Adaptation of the Motivational Interviewing Skills Code to Identify Client Language Predicting Reduced Opioid Use Risk and Increased Use of Alternative Pain Care Strategies in Veterans
Authors:
- Brian BorsariBBBrian Borsari
- Catherine BaxleyCBCatherine Baxley
- Benjamin O. LaddBLBenjamin O. Ladd
- Joannalyn DelacruzJDJoannalyn Delacruz
- Kristina M. JacksonKJKristina M. Jackson
- Theodore FetterlingTFTheodore Fetterling
- Kyle J. SelfKSKyle J. Self
- Shahrzad Hassanbeigi DaryaniSDShahrzad Hassanbeigi Daryani
- Karen H. SealKSKaren H. Seal
- Jennifer K. ManuelJMJennifer K. Manuel
Open Access
- 8 December 2025
- journal article
- research article
- Published by MDPI AG in Methods and Protocols
- Vol. 8 (6) , 149
Abstract
Objective: Motivational Interviewing may be an ideal communication style to use in conjunction with Collaborative Care to address opioid risk, as it can facilitate the discussion of alternative pain care strategies (APCSs) that are pharmacological (APCS-P; e.g., the use of non-opioid pain relievers) or non-pharmacological (APCS-NP; e.g., yoga). This study developed and piloted a coding system (MI Skills Code–APCS) for these discussions. Method: Sessions (n = 119) from a completed randomized controlled trial comparing Collaborative Care Motivational Interviewing (CCMI) or Attention Control Psychoeducation (ACP) delivered by care managers over 12 weeks to veterans with chronic pain and high-risk opioid use enrolled in VA primary care (N = 44). Results: Coders were able to reliably code the client utterances related to APCSs in the sessions (ICCs = 0.58–0.81). The APCS-P and APCS-NP codes were positively correlated with each other. There were two significant relationships between the MISC-APCS codes (motivational states) and the pain interference and endorsement of non-pharmacological pain care goals at 20-week follow-up. Conclusions: The MISC-APCS has promise as a coding system that can reliably record client utterances regarding different types of pain care strategies. These utterances may be associated with post-treatment reports of pain and efforts to reduce opioid risk. The rapid development of artificial intelligence applications to healthcare can utilize this coding system to assist with the assessment and treatment of chronic pain.Keywords
Funding Information
- Health Services Research and Development (1I21HX002813-01)
- National Center for Complimentary and Integrative Health (1 R34 AT008319-01)
- National Clinical Trial (#NCT02230722)
- Department of Veterans Affairs Office of Academic Affiliations Advanced Fellowship Program in Mental Illness Research and Treatment
- Department of Veterans Affairs, Veterans Health Administration, Office of Academic Affiliations VA Quality Scholars Advanced Fellowship Program
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