# Technology-supported, measurement-based supervision for Motivational Interviewing

> **NIH NIH R44** · LYSSN.IO, INC. · 2020 · $134,649

## Abstract

Millions of Americans receive evidence-based counseling for substance use problems each year. Many 
evidence-based treatments for substance abuse are “talk based” therapies, such as motivational 
interviewing (MI), but the existing research-based methodology for evaluating counseling quality is 
to record sessions and use human rating teams to evaluate them. However, using humans as the 
assessment tool via behavioral coding is prohibitive in cost and time, can be error prone, and is 
virtually never used in the real world.
Technology is needed that can analyze the speech patterns and spoken language of counseling 
sessions, provide automatic and intuitive quality scores, and summarize these in actionable 
feedback. Rapid, performance-based quality metrics could support training, ongoing supervision, and 
quality assurance for millions of evidence-based counseling sessions for substance abuse each year.
Lyssn.io is a start-up targeting the development of implementation-focused technology to support 
evidence-based counseling.  Our goal is to develop innovative health technology solutions that are 
objective, scalable, and cost efficient. Lyssn.io includes expertise in speech signal processing, 
machine learning,
user-centered design, software engineering, and clinical expertise in evidence-based counseling. 
Previous NIH-funded research laid a computational foundation for generating MI quality metrics from 
speech and language features in MI sessions, and led to a prototype of a clinical software support 
tool, the Counselor Observer Ratings Expert for MI (CORE-MI).
The current Fast-Track SBIR proposal includes Phase I, which will focus on understanding clinical 
workflows, assessing usability, and initial validation of machine learning of MI fidelity measures 
in the opioid treatment program at Evergreen Treatment Services (ETS) clinic in Seattle, WA. Phase 
II will focus on robust validation of the speech and language technologies underlying the CORE-MI 
tool, and development of scalable supervision protocols that integrate CORE-MI supported feedback 
for counselors. Finally, we will conduct a quasi-experimental evaluation of CORE-MI supported 
supervision and training at a second ETS clinic in the Puget Sound, focusing on acceptability, 
usability, and adoption, the impact on supervision, improved MI fidelity and preliminary evidence 
of increased client retention.  The successful execution of this project will break the reliance on 
human judgment for providing performance-based feedback to MI and will massively expand the 
capacity to train, supervise, and provide quality assurance in MI for substance abuse.

## Key facts

- **NIH application ID:** 9930480
- **Project number:** 3R44DA046243-03S1
- **Recipient organization:** LYSSN.IO, INC.
- **Principal Investigator:** Michael J Tanana
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $134,649
- **Award type:** 3
- **Project period:** 2018-07-15 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9930480

## Citation

> US National Institutes of Health, RePORTER application 9930480, Technology-supported, measurement-based supervision for Motivational Interviewing (3R44DA046243-03S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9930480. Licensed CC0.

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