JMIR Publications

ABSTRACT

Background:

Patient engagement is a critical but challenging public health priority in behavioral healthcare. During telehealth sessions, healthcare providers need to rely predominantly on verbal strategies rather than typical non-verbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and provider training on telehealth patient engagement is unavailable or quite limited. Therefore, we explore the application of machine learning for estimating patient engagement. This can assist psychotherapists in the development of a therapeutic relationship with the patient and enhance patient engagement in the treatment of mental health conditions during telemental health sessions.

Objective:

The objective of this study was to examine the ability of machine learning models to estimate patient engagement levels during a telemental health session and understand whether the machine learning approach could support therapeutic engagement between the client and psychotherapist.

Methods:

We propose a multimodal learning-based framework. We uniquely leverage latent vectors corresponding to Affective, and Cognitive features frequently used in psychology literature to understand a person’s level of engagement. Given the labeled data constraints that exist in healthcare, we explore a semi-supervised learning solution. To further the development of similar technologies that can be useful for telehealth, we also plan to release a dataset MEDICA containing 1229 video clips, each 3 seconds long, and show experiments on the same. The efficacy of our method is also demonstrated through real-world experiments.

Results:

Our framework reports a 40% improvement in RMSE (Root Mean Squared Error) over state-of-the-art methods for engagement estimation. In our real-world tests done on 438 video clips obtained from psychotherapy sessions done with 20 patients, in comparison to prior methods, positive correlations were observed between psychotherapists' working alliance inventory scores and our mean and median engagement level estimates. This indicates the potential of the proposed model to present patient engagement estimations that align well with the engagement measures used by psychotherapists.

Conclusions:

Patient engagement has been identified to be important to improve therapeutic alliance. However little research has been undertaken to measure it in a telehealth setting wherein the conventional cues are not available to the therapist to make a confident decision. The framework developed is an attempt to model person-oriented engagement modeling theories within machine learning frameworks to estimate the level of engagement of the patient accurately and reliably in telehealth. The results are encouraging and emphasize the value of combining psychology and machine learning to understand patient engagement. Further testing in the real-world setting is necessary to fully assess its usefulness in helping therapists gauge patient engagement during virtual sessions. However, the proposed approach, and the creation of the new dataset, MEDICA, opens avenues for future research, and the development of impactful tools for telehealth.