Analytics Meet Healthcare: Leveraging Technology to Take Down Opioid 'Pill Mills'

Blog Post created by on May 1, 2017

The opioid abuse problem in America is growing out of control. Over 2 million Americans have a substance use disorder involving prescription pain relievers, and 91 Americans die every day from opioid abuse.


While there are many contributing factors to this epidemic, including the highly addictive nature of the drugs themselves, one leading cause is particularly nefarious. We call them ‘Pill Mills'. These are clinics and medical providers known for readily providing opioids directly and through prescription.  In other words, while the majority of physicians and caregivers fulfill their Hippocratic duty, there are a few bad apples who intentionally facilitate drug-seeking behavior for increased profits.


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Figure 1 - Total opioid overdose hospitalization costs admissions 2011-12 HOD= heroin overdose. POD=prescription opioid overdose


How does a Pill Mill work? A clinic creates a very low barrier to receive opioids. The doctors, physician assistants (PA), or nurse practitioners (NP), employed at the clinic show a cavalier attitude towards dispensing narcotic medicine, generously believing a patient’s description of pain with minimal evidence. Easy acceptance of patients’ stated allergies to non-narcotic medicine like ibuprofen, naproxen sodium, and acetaminophen puts stronger drugs, like Fentanyl, on the table for even minor pains.


The motivating factor for the clinic is money: as more patients use this clinic, word of mouth spreads knowledge about the provider’s willingness to dispense drugs, so that even more patients seek out this provider. Now the clinic has many more patient visits to bill insurance companies. Often the prescriptions will be written in very low amounts, requiring the patient come in for another office visit to receive more.


Pill Mills cost insurance companies an estimated $50-70 billion per year, and detecting them is difficult. Insurance companies don’t want to falsely accuse hardworking legitimate providers. So to proactively tamp down on America’s opioid epidemic, as well as protect their bottom line, insurers are turning to technology and data driven approaches to find the needles in the haystack.


While most organizations’ policies can detect a single provider billing narcotic prescriptions at an unusual rate, collusion is more difficult to identify. This is a weakness that Pill Mills are able to capitalize on; they will leverage shared provider IDs across the clinic to normalize the rate of narcotic claims by any single provider.


For example, a clinic with two doctors, three PAs, and two NPs could all be colluding together as a Pill Mill. To do this, one doctors and one PA are dedicated to providing quick and easy access to the opioids, while the others see a typical patient workload. As the clinic bills the insurance company, the billing ID of the true opioid provider is replaced by the ID of one of the other providers. This will show a slightly higher than normal opioid prescription load across the board, but nothing high enough to trigger any red flags.


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Figure 2 - Two providers spread their opioid claims across all providers at clinic


World-class analytics like those run through FICO Decision Management Suite (DMS) could prove unmatched in detecting pill mills. How does it work?


FICO Identity Resolution Engine (IRE) reads across the depth and breadth of claims data, provider data, and more to understand and cluster unique providers, including those with multiple IDs, name variations, and operating in multiple facilities. With advanced search, entity resolution, and link analytic capabilities, IRE enables the connection and analysis of entities and entity relationships across disparate internal and external data. The end result is a graphical analysis of the interconnections in data, which can aid investigations as well as analytically detect patterns of cross-provider fraud, waste, and abuse.


As a real world example, one leading insurance consortium partnered with FICO and found dozens of providers involved in multiple different provider fraud schemes. One such provider had 27 different instances of himself with various names, facilities, specialties, and provider IDs operating across 5 different states.


Detecting this huge fraud was achieved using statistical pattern recognition to understand the characteristics of legitimate behavior versus patterns and outliers that flag abusive practices, based on linkages within a claim. By filing through thousands upon thousands of claims, these analytics detect the claims, clinics, and personnel that depart from legitimate behavior, and determine the precise indicators of suspicious activity. Because the models constantly analyze new, incoming data, they adapt to shifts in behavior; this facilitates detection of new and evolving fraud types, whether these changes occur abruptly or evolve subtly over time. These models return scores and “reason codes” with which investigators can understand why a claim or provider received a high potential fraud score and launch targeted investigations into the suspicious behavior.


Models are then executed by investigators in order to build, review, and test new strategies to combat Pill Mills. Driven by analytic scores and highly targeted rules, investigators can rapidly develop, adapt, and execute rules that address newly discovered emerging schemes. The providers and clinics operating suspiciously are ranked for insurers to review and investigate. As a result, pill mills can be uncovered even as they start to develop, cutting up the supply ring before it can take hold.


This same solution could be used by Prescription Monitoring Programs run by the state. By detecting fraud at the state level, even Pill Mill providers running outside of insurance could be detected. In this case, IRE and DMS would process all the prescriptions given out by both pharmacies and providers to detect collusion.


Pill Mills represent a particularly ugly side of an already cruel public health problem. Combating these complex systems—and defending the people they harm—requires no less than the same advanced detection software that’s used to protect consumers from identity theft. In strong, data-driven solutions, we have an important ally to help overcome this problem, and keep Americans healthy and safe.


By, Neil Stickels & Anya Vanecek