Fighting Opioid Abuse with Analytics

Blog Post created by anya.vane on Apr 6, 2017

Drug addiction is a powerful and destructive debilitation. Death rates associated with the growing Opioid Epidemic rose an astonishing 72.2% from 2014 to 2015 across the country, according to data from the Centers for Disease Control and Prevention. A total of 33,091 Americans died from opioid overdose in 2015; 91 people every day. These addictions cost families and the American economy dearly.


There is a great need for innovation in our medical system to address this growing failure of care. Fortunately, advanced analytic technology is rising to the occasion.


Drug-seeking behavior can be hard to identify. Though drug addicts can be predictable, their patterns of behavior and medical backstories are sophisticated. Wherever and however they can access their drug of choice, they will. And increasingly, that is not some deserted alley in the dead of night – it’s the emergency rooms of hospitals.


Emergency Rooms are an ideal target because they are required to treat and at least stabilize all patients admitted. That said, walking into a hospital to get a drug fix is a bold move, and executing it takes guile. Multiple variations of various personas, combinations of first and last names, addresses, symptoms, and medical histories enable addicts to cycle through multiple hospitals over time. The highly private nature of health information means that networks rarely compare their records. So when denied Fentanyl at one hospital, an addict could simply drive across town to a hospital in another network. Under a variation of their fluid and internalized pseudonym, she’s just a patient needing immediate care. It’s remarkable what one can achieve when desperate.


Of course, not everything can be faked, and this is where technology can help. The first tip-off to a drug-seeker persona is that a patient received a controlled drug. See if you can identify the others…


A woman, born March 16th, 1958, with grey hair and a medium build, walks into Hospital A complaining of lower back pain. She’s in a lot of pain: on a scale of 1-10, she’s at an 8, reporting that she’s been missing work, unable to sit at her desk for any amount of time. In fact, she took the bus into Cincinnati all the way from her home an hour away, just for the opportunity to stand. In her case, morphine is out of the question: she’s highly allergic. As a new patient with no insurance card in hand, her medical records cannot be accessed. The doctor offers her a shot of Fentanyl to get her through the day, and encourages her to seek treatment from a primary care physician.


One week later, on the other side on Cincinnati, a grey-haired woman enters Hospital B complaining of back pain. Her new patient form states that she was born August 8th, 1960, works as a store clerk, and that she’s allergic to morphine. Her back pain is excruciating: she’s been out of work all week, simply unable to stand for more than a few minutes. The ER here is crowded, and the diagnosis is clear enough. The clinician provides her a low dose of Fentanyl, and sends her home to rest her apparently herniated disks.


Did you identify the patterns? These women are one and the same. For one provider, a single drug seeker, much like our example, cost the provider over $500k in unbilled medical expenses. Advanced analytic technology helped prove it— and enabled those hospitals to intervene.


A custom-built solution leveraged cluster analysis to identify and map seemingly unique individual characteristics, and definitively tied them to the same person.

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Cluster analysis initially matched iterations of a single identity by sorting through the hospital’s data records and pairing seemingly redundant data. Additional information like emergency contact information, clinical data like height and weight, and ultimately the care that was rendered, sharpened the edges of fuzzy clusters into tangible shared personas. A few key tip-offs merged what had seemed like one-time visits into patterns of drug seeking behavior: the morphine allergy, a clever trick to get a stronger opioid; the age range; the lack of an insurance card; the general geographic location. Analytic tools then scored these drug-seeking identities, and provided the hospital a ranked list of potential drug-seeking patient personas to review.


All in all, 39 ER visits (and 39 treatments) were linked to the same person. The medical network issued a warning to their regional facilities, showing an image of this woman and listing her aliases. One week later, she stopped by another network hospital. This time, she was not “treated.”


While this data goes a long way towards identifying and stopping drug-seeking behaviors within hospital networks, more can and must be done to address this growing public health crisis. Fortunately, there is much more technology can do.


Each state owns and runs Prediction Drug Monitoring Programs, which operate as databases of every schedule two or three narcotic prescribed and filled. These are checked prior to every prescription of an opioid, to ensure prescriptions don’t go to those with a history of abuse. Of course, this only succeeds if the patient is using their real name.


Fortunately, drug-seeking behavior is patterned. Even without clues as obvious as names, link analysis programs could identify problematic providers and patients using data from a state’s Prediction Drug Monitoring Program files. Through analysis, the programs could help uncover ‘pill mills,’ or groups of people working together to accumulate and then sell prescription drugs from numerous providers. It could catch areas in which pills are given too freely or prescribed at unreasonably high amounts.


This problem is more common and less sinister than you might expect. One would not want to falsely cut someone off from a drug they need. That sets the default to letting the bad stuff go, assuming that these represent the significant minority. Yet, as the opioid crisis explodes, such gut decisions are becoming more dangerous.


One thing is clear—there is a great need for innovation in our medical system. With the network-level view that technology can provide, greater accountability and success. Analytics take on the responsibility of identifying potentially suspicious behavior, so that doctors and nurses can focus on providing life-saving care. In an industry that swears to “first, do no harm,” technology is the ultimate ally. By cutting down on missed connections and avoidable mistakes, link analytics and scoring technology help us fulfill that promise.