industry events focused on health care cost containment and payment integrity, most recently on the topic of social network analysis and link analysis, predictive analytics, and fraud/waste and abuse in the European market. Ross earned a bachelor's degree in Business Administration, with a concentration in Computer Information Systems (CIS) from San Francisco State University and his Master’s Degree in Statistics as well as an MBA with a concentration in Systems Analysis. Sales Training: • Consultative Selling • The Customer Delight Principal • Major Account Sales Strategy

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