Deep Annotation of Therapeutic Working Alliance in Psychotherapy

TL;DR

This paper introduces a deep learning framework to infer the therapeutic working alliance directly from psychotherapy session transcripts, enabling real-time assessment and insights into patient-therapist interactions.

Contribution

It presents a novel method using deep embeddings to analyze psychotherapy dialogues at turn-level resolution, improving real-time assessment of the working alliance.

Findings

Effective mapping of patient-therapist alignment trajectories

Real-time analysis capability demonstrated on over 950 sessions

Provides interpretable insights for clinical psychiatry

Abstract

The therapeutic working alliance is an important predictor of the outcome of the psychotherapy treatment. In practice, the working alliance is estimated from a set of scoring questionnaires in an inventory that both the patient and the therapists fill out. In this work, we propose an analytical framework of directly inferring the therapeutic working alliance from the natural language within the psychotherapy sessions in a turn-level resolution with deep embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each psychotherapy session can be transcribed and generated in real-time from the session speech recordings, and these embedded dialogues are compared with the distributed representations of the statements in the working alliance inventory. We demonstrate, in a real-world dataset with over 950 sessions of psychotherapy treatments in anxiety, depression, schizophrenia and suicidal patients, the effectiveness of this method in mapping out trajectories of patient-therapist alignment and the interpretability that can offer insights in clinical psychiatry. We believe such a framework can be provide timely feedback to the therapist regarding the quality of the conversation in interview sessions.