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

Journal: JMIR formative research
PMID: 39832353

Abstract

BACKGROUND: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care 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 tele-mental health sessions.

Authors

  • Pooja Guhan
    IBM Research, Nairobi, Kenya.
  • Naman Awasthi
    Department of Computer Science, University of Maryland, College Park, MD, United States.
  • Kathryn McDonald
    Department of Psychiatry, Child and Adolescent Division, University of Maryland, Baltimore, MD, United States.
  • Kristin Bussell
    School of Nursing, University of Maryland, Baltimore, MD, United States.
  • Gloria Reeves
    Department of Psychiatry, Child and Adolescent Division, University of Maryland, Baltimore, MD, United States.
  • Dinesh Manocha
  • Aniket Bera
    Department of Computer Science, Purdue University, West Lafayett, IN, United States.

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