How predictive analytics can tackle the opioid crisis

How predictive analytics can tackle the opioid crisis

How predictive analytics can tackle the opioid crisis

Every day in the U.S., there are about 650,000 opioid prescriptions dispensed, 3,900 people who begin abusing opioids, and 78 deaths from opioid-related overdoses. There are states where the number of yearly opioid prescriptions outnumbers the population. As the abuse of prescription painkillers increases so too has the use of heroin, filling our newspapers and news feeds with tragedies and announcements of loved ones lost. If you are not personally affected by the epidemic, it is practically a guarantee that you know someone who is. But despite the seemingly dire situation, there may be good news – from an unlikely source – on the horizon.

“Knowledge is power,” though cliché, accurately describes what it will take to cure the opioid epidemic. In a typical day, Americans attend millions of doctors’ appointments, resulting in hundreds of thousands of prescriptions. All of this information must be collected and routed through insurers (public and private alike). The result is a treasure trove of information – aptly referred to as “big data” – that allows us to pick up on trends and find patterns within enormous data sets. The huge amount of data could be used to detect areas where opioid abuse is likely occurring. Unfortunately, so far we’ve lacked the technology to effectively separate the signal from the noise. That is, until now.

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That is, until now.

In Michigan, we have built a Proof of Concept for an addiction-identification tool that analyzes Medicaid claims for signs of opioid abuse both at the prescriber and beneficiary level. The solution combines the Medicaid administrative data with pharmacy and clinical datasets. In statistics, an outlier is a data point that is distant from the rest of the data set. By sorting and analyzing health information datasets in Medicaid claims, we are able to identify outliers that may be indicative of addictive behavior. Some of the key data elements that are analyzed are individuals that visit numerous pharmacies and prescribers, early prescription refills, prior non-opioid substance abuse/dependence diagnoses, mental health-related diagnoses, and demographic information.



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