Healthc Inform Res.  2023 Oct;29(4):394-399. 10.4258/hir.2023.29.4.394.

Videoconferencing Applications for Training Professionals on Nonverbal Communication in Online Clinical Consultations

• Kyyhkynen R
• Peltonen LM
• Smed J

• KMID: 2547692
• DOI: http://doi.org/10.4258/hir.2023.29.4.394

Abstract

Objectives
The use of videoconferencing technologies for clinician-patient online consultations has become increasingly popular. Training on online communication competence through a videoconferencing application that integrates nonverbal communication detection with feedback is one way to prepare future clinicians to conduct effective online consultations. This case report describes and evaluates two such applications designed for healthcare professionals and students in healthcare-related fields.
Methods
We conducted a literature review using five databases, including the Web of Science, Scopus, PubMed, ACM, IEEE, and CINAHL in the spring of 2022.
Results
We identified seven studies on two applications, ReflectLive and EQClinic. These studies were conducted by two research groups from the USA and Australia and were published between 2016 and 2020. Both detected nonverbal communication from video and audio and provided computer-generated feedback on users’ nonverbal communication. The studies evaluated usability, effectiveness in learning communication skills, and changes in the users’ awareness of their nonverbal communication. The developed applications were deemed feasible. However, the feedback given by the applications needs improvement to be more beneficial to the user. The applications were primarily evaluated with medical students, with limited or no attention given to questions regarding ethics, information security, privacy, sustainability, and costs.
Conclusions
Current research on videoconferencing systems for training online consultation skills is very limited. Future research is needed to develop more user-centered solutions, focusing on a multidisciplinary group of students and professionals, and to explore the implications of these technologies from a broader perspective, including ethics, information security, privacy, sustainability, and costs.

Keyword

Nonverbal Communication, Videoconferencing, Feedback, Telehealth, Facial Expression

Reference

References

1. Kissi J, Annobil C, Mensah NK, Owusu-Marfo J, Osei E, Asmah ZW.Telehealth services for global emergencies: implications for COVID-19: a scoping review based on current evidence. BMC Health Serv Res. 2023; 23(1):567. https://doi.org/10.1186/s12913-023-09584-4.

2. Gajarawala SN, Pelkowski JN.Telehealth Benefits and Barriers. J Nurse Pract. 2021; 17(2):218–21. https://doi.org/10.1016/j.nurpra.2020.09.013.

3. Budakoglu Iİ, Sayilir MU, Kiyak YS, