Health Care Analytics: The latest weapon in fighting the opioid abuse epidemic


Rena Bielinski, Pharm.D., A.H.F.I.

According to the Institute of Medicine of the National Academies, 100 million Americans, or nearly one out of every three people, suffer each day with chronic pain. That's roughly quadruple the number of Americans with diabetes (25.8 million), and nearly 10 times as many as the number of cancer patients (11.9 million).

Fortunately, we live in an era when modern medicine offers effective and readily available treatment for pain in the form of prescription medications. Prescription painkillers help improve daily function, and therefore the quality of life, for millions of Americans and even more people across the globe.

These benefits, however, come at a cost. According to the Centers for Disease Control and Prevention, the U.S. is now in the midst of an opioid abuse epidemic. In 2010, 5.1 million Americans abused painkillers to some degree, which made them the most abused prescription medications by far, according to the National Institute on Drug Abuse. In addition, the 2012 National Survey on Drug Use and Health stated that 2.1 million Americans were addicted to opioid pain relievers. And in 2013, opioid analgesics caused more than 16,000 deaths, far more than any other drug class.

The problem hasn't gone unnoticed: As of May 2015, the U.S. government had 540 pending complaints and cases involving fraud, waste and abuse (FWA) in prescription drug billing related to Medicare and Medicaid. These cases account for 60 percent of the FWA total, and don't take into account instances with commercial insurance.

Why is it such a challenge to control opioid abuse? It's a combination of the sheer number of pharmacy claims and the woefully outdated manual methods used to review them. The slow, labor-intensive process of manually inspecting spreadsheets, even those generated from a database, can lead to false positives. It also uses time and resources that should be spent tracking down those who are actually committing FWA. The sheer size of data can cause processing time and infrastructure issues, and overwhelm the system.

Next-generation analytics overcome these challenges by using multiple data points — more than humans can process at one time — to identify and uncover purchasing and prescribing patterns that indicate a high probability of abuse. Experts can then focus their time evaluating actionable insights rather than sifting through data to determine those members or prescribers to target.

Here's how analytics can help in two key areas.

Member drug-seeking behavior

Analytics make it easier to find behaviors that are unusual. Rather than paging through spreadsheets, color-coded dashboards can assign scores based on risk factors and bring the most likely cases of FWA to the top of the list based on pre-set thresholds, such as health plan members who are seeing more than 10 physicians or filling prescriptions at more than 10 pharmacies. These thresholds can be set based on industry benchmarks or adjusted to the preferences of the payer or pharmacy benefit manager (PBM).

One of the challenges of uncovering FWA among members is that on the surface, the patterns that could indicate it might also reflect legitimate (non-FWA) behavior. For example, a common indicator of potential fraud is when a patient receives an opioid and/or other prescription from multiple providers and fills them at different pharmacies. Yet an oncology patient who receives multiple prescriptions from several different specialists might have a legitimate reason for this behavior.

This is where next-generation analytics brings in additional data, such as displaying the locations of prescribers and pharmacies on a map relative to the member's home. If several prescriptions are being filled at different locations far from the member's home, it's a strong indicator of possible FWA.

The intelligent application of analytics will help automate the process of revealing the most likely FWA perpetrators while minimizing false positives, which ensures that the payer's or PBM's resources are being used most effectively to reduce costs while not alienating members in good standing.

Read the full article and discover the other area where analytics can help on