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Using Sensor-Captured Patient-Generated Data to Support Clinical Decision-making in PTSD Therapy

  • Hayley I. Evans
  • , Myeonghan Ryu
  • , Theresa Hsieh
  • , Jiawei Zhou
  • , Kefan Xu
  • , Kenneth W. Akers
  • , Andrew M. Sherrill
  • , Rosa I. Arriaga
  • Georgia Institute of Technology
  • Emory University

Research output: Contribution to journalArticlepeer-review

Abstract

Today, clinicians have limited visibility into the quality of homework exercises that occur outside of the clinical context; however, understanding patient performance in these exercises is essential for guiding patient-centered care. To address this, we present the Clinician Homework Review (CHR), a unique measure and interface that displays similarity ratings calculated using sensor-captured patient-generated data (sPGD; i.e. heart rate, phone usage, ambient noise, and physical activity) for therapeutic exercises outside of the clinical setting within the post-traumatic stress disorder (PTSD) treatment context. Through concept testing sessions with 10 clinicians, we examine how sPGD can be leveraged to measure and investigate what contributes to patient performance in a therapeutic exercise. We also share in-depth information regarding clinician interpretation and planned use of data displayed by CHR in clinical sessions with patients. We frame our results in the context of situated objectivity and propose the notion of "perceived reference weight," which describes the significance attributed to contextualized data. In doing so, we support clinical decision-making in PTSD therapy.

Original languageEnglish
Article number149
JournalProceedings of the ACM on Human-Computer Interaction
Volume8
Issue numberCSCW1
DOIs
StatePublished - 23 Apr 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • clinical decision-making
  • exposure therapy
  • mental health
  • post-traumatic stress disorder
  • sensor-captured patient-generated data
  • veterans

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