By Rena Bielinski

In W. W. Jacob’s supernatural tale The Monkey’s Paw, the person holding the titular charm is granted three wishes. It doesn’t take long, however, for that person to discover that every wish is also accompanied by a curse. So it is in the world of business. The granted wish was the ability to gather, process and exchange data more easily to improve efficiency and service customers better. We have that in abundance. The curse that has come with data, however, is a marked increase not only in the volume of fraud, waste and abuse (FWA), but also in the relative ease with which FWA can be perpetrated.

In other words, you used to have to be a professional with some level of skill (and bravado) to be a criminal. Now all you need is access to the Internet. This has certainly been the experience in health care. Over the last five to 10 years, there have been significant advances in the digital transformation of health care, such as the move from paper to electronic health records and the increased use of telemedicine.



These advances have resulted in an improvement in efficiency, quality and safety, yet they have also created new opportunities for health care fraud and medical identity theft. One area that is particularly troubling is in the fraud, waste and abuse of prescription pharmaceuticals — particularly opioids (pain killers), which present not only a financial risk, but a significant public health hazard when they fall into the wrong hands.

Spending for these medications among Medicare Part D beneficiaries alone increased 156 percent from 2006 to 2014. The total number of beneficiaries receiving opioids grew by 92 percent in that time, compared to 68 percent for all other drugs. These figures translate into $7.8 billion being spent on controlled substances.

So here’s the rub: if just 1 percent of the nearly $375 billion being spent on all prescriptions in the U.S. is fraudulent, that translates into a $3.75 billion loss. That puts it into the same financial league as credit card fraud, but with much more dire consequences for the nation than someone ordering a free computer or booking an unauthorized vacation on someone else’s dime.

As in many industries, one of the challenges with detecting FWA of prescription drugs in a timely manner has been a reliance on manual review methods — in this case, poring over spreadsheets to look for patterns. Further exacerbating the problem, these manual methods can lead to many false positives, taking up time that could and should be spent on tracking down real abusers of the system.

That is now changing, as next-generation analytics begin to automate the review process. These analytics use multiple data points — more than a human can process at one time — to identify and surface purchasing and prescribing patterns that offer a high probability of abuse. Experts can then focus their time evaluating actionable insights rather than sifting through data to determine which members or prescribers to target. Following are the strategies being used by health plans and pharmacy benefit managers (PBMs) to discover this fraud, waste and abuse.

Discovering unusual consumer behavior
After setting a baseline of what is considered “normal” behavior, analytics are being used by health plans to churn through dozens of data points to find behaviors that fall outside the norm. Some examples are consumers who are seeing more than 10 physicians or filling prescriptions at more than 10 pharmacies.

Color-coded dashboards are then assigning scores based on risk factors to bring the most likely cases of FWA to the top. Yet it’s not quite that simple, as sometimes these patterns of unusual behavior may be legitimate. While a patient receiving opioid prescriptions from multiple providers and filling them at different pharmacies can be an indication of FWA, a cancer patient who is seeing several specialists may have a valid reason for doing so.

To help separate these patients from the abusers, next-generation analytics are bringing in additional data, such as displaying the locations of the prescribers and pharmacies on a map relative to the patient’s home. If multiple prescriptions are being filled at locations far from the patient’s home, it’s generally a strong indicator of FWA. By automating this process and using all the data at their disposal wisely, health plans and PBMs can focus their efforts more effectively while being sure not to alienate members in good standing.

At the store (pharmacy) level
Despite tight governmental control over pharmaceuticals, there is still plenty of opportunity for FWA because of the complexities involved. Next-generation analytics are helpful in uncovering this activity by establishing a benchmark of patterns over a specified time period, such as a year, and then monitoring activities against that benchmark each week going forward.

If there are significant deviations from the benchmark, those pharmacies are highlighted on a color-coded dashboard to determine which require immediate action, which should be on the “watch list” and which may have just had an unusual week. The analytics also enable health plans and PBMs to comply with the Centers for Medicare and Medicaid Services (CMS)-required monitoring of “watch lists." Among the metrics that can be monitored are:

• Rate of “new billing."
• Reversal rate (very high and very low).
• Percentage of member copays.
• Average ingredient cost.
• Average paid subscription.
• Average number of prescriptions per member (stratified by age).
• Percentage of controlled substances.
• Average dollars paid per member.

By highlighting the results on the dashboard, it becomes easier to identify overall trends. It is also easier to identify pharmacies that may require corrective interventions such as pending (holding) claims or withholding payment, as well as those that require an on-site visit or other more severe actions. It is similar to finding retail stores claiming excessive losses that would be otherwise difficult to discover.

The importance of flexibility
As in most industries, 90 percent of FWA analytics in health care tend to be the same. The other 10 percent, however, are often critical to an individual organization — so it is crucially important that the analytics being used have the flexibility to meet those more specialized demands.

Retail pharmacies, for example, may want the ability to configure the analytics to compare the performance of different locations to those of its competitors to determine if a one- or two-week spike in controlled substances is specific to its organization, or is being seen by other retailers as well. This ability to adjust what is being measured and how the information is being displayed, is helping health payers and PBMs focus their efforts where they will yield the greatest benefit and ROI, while at the same time limiting wasted effort chasing false positives. This is a far more effective approach than trying to investigate every potential incident.

Overcoming the curse
The information age has accelerated the pace and reach of business to drive greater profitability. But it has also opened organizations to greater risks. Next-generation analytics that are able to automatically identify significant deviations from the norm, and then apply additional logic or parameters to separate the concerning from the merely odd, can remove the curse of the monkey’s paw from the vulnerabilities inherent in its data today. And that will help keep organizations from increasingly falling victim to fraud, waste and abuse of prescription drugs.

About the author: Rena Bielinski, PharmD, AHFI, is senior vice president and chief pharmacy officer at SCIO Health Analytics, an organization dedicated to using health care analytics to improve clinical outcomes, operational performance and business results. Dr. Bielinski has more than 20 years of experience in managing clinical and pharmaceutical data integrity, and is an accredited health care fraud investigator.