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Developing a Machine Learning-Based Automated Patient Engagement Estimator for Telehealth: Algorithm Development and

Pooja Guhan1, Naman Awasthi1, Kathryn McDonald2

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Summary
This summary is machine-generated.

Machine learning models can now estimate patient engagement in telehealth, improving therapeutic alliances. This technology assists psychotherapists by providing reliable engagement metrics during virtual mental health sessions.

Keywords:
engagement detectionmachine learningmental healthpatient engagementtelehealth

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Area of Science:

  • Psychology and Machine Learning
  • Behavioral Health Care Technology
  • Telehealth Innovations

Background:

  • Patient engagement is crucial in behavioral health but challenging in telehealth due to limited nonverbal cues.
  • Existing training for telehealth patient engagement is scarce, necessitating new methods for assessment.
  • Machine learning offers a potential solution for estimating patient engagement during virtual therapy sessions.

Purpose of the Study:

  • To evaluate machine learning models' ability to estimate patient engagement levels in tele-mental health.
  • To determine if machine learning can support and enhance therapeutic engagement between clients and psychotherapists.
  • To introduce a novel dataset for advancing telehealth engagement detection research.

Main Methods:

  • A multimodal learning approach was developed, utilizing latent vectors for affective and cognitive engagement features.
  • A semisupervised learning solution was explored due to labeled data constraints in healthcare.
  • The Multimodal Engagement Detection in Clinical Analysis (MEDICA) dataset, comprising 1229 video clips, was created and used for experiments.

Main Results:

  • The proposed algorithm achieved a 40% improvement in root mean square error for engagement estimation compared to state-of-the-art methods.
  • Real-world tests showed positive correlations between the model's engagement estimates and psychotherapists' Working Alliance Inventory scores.
  • The findings suggest the model's potential to provide patient engagement estimations that align with clinical assessments.

Conclusions:

  • Machine learning can accurately and reliably estimate patient engagement in telehealth, supporting therapeutic alliance.
  • The developed algorithm integrates psychological theories with machine learning for enhanced telehealth patient engagement assessment.
  • The creation of the MEDICA dataset and the proposed method open new research avenues for telehealth tools.