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

> **NIH NIH R44** · LYSSN.IO, INC. · 2020 · $394,059

## 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:** 9847959
- **Project number:** 5R44DA046243-03
- **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:** $394,059
- **Award type:** 5
- **Project period:** 2018-07-15 → 2022-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9847959

## Citation

> US National Institutes of Health, RePORTER application 9847959, Technology-supported, measurement-based supervision for Motivational Interviewing (5R44DA046243-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9847959. Licensed CC0.

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