Improving the quality of counseling and clinical supervision in opioid treatment programs: how can technology help?

Addiction counseling Machine learning Motivational interviewing Opioid treatment

Journal

Addiction science & clinical practice
ISSN: 1940-0640
Titre abrégé: Addict Sci Clin Pract
Pays: England
ID NLM: 101316917

Informations de publication

Date de publication:
20 Jan 2024
Historique:
received: 19 12 2022
accepted: 05 01 2024
medline: 21 1 2024
pubmed: 21 1 2024
entrez: 20 1 2024
Statut: epublish

Résumé

The opioid epidemic has resulted in expanded substance use treatment services and strained the clinical workforce serving people with opioid use disorder. Focusing on evidence-based counseling practices like motivational interviewing may be of interest to counselors and their supervisors, but time-intensive adherence tasks like recording and feedback are aspirational in busy community-based opioid treatment programs. The need to improve and systematize clinical training and supervision might be addressed by the growing field of machine learning and natural language-based technology, which can promote counseling skill via self- and supervisor-monitoring of counseling session recordings. Counselors in an opioid treatment program were provided with an opportunity to use an artificial intelligence based, HIPAA compliant recording and supervision platform (Lyssn.io) to record counseling sessions. We then conducted four focus groups-two with counselors and two with supervisors-to understand the integration of technology with practice and supervision. Questions centered on the acceptability of the clinical supervision software and its potential in an OTP setting; we conducted a thematic coding of the responses. The clinical supervision software was experienced by counselors and clinical supervisors as beneficial to counselor training, professional development, and clinical supervision. Focus group participants reported that the clinical supervision software could help counselors learn and improve motivational interviewing skills. Counselors said that using the technology highlights the value of counseling encounters (versus paperwork). Clinical supervisors noted that the clinical supervision software could help meet national clinical supervision guidelines and local requirements. Counselors and clinical supervisors alike talked about some of the potential challenges of requiring session recording. Implementing evidence-based counseling practices can help the population served in OTPs; another benefit of focusing on clinical skills is to emphasize and hold up counselors' roles as worthy. Machine learning technology can have a positive impact on clinical practices among counselors and clinical supervisors in opioid treatment programs, settings whose clinical workforce continues to be challenged by the opioid epidemic. Using technology to focus on clinical skill building may enhance counselors' and clinical supervisors' overall experiences in their places of work.

Sections du résumé

BACKGROUND BACKGROUND
The opioid epidemic has resulted in expanded substance use treatment services and strained the clinical workforce serving people with opioid use disorder. Focusing on evidence-based counseling practices like motivational interviewing may be of interest to counselors and their supervisors, but time-intensive adherence tasks like recording and feedback are aspirational in busy community-based opioid treatment programs. The need to improve and systematize clinical training and supervision might be addressed by the growing field of machine learning and natural language-based technology, which can promote counseling skill via self- and supervisor-monitoring of counseling session recordings.
METHODS METHODS
Counselors in an opioid treatment program were provided with an opportunity to use an artificial intelligence based, HIPAA compliant recording and supervision platform (Lyssn.io) to record counseling sessions. We then conducted four focus groups-two with counselors and two with supervisors-to understand the integration of technology with practice and supervision. Questions centered on the acceptability of the clinical supervision software and its potential in an OTP setting; we conducted a thematic coding of the responses.
RESULTS RESULTS
The clinical supervision software was experienced by counselors and clinical supervisors as beneficial to counselor training, professional development, and clinical supervision. Focus group participants reported that the clinical supervision software could help counselors learn and improve motivational interviewing skills. Counselors said that using the technology highlights the value of counseling encounters (versus paperwork). Clinical supervisors noted that the clinical supervision software could help meet national clinical supervision guidelines and local requirements. Counselors and clinical supervisors alike talked about some of the potential challenges of requiring session recording.
CONCLUSIONS CONCLUSIONS
Implementing evidence-based counseling practices can help the population served in OTPs; another benefit of focusing on clinical skills is to emphasize and hold up counselors' roles as worthy. Machine learning technology can have a positive impact on clinical practices among counselors and clinical supervisors in opioid treatment programs, settings whose clinical workforce continues to be challenged by the opioid epidemic. Using technology to focus on clinical skill building may enhance counselors' and clinical supervisors' overall experiences in their places of work.

