Routine Outcome Monitoring in Psychotherapy Treatment using Sentiment-Topic Modelling Approach

TL;DR

This paper proposes a sentiment-topic modeling approach for routine outcome monitoring in psychotherapy, aiming to provide an efficient, cost-effective alternative to traditional questionnaires for tracking patient progress.

Contribution

It introduces a novel computational method using sentiment-topic modeling to monitor psychotherapy outcomes, reducing reliance on questionnaires and enabling broader clinical application.

Findings

Demonstrated effectiveness of sentiment-topic modeling in capturing patient progress

Reduced time and financial costs compared to traditional questionnaires

Potential for scalable, automated outcome monitoring in clinical settings

Abstract

Despite the importance of emphasizing the right psychotherapy treatment for an individual patient, assessing the outcome of the therapy session is equally crucial. Evidence showed that continuous monitoring patient's progress can significantly improve the therapy outcomes to an expected change. By monitoring the outcome, the patient's progress can be tracked closely to help clinicians identify patients who are not progressing in the treatment. These monitoring can help the clinician to consider any necessary actions for the patient's treatment as early as possible, e.g., recommend different types of treatment, or adjust the style of approach. Currently, the evaluation system is based on the clinical-rated and self-report questionnaires that measure patients' progress pre- and post-treatment. While outcome monitoring tends to improve the therapy outcomes, however, there are many challenges in the current method, e.g. time and financial burden for administering questionnaires, scoring and analysing the results. Therefore, a computational method for measuring and monitoring patient progress over the course of treatment is needed, in order to enhance the likelihood of positive treatment outcome. Moreover, this computational method could potentially lead to an inexpensive monitoring tool to evaluate patients' progress in clinical care that could be administered by a wider range of health-care professionals.