Identification of patients on chronic prescription opioids at risk for opioid use disorder using pharmacy claims data

Using pharmacy claims data from a single commercial health plan to identify opportunities for opioid use disorder (OUD) focused screening and interventions communicated via targeted prescriber messaging. Participants included members ≥18 years using more than 90 morphine milligram equivalents (MMED) daily based on all opioid claims, had >1 paid claim for an opioid product, and had ≥90 days of total opioid therapy. Members were excluded with ≥1 claims for an oral chemotherapy agent (except methotrexate). Intervention was completed with a secure communication to the primary outpatient opioid prescriber that included resources for diagnosis, treatment, and best practices for opioid prescribing. The main outcome measure was any documented change to opioid use following the intervention. Seven hundred forty-five members were identified; a subset (n = 20) was further assessed, and all had identified OUD risk factors; providers were subsequently sent a communication. Sixteen providers acknowledged receipt and 11 patients (55%) had at least one documented intervention following communication receipt. Provision of targeted, evidence-based recommendations to providers for patients identified to be at risk of OUD from pharmacy claims data can result in increased recognition and intervention. Future efforts to explore feasibility of provider education detailing efforts and continued evaluation of efficacy are needed.

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Publication Details

Journal
Journal of Healthcare Risk Management
Published
2024-10-17
DOI
https://doi.org/10.1002/jhrm.21585
Citations
1
Primary Topic
Opioid Use Disorder Treatment
Type
article
Field-Weighted Citation Impact
0.54
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article

Identification of patients on chronic prescription opioids at risk for opioid use disorder using pharmacy claims data

Benjamin Lai, Julie L. Cunningham, Scott T. Hall, Laura Odell et al.
1 citations
Journal of Healthcare Risk Management
Opioid Use Disorder Treatment
0.54
article

Identification of patients on chronic prescription opioids at risk for opioid use disorder using pharmacy claims data

Benjamin Lai, Julie L. Cunningham, Scott T. Hall, Laura Odell, Aleesha Jantzen
article en
1 citations

Abstract

Using pharmacy claims data from a single commercial health plan to identify opportunities for opioid use disorder (OUD) focused screening and interventions communicated via targeted prescriber messaging. Participants included members ≥18 years using more than 90 morphine milligram equivalents (MMED) daily based on all opioid claims, had >1 paid claim for an opioid product, and had ≥90 days of total opioid therapy. Members were excluded with ≥1 claims for an oral chemotherapy agent (except methotrexate). Intervention was completed with a secure communication to the primary outpatient opioid prescriber that included resources for diagnosis, treatment, and best practices for opioid prescribing. The main outcome measure was any documented change to opioid use following the intervention. Seven hundred forty-five members were identified; a subset (n = 20) was further assessed, and all had identified OUD risk factors; providers were subsequently sent a communication. Sixteen providers acknowledged receipt and 11 patients (55%) had at least one documented intervention following communication receipt. Provision of targeted, evidence-based recommendations to providers for patients identified to be at risk of OUD from pharmacy claims data can result in increased recognition and intervention. Future efforts to explore feasibility of provider education detailing efforts and continued evaluation of efficacy are needed.

Journal of Healthcare Risk ManagementVol. 44(2)
Regions Hospital (US), Mayo Clinic (US), Mayo Clinic Health System (US), Mayo Clinic in Florida (US)
Openalex Percentile: Top 28%
Opioid Use Disorder Treatment
0.54
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Identification of patients on chronic prescription opioids at risk for opioid use disorder using pharmacy claims data — Benjamin Lai, Julie L. Cunningham, et al. · Journal of Healthcare Risk Management (2024) | TGRS Research Map | TGRS