Identifiants

pubmed: 38245783
doi: 10.1186/s13722-024-00435-z
pii: 10.1186/s13722-024-00435-z
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

8

Subventions

Organisme : NIDA NIH HHS
ID : R44DA046243-S1
Pays : United States

Informations de copyright

© 2024. The Author(s).

Références

McBain RK, Dick A, Sorbero M, Stein BD. Growth and distribution of buprenorphine-waivered providers in the United States, 2007–2017. Ann Intern Med. 2020;172(7):504–6.
pubmed: 31905379 pmcid: 7217729 doi: 10.7326/M19-2403
Mojtabai R, Mauro C, Wall MM, Barry CL, Olfson M. Medication treatment for opioid use disorders in substance use treatment facilities. Health Aff. 2019;38(1):14–23.
doi: 10.1377/hlthaff.2018.05162
Doumas DM, Miller RM, Esp S. Continuing education in motivational interviewing for addiction counselors: Reducing the research-to-practice gap. J Addict Offender Couns. 2019;40(1):36–51.
doi: 10.1002/jaoc.12055
Madson MB, Villarosa-Hurlocker MC, Schumacher JA, Williams DC, Gauthier JM. Motivational interviewing training of substance use treatment professionals: a systematic review. Subst Abuse. 2018. https://doi.org/10.1080/08897077.2018.1475319 .
doi: 10.1080/08897077.2018.1475319
Martino S, Paris M Jr, Añez L, Nich C, Canning-Ball M, Hunkele K, Olmstead TA, Carroll KM. The effectiveness and cost of clinical supervision for motivational interviewing: a randomized controlled trial. J Subst Abuse Treat. 2016;1(68):11–23.
doi: 10.1016/j.jsat.2016.04.005
Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, Griffey R, Hensley M. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Mental Health Mental Health Serv Res. 2011;38(2):65–76.
doi: 10.1007/s10488-010-0319-7
Bearman SK, Schneiderman RL, Zoloth E. Building an evidence base for effective supervision practices: An analogue experiment of supervision to increase EBT fidelity. Adm Policy Mental Health Mental Health Serv Resh. 2017;44(2):293–307.
doi: 10.1007/s10488-016-0723-8
Center for Substance Abuse Treatment. Competencies for substance abuse treatment clinical supervision (Technical Assistance Publication (TAP) Series 21–1, DHHS Publication No. (SMA) 07–4243). Rockville: Substance Abuse and Mental Health Services Administration; 2007.
Beitel M, Oberleitner L, Muthulingam D, Oberleitner D, Madden LM, Marcus R, Eller A, Bono MH, Barry DT. Experiences of burnout among drug counselors in a large opioid treatment program: a qualitative investigation. Subst Abuse. 2018;39(2):211–7.
doi: 10.1080/08897077.2018.1449051
Knudsen HK, Ducharme LJ, Roman PM. Clinical supervision, emotional exhaustion, and turnover intention: a study of substance abuse treatment counselors in the clinical trials network of the National Institute on Drug Abuse. J Subst Abuse Treat. 2008;35(4):387–95.
pubmed: 18424048 pmcid: 2637454 doi: 10.1016/j.jsat.2008.02.003
Oser CB, Biebel EP, Pullen E, Harp KL. Causes, consequences, and prevention of burnout among substance abuse treatment counselors: a rural versus urban comparison. J Psychoact Drugs. 2013;45(1):17–27.
doi: 10.1080/02791072.2013.763558
Uscher-Pines L, Sousa J, Raja P, Mehrotra A, Barnett M, Huskamp HA. Treatment of opioid use disorder during COVID-19: experiences of clinicians transitioning to telemedicine. J Subst Abuse Treat. 2020;1(118):108124.
