When Ann Maxwell, assistant inspector general of the Department of Health and Human Services (HHS) Office of Inspector General (OIG) testified before the Congressional Committee on Energy and Commerce's subcommittee on oversight and investigation in May of 2016, the new was sobering.
Maxwell said the U.S. government misspent more than $88 billion in 2015 on Medicare and Medicaid. That's more than the individual gross domestic product (GDP) of roughly two-thirds of the world's countries in that same year.
The largest number of fraud, waste and abuse (FWA) cases overall today involves prescription drugs, with the opioid epidemic leading the way. As of May 2015, in fact, the government had 540 pending complaints and cases involving FWA in prescription drug billing, 60 percent of the total cases and a 134 percent increase over five years. And that doesn't even take commercial instances into account.
Why the sharp increase despite increased awareness of and focus on the problem? It's a combination of the sheer number of pharmacy claims, which number in the millions each month, and the woefully outdated manual methods being used to review them. Manually inspecting spreadsheets is a slow, labor-intensive process; in the meantime, FWA continues. In addition, manual methods lead to many false positives, taking up time and resources that should be spent tracking down those who actually are committing FWA. The system is simply overwhelmed.
There is hope on the horizon, however. Next-generation analytics solve this issue by using multiple big data points – more than humans 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. Here are some of the ways big data is helping in the fight against prescription drug FWA.
• Evaluating patient/member behavior – Two of the key indicators that a patient or health plan member may be abusing prescriptions is that he/she is receiving prescriptions from multiple physicians, or is filling prescriptions at multiple pharmacies. This behavior might be difficult to spot on a spreadsheet, but big data analytics can quickly bring it to the attention of investigators. By settling thresholds based on industry benchmarks and then adjusting them to the provider's or payer's norms, any activity that exceeds the threshold can be shown on a color-coded dashboard that assigns scores based on risk factors. Investigators can use the scores to prioritize the cases and use their limited time most effectively.
• Avoiding false positives for legitimate variances – Sometimes patient/member behaviors that seem to be outside the norms based on one set of data turn out to have legitimate reasons for that activity when you look deeper. Take the previously-cited example of a patient/member receiving multiple prescriptions from multiple providers and filling them at different pharmacies. While it is unusual in most cases, it may be acceptable for an oncology patient who is seeing several different specialists.
• Incorporating non-clinical data for more detailed answers – Most FWA detection efforts focus around clinical or claims data. Yet incorporating non-traditional data into the analytics can deliver a clearer picture of unusual patterns that are worth another look. For example, showing the locations of prescribers and pharmacies on a map relative to the patient's/member's home can indicate if the patient/member is traveling unusually far to obtain or fill prescriptions. This sort of behavior is often an indicator of FWA and would be prioritized highly. In addition, overlapping claims data with consumer data (such as socioeconomic) can help with any interventions that the payer may want to implement with a particular member.
• Determining normal patterns for pharmacies – On the pharmacy side, analytics are being used to establish the normal patterns of dispensing prescriptions over a specified period of time to create benchmarks. From there, transactional analytics automatically detect significant deviations from the benchmarks, especially those that repeat from week to week or month to month. The most sophisticated analytics even take it a step further, ranking the anomalies based on severity to determine which require immediate attention, which aren't over the line yet but should be on a "watch list," and which may just have had an unusual week. The use of these analytics also helps payers and pharmacy benefit managers (PBMs) comply with the Centers for Medicare and Medicaid Services' (CMS) monitoring of risk lists.
• Uncovering FWA networks – As FWA around prescriptions becomes an ever-larger dollar business and perpetrators become more sophisticated, incidents have been moving from individual actors to coordinated networks where information on "beating the system" is shared. These networks of activity can be much more difficult to detect with manual methods, meaning shutting down one pharmacy or provider location may not be enough. Next-generation analytics again use mapping and other techniques to determine if there is a concentration of FWA around one geographic location, a particular provider, patient or pharmacy, or some other common thread. Armed with this information, investigators can shut down the entire network, delivering significant savings immediately while helping prevent future incidents.
• Bringing flexibility – One of the most important aspects next-generation analytics are bringing to the fight against FWA is flexibility. Once an avenue of FWA is uncovered and shut down, those committing it simply look for new holes to exploit. They are constantly evolving, so organizations looking to stop it must constantly evolve with them. Yet it can take a long time to revamp manual inspection methods and deploy them into the field. The ability to adjust the analytics quickly and alter how the information is being displayed to make it easier to use, helps organizations focus their efforts where they will deliver the greats benefits and ROI while limiting wasted effort for false positives.
As long as prescription medications remain a nearly $375 billion industry in the U.S., there will be plenty of financial incentives for FWA. Most of those committing it count on their ability to fly under the radar and get lost in the weeds.
Next-generation analytics are making it easier to put FWA criminals back on the radar and eliminate their ability to hide in plain sight. They are our best opportunity to reverse this costly trend in order to reduce costs, improve care, and most of all save lives by stopping the plague of rampant prescription drug abuse.
Rena Bielinski, Pharm.D., AHFI is senior vice president and chief pharmacy officer at SCIO Health Analytics®, an organization dedicated to using healthcare 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. She can be reached at rbielinsky@sciohealthanalytics.com.
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