Enhancing the quality of cognitive behavioral therapy in community mental health through artificial intelligence generated fidelity feedback (Project AFFECT): a study protocol.

Journal: BMC health services research
Published Date:

Abstract

BACKGROUND: Each year, millions of Americans receive evidence-based psychotherapies (EBPs) like cognitive behavioral therapy (CBT) for the treatment of mental and behavioral health problems. Yet, at present, there is no scalable method for evaluating the quality of psychotherapy services, leaving EBP quality and effectiveness largely unmeasured and unknown. Project AFFECT will develop and evaluate an AI-based software system to automatically estimate CBT fidelity from a recording of a CBT session. Project AFFECT is an NIMH-funded research partnership between the Penn Collaborative for CBT and Implementation Science and Lyssn.io, Inc. ("Lyssn") a start-up developing AI-based technologies that are objective, scalable, and cost efficient, to support training, supervision, and quality assurance of EBPs. Lyssn provides HIPAA-compliant, cloud-based software for secure recording, sharing, and reviewing of therapy sessions, which includes AI-generated metrics for CBT. The proposed tool will build from and be integrated into this core platform.

Authors

  • Torrey A Creed
    Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. tcreed@pennmedicine.upenn.edu.
  • Leah Salama
    Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA.
  • Roisin Slevin
    Lyssn.io, Inc, Seattle, USA.
  • Michael Tanana
    Lyssn, Seattle, WA.
  • Zac Imel
    Lyssn.io, Inc, Seattle, USA.
  • Shrikanth Narayanan
    Viterbi School of Engineering, University of Southern California, Los Angeles, CA, United States.
  • David C Atkins
    University of Washington.