Kimberly Lenz, PharmD, clinical pharmacy manager, MassHealth/Office of Clinical Affairs, University of Massachusetts Medical School, discusses identifying patients most at risk for misusing or abusing controlled substances and how approaches to treatment differ for these patients.
Kimberly Lenz, PharmD, clinical pharmacy manager, MassHealth/Office of Clinical Affairs, University of Massachusetts Medical School, discusses identifying patients most at risk for misusing or abusing controlled substances and how approaches to treatment differ for these patients.
Transcript
How can you identify patients at greatest risk for misuse or abuse of controlled substances?
It’s a really tricky question. I think a lot of health plans use risk stratification tools to help them identify members at the greatest risk. At our plan, we currently use the methodology of 11 or more controlled substances written by 4 or more providers or filled by 4 or more pharmacies. We are looking to change that into a more risk-based criteria, where it’s not an arbitrary number of prescriptions or providers, more a combination of things that put a member at greater risk of either an overdose or some other concerning event. So, we’re looking at combinations of opioids and benzodiazepines, gabapentins, stimulants, skeletal muscle relaxants, and then combining that potentially with not just the number of opioids they’re receiving, but their milligrams of morphine equivalence, which has been shown in the literature to correlate to overdose events.
Is the approach to treatment different for these patients?
I think it has to be. So, you can’t just assume the criteria will be a one-size-fits-all for everybody. So, I think you have to take into consideration if you have a beneficiary who recently started on addiction treatment or had a significant change in their medical history—recent diagnoses of cancer, things like that. I think you have to give those members a little bit more time before you put them into a program to see what their long-term treatment will look like.
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