doi: 10.1016/j.jsat.2020.108124
Mattson CL, Tanz LJ, Quinn K, Kariisa M, Patel P, Davis NL. Trends and geographic patterns in drug and synthetic opioid overdose deaths—United States, 2013–2019. Morb Mortal Wkly Rep. 2021;70(6):202.
doi: 10.15585/mmwr.mm7006a4
Serre F, Fatseas M, Swendsen J, Auriacombe M. Ecological momentary assessment in the investigation of craving and substance use in daily life: a systematic review. Drug Alcohol Depend. 2015;1(148):1–20.
doi: 10.1016/j.drugalcdep.2014.12.024
Marsch LA, Dallery J. Advances in the psychosocial treatment of addiction: the role of technology in the delivery of evidence-based psychosocial treatment. Psychiatric Clinics. 2012;35(2):481–93.
pubmed: 22640767
McPherson SM, Burduli E, Smith CL, Herron J, Oluwoye O, Hirchak K, Orr MF, McDonell MG, Roll JM. A review of contingency management for the treatment of substance-use disorders: adaptation for underserved populations, use of experimental technologies, and personalized optimization strategies. Subst Abuse Rehabilit. 2018;9:43.
doi: 10.2147/SAR.S138439
Moore BA, Fazzino T, Garnet B, Cutter CJ, Barry DT. Computer-based interventions for drug use disorders: a systematic review. J Subst Abuse Treat. 2011;40(3):215–23.
pubmed: 21185683 doi: 10.1016/j.jsat.2010.11.002
Godersky ME, Klein JW, Merrill JO, Blalock KL, Saxon AJ, Samet JH, Tsui JI. Acceptability and feasibility of a mobile health application for video directly observed therapy of buprenorphine for opioid use disorders in an office-based setting. J Addict Med. 2020;14(4):319.
pubmed: 31972762 pmcid: 7358111 doi: 10.1097/ADM.0000000000000608
Weingardt KR, Villafranca SW, Levin C. Technology-based training in cognitive behavioral therapy for substance abuse counselors. Subst Abuse. 2006;27(3):19–25.
doi: 10.1300/J465v27n03_04
Weingardt KR, Cucciare MA, Bellotti C, Lai WP. A randomized trial comparing two models of web-based training in cognitive–behavioral therapy for substance abuse counselors. J Subst Abuse Treat. 2009;37(3):219–27.
pubmed: 19339136 pmcid: 2771721 doi: 10.1016/j.jsat.2009.01.002
Aafjes-van Doorn K, Kamsteeg C, Bate J, Aafjes M. A scoping review of machine learning in psychotherapy research. Psychother Res. 2021;31(1):92–116.
pubmed: 32862761 doi: 10.1080/10503307.2020.1808729
Cao J, Tanana M, Imel ZE, Poitras E, Atkins DC, Srikumar V. Observing dialogue in therapy: categorizing and forecasting behavioral codes. arXiv preprint arXiv:1907.00326 . 2019.
Imel ZE, Pace BT, Soma CS, Tanana M, Hirsch T, Gibson J, Georgiou P, Narayanan S, Atkins DC. Design feasibility of an automated, machine-learning based feedback system for motivational interviewing. Psychotherapy. 2019;56(2):318.
pubmed: 30958018 doi: 10.1037/pst0000221
Hirsch T, Soma C, Merced K, Kuo P, Dembe A, Caperton DD, Atkins DC, Imel ZE. “ It’s hard to argue with a computer" Investigating Psychotherapists' Attitudes towards Automated Evaluation. In Proceedings of the 2018 Designing Interactive Systems Conference. 2018. pp. 559–571.
Miller WR, Rollnick S. Motivational interviewing: Helping people change. New York: Guilford press; 2012.
