Consensus-based Algorithms to Address Opioid Misuse Behaviors Among Individuals Prescribed LTOT

Study Overview
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Brief Summary
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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.

Detailed Description
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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 CFIR- and 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.

Official Title
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Consensus-based Algorithms to Address Opioid Misuse Behaviors Among Individuals Prescribed Long-term Opioid Therapy: Developing Implementation Strategies and Pilot Testing

Conditions
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Opioid Misuse, Chronic Pain

Intervention / Treatment
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* Behavioral: Pilot study of algorithms implementation package


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: UPMC community primary care practices Exclusion Criteria: non-UPMC practices

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Intervention Model: Single Group Assignment
Interventional Model Description: Investigators will conduct a pilot trial to investigate the feasibility, acceptability, and preliminary effectiveness of the algorithm Implementation Package. The investigators will conduct the pilot in 3 University of Pittsburgh Medical Center (UPMC) practices. the investigators will randomize by site and use a waitlist control. Feasibility and acceptability will be assessed qualitatively and via a clinician survey. Preliminary effectiveness outcomes will include reduction in concerning behaviors and increased diagnosis/treatment of Opioid Use Disorder (OUD). The investigators will also pilot Electronic Health Record-based outcomes assessment.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Implementation bundle<br>Participating clinics will experience integration of the Implementation Bundle into their practices. | Behavioral: Pilot study of algorithms implementation package<br>* The Implementation Bundle will include a link to algorithms in the Electronic Health Record, Smartphrases, audited feedback, and instructions.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Feasibility of algorithms | Number of algorithms used by Primary Care Physicians (PCPs) during the study period, measured by surveys administered to the PCPs at the end of the study period. | 1 year | 
| Acceptability of algorithms | Level of acceptability of the algorithms will be measured via qualitative interviews with clinicians and staff at each of the clinics. | 1 year | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Preliminary Effectiveness of algorithms | Change in opioid misuse behaviors as assessed using the Electronic Health Record and natural language processing methods. | 1 year | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Long-Term Opioid Therapy, Algorithms, Opioid Misuse, Chronic Pain