Abstract

Direct observation of psychotherapy and providing performance-based feedback is the gold-standard approach for training psychotherapists. At present, this requires experts and training human coding teams, which is slow, expensive, and labor intensive. Machine learning and speech signal processing technologies provide a way to scale up feedback in psychotherapy. We evaluated an initial proof of concept automated feedback system that generates motivational interviewing quality metrics and provides easy access to other session data (eg, transcripts). The system automatically provides a report of session-level metrics (eg, therapist empathy) and therapist behavior codes at the talk-turn level (eg, reflections). We assessed usability, therapist satisfaction, perceived accuracy, and intentions to adopt. A sample of 21 novice (n= 10) or experienced (n= 11) therapists each completed a 10-min session with a …

Date

2019

Authors

Zac E Imel, Brian T Pace, Christina S Soma, Michael Tanana, Tad Hirsch, James Gibson, Panayiotis Georgiou, Shrikanth Narayanan, David C Atkins

Journal

Psychotherapy

Volume

56

Issue

2

Pages

318

Publisher

Educational Publishing Foundation