Abuse S, Administration MH. Federal guidelines for opioid treatment programs. HHS publication no (SMA) PEP15-FEDGUIDEOTP. 2015.
Miller WR, Moyers TB, Ernst D, Amrhein P. Manual for the motivational interviewing skill code (MISC). Unpublished manuscript. Albuquerque: Center on Alcoholism, Substance Abuse and Addictions, University of New Mexico. 2003.
Hatch-Maillette MA, Harwick R, Baer JS, Masters T, Cloud K, Peavy M, Wiest K, Wright L, Beadnell B, Wells EA. Counselor turnover in substance use disorder treatment research: observations from one multisite trial. Subst Abuse. 2019;40(2):214–20.
doi: 10.1080/08897077.2019.1572051
Wood K, Giannopoulos V, Louie E, Baillie A, Uribe G, Lee KS, Haber PS, Morley KC. The role of clinical champions in facilitating the use of evidence-based practice in drug and alcohol and mental health settings: a systematic review. Implement Res Pract. 2020;1:2633489520959072.
pubmed: 37089122 pmcid: 9924254
Schmidt EA. Clinical supervision in the substance abuse profession: a review of the literature. Alcohol Treat Q. 2012;30(4):487–504.
doi: 10.1080/07347324.2012.718966
Watkins CE Jr. What do clinical supervision research reviews tell us? Surveying the last 25 years. Couns Psychother Res. 2020;20(2):190–208.
doi: 10.1002/capr.12287
Culbreth JR. Clinical supervision of substance abuse counselors: current and preferred practices. J Addict Offender Couns. 1999;20(1):15–25.
doi: 10.1002/j.2161-1874.1999.tb00137.x
Laschober TC, de Tormes Eby LT, Sauer JB. Clinical supervisor and counselor perceptions of clinical supervision in addiction treatment. J Addict Dis. 2012;31(4):382–8.
pubmed: 23244557 pmcid: 3530843 doi: 10.1080/10550887.2012.735599
O’Grady MA, Lincourt P, Gilmer E, Kwan M, Burke C, Lisio C, Neighbors CJ. How are substance use disorder treatment programs adjusting to value-based payment? A statewide qualitative study. Subst Abuse Res Treat. 2020;14:1178221820924026.
Schwalbe CS, Oh HY, Zweben A. Sustaining motivational interviewing: a meta-analysis of training studies. Addiction. 2014;109(8):1287–94.
pubmed: 24661345 doi: 10.1111/add.12558
Maslach C, Leiter MP. Early predictors of job burnout and engagement. J Appl Psychol. 2008;93(3):498.
pubmed: 18457483 doi: 10.1037/0021-9010.93.3.498
Murphy J. Improving the recruitment and retention of counselors in rural substance use disorder treatment programs. J Drug Issues. 2022;52(3):434–56.
doi: 10.1177/00220426221080204
Martino S, Gallon S, Ball SA, Carroll KM. A step forward in teaching addiction counselors how to supervise motivational interviewing using a clinical trials training approach. J Teach Addict. 2008;6(2):39–67.
doi: 10.1080/15332700802127946
Volker R, Bernhard B, Anna K, Fabrizio S, Robin R, Jessica P, Rudolf S, Lucia D, Jürgon R, Franz H, Christine S. Burnout, coping and job satisfaction in service staff treating opioid addicts—from Athens to Zurich Stress and Health. J Int Soc Investig Stress. 2010;26(2):149–59.
Peavy KM, Darnton J, Grekin P, Russo M, Green CJ, Merrill JO, Fotinos C, Woolworth S, Soth S, Tsui JI. Rapid implementation of service delivery changes to mitigate COVID-19 and maintain access to methadone among persons with and at high-risk for HIV in an opioid treatment program. AIDS Behav. 2020;24(9):2469–72.
