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International Conference on Human-Computer Interaction

HCII 2020: Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health pp 473–490Cite as

Developing Parameters for a Technology to Predict Patient Satisfaction in Naturalistic Clinical Encounters

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12198))

Abstract

Patient-centered communication is crucial in the clinical encounter. Previous studies on patient satisfaction have focused on nonverbal cues and demographics of the patient separately; the integrated influence of both aspects is yet to be explored. This study aims to build a model to learn the quantitative relationship among nonverbal behaviors such as mutual gaze and social touch, demographics of the patients such as age, education and income, and patient perceptions of clinicians. Using 110 videotaped clinical encounters of patients from a study of assessing placebo, Echinacea, and doctor-patient interaction in the acute upper respiratory infection and a decision tree machine learning approach, duration per mutual gaze, percentage of mutual gaze, age, and social touch were identified as the top four important features in predicting how much patients liked their clinicians. Patients of older age, with higher percentage of mutual gaze, longer social touch duration and moderate duration per mutual gaze tended to report greater rating on likeness towards their clinicians. Findings from this study will be used to inform the design of a real-time automatic feedback system for physicians. By using the decision tree machine learning approach, the findings help determine the parameters required for the design of a real-time monitoring and feedback system of the quality of care and doctor-patient interaction in natural environments.

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Acknowledgments

This research was supported by NSF Division of Information & Intelligent Systems Award - “CHS: Small: Extracting affect and interaction information from primary care visits to support patient-provider interactions” (Grant No: 1816010).

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Authors and Affiliations

  1. College of Computing and Digital Media, DePaul University, Chicago, IL, 60604, USA

    Tianyi Tan, Enid Montague, Jacob Furst & Daniela Raicu

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  1. Tianyi Tan
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  2. Enid Montague
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  3. Jacob Furst
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  4. Daniela Raicu
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Correspondence to Tianyi Tan .

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  1. Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

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Tan, T., Montague, E., Furst, J., Raicu, D. (2020). Developing Parameters for a Technology to Predict Patient Satisfaction in Naturalistic Clinical Encounters. In: Duffy, V. (eds) Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Posture, Motion and Health. HCII 2020. Lecture Notes in Computer Science(), vol 12198. Springer, Cham. https://doi.org/10.1007/978-3-030-49904-4_35

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  • DOI: https://doi.org/10.1007/978-3-030-49904-4_35

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-49904-4

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