Opioid prescribing patterns in a commercially insured population.

Area of Science:

• Health Services Research
• Pharmacology
• Public Health

Background:

• Claims data analysis enhances understanding of patient risk factors for opioid misuse.
• Limited evidence exists for identifying prescribers with at-risk opioid prescribing practices.
• An algorithm was developed to detect outlying opioid prescribing patterns in a managed care setting.

Purpose of the Study:

• To develop and validate an algorithm for identifying prescribers with at-risk opioid prescribing behaviors.
• To enable targeted interventions for mitigating opioid misuse and abuse.
• To provide a flexible tool for managed care organizations to tailor identification criteria.

Main Methods:

• Retrospective analysis of enrollment, prescription, and medical claims data (September-December 2019).
• Identification of prescribers for Schedule II, III, or IV opioid analgesics.
• Characterization of prescribers as frequent, continuous, and/or high-dose prescribers based on defined criteria.

Main Results:

• The algorithm identified 5136 prescribers of at least one opioid, accounting for 53.9% of total opioids prescribed.
• 12.2% were frequent prescribers (opioids to ≥50% of patients), 9.6% were continuous prescribers (>185 day-supply to ≥10% of patients), and 2.9% were high-dose prescribers (average daily MME >90 mg to ≥10% of patients).
• 0.9% of prescribers met all three criteria (frequent, continuous, and high-dose).

Conclusions:

• The developed algorithm successfully identifies a targeted group of prescribers for intervention.
• Flexible criteria allow adaptation to the specific needs of diverse managed care organizations.
• This approach aids in mitigating opioid misuse and abuse by focusing resources on at-risk prescribing practices.