pubmed: 32347404 pmcid: 7186943 doi: 10.1007/s10461-020-02887-1
Imel ZE, Steyvers M, Atkins DC. Computational psychotherapy research: Scaling up the evaluation of patient–provider interactions. Psychotherapy. 2015;52(1):19.
pubmed: 24866972 doi: 10.1037/a0036841
Xiao B, Imel ZE, Georgiou PG, Atkins DC, Narayanan SS. “ Rate my therapist”: automated detection of empathy in drug and alcohol counseling via speech and language processing. PLoS ONE. 2015;10(12):e0143055.
pubmed: 26630392 pmcid: 4668058 doi: 10.1371/journal.pone.0143055
Tanana M, Hallgren K, Imel Z, Atkins D, Smyth P, Srikumar V. Recursive neural networks for coding therapist and patient behavior in motivational interviewing. In: Proceedings of the 2nd workshop on computational linguistics and clinical psychology: from linguistic signal to clinical reality. 2015. pp. 71–79.
Can D, Marín RA, Georgiou PG, Imel ZE, Atkins DC, Narayanan SS. “It sounds like…”: A natural language processing approach to detecting counselor reflections in motivational interviewing. J Couns Psychol. 2016;63(3):343.
pubmed: 26784286 pmcid: 4833560 doi: 10.1037/cou0000111
Gaut G, Steyvers M, Imel ZE, Atkins DC, Smyth P. Content coding of psychotherapy transcripts using labeled topic models. IEEE J Biomed Health Inform. 2015;21(2):476–87.
pubmed: 26625437 pmcid: 4879602 doi: 10.1109/JBHI.2015.2503985
Mattick RP, Breen C, Kimber J, Davoli M. Buprenorphine maintenance versus placebo or methadone maintenance for opioid dependence. Cochrane Database Syst rev. 2014. https://doi.org/10.1002/14651858.CD002207.pub4 .
doi: 10.1002/14651858.CD002207.pub4 pubmed: 24500948 pmcid: 10617756
National Academies of Sciences, Engineering, and Medicine. Medications for opioid use disorder save lives. Washington: National Academies Press; 2019.
Oesterle TS, Thusius NJ, Rummans TA, Gold MS. Medication-assisted treatment for opioid-use disorder. In Mayo Clinic Proceedings. Elsevier, 2019 Vol. 94, No. 10, pp. 2072–2086.
Sayegh CS, Huey SJ Jr, Zara EJ, Jhaveri K. Follow-up treatment effects of contingency management and motivational interviewing on substance use: a meta-analysis. Psychol Addict Behav. 2017;31(4):403.
pubmed: 28437121 doi: 10.1037/adb0000277
Mumba MN, Findlay LJ, Snow DE. Treatment options for opioid use disorders: a review of the relevant literature. J Addict Nurs. 2018;29(3):221–5.
pubmed: 30180011 doi: 10.1097/JAN.0000000000000241

Auteurs

K Michelle Peavy (KM)

PRISM, Department of Community and Behavioral Health, Elson S. Floyd College of Medicine, Washington State University, Spokane, WA, USA.

Angela Klipsch (A)

Lyssn.Io, Seattle, Washington, USA.

Christina S Soma (CS)

Lyssn.Io, Seattle, Washington, USA. tina@lyssn.io.

Brian Pace (B)

Lyssn.Io, Seattle, Washington, USA.

Zac E Imel (ZE)

Lyssn.Io, Seattle, Washington, USA.
University of Utah, Salt Lake City, UT, USA.

Michael J Tanana (MJ)

Lyssn.Io, Seattle, Washington, USA.

Sean Soth (S)

Evergreen Treatment Services, Seattle, Washington, USA.

Esther Ricardo-Bulis (E)

Evergreen Treatment Services, Seattle, Washington, USA.

David C Atkins (DC)

Lyssn.Io, Seattle, Washington, USA.

Classifications MeSH