Consensus-based algorithms to address opioid misuse behaviors among individuals prescribed long-term opioid therapy: developing implementation strategies and pilot testing

PI: Jessica Merlin, MD, PhD, MBA

Co-Is: Ellie Green; Jane Margaret Liebshutz; Reed Van Deusen; Megan E. Hamm; Janel Hanmer; Jonathan Silverstein; Utibe Essien

Grant Number: 1R34DA050004-01A1

Project Summary: Despite a growing understanding of the risks of long-term opioid therapy (LTOT), it continues to be frequently prescribed and remains a mainstay of treatment for chronic pain. The CDC Guideline for Prescribing Opioids for Chronic Pain is geared toward primary care providers and has been adopted as the standard of care by many healthcare organizations and insurers. Importantly, it encourages monitoring of patients on LTOT for opioid-related harms. By implementing monitoring, primary care providers may uncover various concerning behaviors, sometimes called aberrant drug-related behaviors or opioid misuse behaviors, that arise among individuals prescribed LTOT for chronic pain. These behaviors (e.g., missed appointments, using more opioid medication than prescribed, asking for an increase in opioid dose, aggressive behavior, and alcohol and other substance use) are common, concerning, and may represent unsafe use of LTOT or a developing opioid use disorder (OUD). However, the CDC Guideline and other existing evidence do not provide specific, detailed guidance about how to address concerning behaviors when they occur. Therefore, there is a critical need to understand how to best respond to these behaviors.

The long-term goal of our program of research is to reduce LTOT-related harms, particularly from opioid misuse, and diminish their impact on the U.S. opioid epidemic. As a first step toward accomplishing this goal, we conducted a Delphi study to rigorously establish consensus-based approaches to managing common and challenging concerning behaviors, from which we created algorithms. Identifying and operationalizing implementation strategies using an evidence-based framework are the critical next steps that must occur before any testing of the algorithms. Therefore, we will pursue the following Specific Aims:
  • Aim 1: To a) identify and b) operationalize implementation strategies for the algorithms. Our approach will be guided by the Consolidated Framework for Implementation Research (CFIR) and the Expert Recommendations for Implementing Change (ERIC). Optimal implementation strategies will be uncovered through primary care provider experiences with Standardized Patients (SPs) followed by CFIR- and ERIC-guided group interviews. Using our prior expertise developing clinic-wide opioid risk reduction strategies and a Patient-Provider advisory board, we will develop a comprehensive “implementation package” that can be delivered to primary care practices.
  • Aim 2: To conduct a pilot trial of the algorithms. Guided by the CFIR-based implementation plan and using the implementation package developed in Aim 1b, we will conduct a pilot trial to investigate the algorithms’ feasibility, acceptability, and preliminary effectiveness. This approach is innovative because it involves novel algorithms and uses SPs in a new way, to identify and operationalize implementation strategies. The proposed research is significant because it will lead to an R01 to evaluate the algorithms and implementation strategies in an effectiveness-implementation type 2 hybrid trial that, if successful, would reduce opioid misuse-related harms and diminish their impact on the opioid epidemic.
Other Sites:
Arizona State University, Tempe, AZ