June 21 2018 | 0 Comments | 71 reads Average Rating: 3

4 Ways to Fight the Good Fight Against Opioid Abuse with Analytics

by Rena Bielinski in Health Analytics

In 2016, there were 32,445 deaths involving prescription opioids, which works out to roughly 89 deaths per day. That’s a 44% increase in deaths in just one year, up from 22,598 in 2015.1 Startling figures, to be sure.

Unfortunately, the enormity of the opioid crisis could potentially result in a certain amount of jaw-dropping inertia. What’s needed, however, is action. Here are four strategies that illustrate just how next generation analytics can be used to fight the epidemic:

1) Identify potential abusers. Claims data from health payers and pharmacy benefit managers (PBMs) can be leveraged to deliver a holistic view of each member’s/patient’s total opioid purchases. While many members/patients have multiple care providers, most only have one health insurance payer and one PBM. As such, an analysis of claims data can point to those members/patients who are taking an inordinate amount of opioids.

2) Assess the likelihood of abuse. Once the data has been analyzed, patterns have been identified through machine learning, and algorithms are established, healthcare organizations can develop risk scores and a color-coded dashboard that shows the likelihood that a particular member is committing fraud around opioid use. In addition, risk scoring can be controlled to minimize false positives, enabling investigators to focus on those members who are displaying the highest propensity for opioid abuse, collusion, or diversion.

3) Uncover suspect patterns. Geospatial data can be used to determine if a member/patient is filling several prescriptions at pharmacies that are far away from the member’s/patient’s home. Doing so is often a strong indicator that the member/patient is doctor-shopping and that fraud is occurring. Bringing in additional behavioral data about members/patients, such as age, gender, income, education levels, and other socioeconomic and psychographic factors based on Zip+4 and other sources can help further refine the risk scores.

4) Pinpoint pharmacies that are intentionally committing fraud, waste and abuse (FWA). An analysis of a broad range of metrics such as rate of “new billing,” reversal rate (very high and very low), percentage of member co-pays, average ingredient cost, average paid per prescription, average number of prescriptions per member (stratified by age), percentage of controlled substances, DAW-1 percentage, average dollars paid per member can be leveraged to determine if pharmacies are committing FWA. Color-coded dashboard s can be used to call out where certain pharmacies deviated from established norms.

These are just a few of the ways that next-generation analytics can be used to fight the opioid epidemic.

This blog was adapted from “Winning the Ongoing Battle Over Opioids,” an article that was recently published in Digital Commerce 360.

References

1. Seth P, Rudd R, Noonan, R, Haegerich, T. Quantifying the Epidemic of Prescription Opioid Overdose Deaths. American Journal of Public Health, March 2018;108(4),e1-e3

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Author
Rena Bielinski
SVP, Strategic Accounts

Dr. Bielinski has been with SCIO® for 11 years. She currently serves as a Strategic Account Manager for SCIO’s PBM clients as well as various health plan clients.

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