JMIR Publications

ABSTRACT

Background:

Communication is a critical component of the patient-provider relationship; however, limited research exists on the role of nonverbal communication. Virtual human training is an informatics-based educational strategy that offers various benefits in communication skill training directed at providers. Although recent informatics-based interventions aimed at improving communication have mainly focused on verbal communication, research is needed to better understand how virtual humans can improve verbal and nonverbal communication and further elucidate the patient-provider dyad.

Objective:

The purpose of this study is to enhance a conceptual model that incorporates technology to examine verbal and nonverbal components of communication and develop a nonverbal assessment that will be included in the virtual simulation for further testing.

Methods:

This study will consist of a multistage mixed methods design, including convergent and exploratory sequential components. A convergent mixed methods design will be carried out to examine the mediating effects of nonverbal communication. Quantitative (e.g., MPathic game scores, Kinect nonverbal data, OSCE communication score, and RIAS and FACS coding of video) and qualitative (e.g., video recordings of MPathic-VR interventions and student reflections) data will be collected simultaneously. Data will be merged to determine the most crucial components of nonverbal behavior in human-computer interaction. An exploratory sequential design will proceed, consisting of a grounded theory qualitative phase. Using theoretical, purposeful sampling, interviews will be conducted with oncology providers probing intentional nonverbal behaviors. The qualitative findings will aid the development of a nonverbal communication model that will be included in a virtual human. The subsequent quantitative strand will incorporate and validate a new automated nonverbal communication behavior assessment into the virtual human simulation, MPathic-VR, by assessing interrater reliability, code interactions, and dyadic data analysis by comparing Kinect responses (system recorded) to manually scored records for specific nonverbal behaviors. Data will be integrated using building integration to develop the automated nonverbal communication behavior assessment and conduct a quality check of these nonverbal features.

Results:

Secondary data will be used to analyze the first part of this study and will be obtained from the MPathic-VR randomized controlled trial dataset (n = 210 medical students, 840 video-recordings of interactions). Following the analysis of the convergent design, participants consisting of medical providers (n = 30) will be recruited for the qualitative phase of the subsequent exploratory sequential design. We plan to complete data analysis by March 2023 to analyze and integrate these findings.

Conclusions:

The results from this study contribute to the improvement of patient-provider communication, both verbal and nonverbal, including the dissemination of health information and health outcomes for patients. The findings from this research aim to transfer to various topical areas, including medication safety, informed consent processes, patient instructions, and treatment adherence between patients and providers.