The NIH Helping to End Addiction Long-term (HEAL) initiative has identified a critical next step to addressing the opioid crisis: improving treatments for opioid misuse behaviors (e.g., using more opioids than prescribed, illicit substance use) in patients prescribed long-term opioid therapy for chronic pain. In previous work, the investigators have developed innovative consensus-based algorithms to manage these behaviors. By developing implementation strategies for these algorithms, this project is directly responsive to the HEAL initiative and promises to reduce opioid misuse-related harms.

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 Centers for Disease and Control (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, the investigators conducted a Delphi study to rigorously establish consensus-based approaches to managing common and challenging concerning behaviors, from which algorithms were created. Identifying and operationalizing implementation strategies using an evidence-based framework are the critical next steps that must occur before any testing of the algorithms.

The investigators successfully uncovered optimal implementation strategies through primary care provider experiences with Standardized Patients (SPs) followed by Consolidated Framework for Implementation Research (CFIR)- and Expert Recommendations for Implementing Change (ERIC)-guided individual interviews. Using our prior expertise developing clinic-wide opioid risk reduction strategies and a Patient-Provider advisory board, the investigators developed a comprehensive "implementation package" that can be delivered to primary care practices.

The investigators now aim to conduct a pilot trial to test the algorithm implementation package. Guided by the CFIR-based implementation plan and using the implementation package that the investigators developed, pilot trial will be conducted to investigate feasibility, acceptability, and preliminary effectiveness of the algorithm implementation package.

Inclusion Criteria:

• Clinicians practicing at UPMC community primary care clinics.

Exclusion Criteria:

• Clinicians not practicing at UPMC community primary care clinics.

18 months for the academic clinic, 21 months for the two community clinics

Although we are using electronic health record (EHR) data from the pre-implementation period for comparison purposes, these data were extracted retrospectively after the implementation phase. Because no active intervention was in place during the pre-implementation phase, adverse events cannot be collected for that period.

The study's findings are limited by its pilot nature, focusing on a small number of clinics and clinicians. Results may not be generalizable to other settings or populations. Additionally, qualitative data were collected from a subset of participants, which may not fully represent all perspectives. The limited follow-up time may also restrict the interpretation of longer-term outcomes or broader feasibility.