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

> **NIH NIH R34** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2020 · $327,224

## Abstract

Project Summary: Despite a growing understanding of the risks of long-term opioid therapy (LTOT), it contin-
ues 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 pa-
tients on LTOT for opioid-related harms. By implementing monitoring, primary care providers may uncover var-
ious 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 alco-
hol and other substance use) are common, concerning, and may represent unsafe use of LTOT or a develop-
ing opioid use disorder (OUD). However, the CDC Guideline and other existing evidence do not provide specif-
ic, 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 con-
duct a pilot trial to investigate the algorithms’ feasibility, acceptability, and preliminary effectiveness. This ap-
proach is innovative because it involves novel algorithms and uses SPs in a new way, to identify and opera-
tionalize implementation strategies. The proposed research is significant because it will...

## Key facts

- **NIH application ID:** 10055996
- **Project number:** 1R34DA050004-01A1
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Jessica S Merlin
- **Activity code:** R34 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $327,224
- **Award type:** 1
- **Project period:** 2020-07-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10055996

## Citation

> US National Institutes of Health, RePORTER application 10055996, Consensus-based algorithms to address opioid misuse behaviors among individuals prescribed long-term opioid therapy: developing implementation strategies and pilot testing (1R34DA050004-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10055996. Licensed CC0.

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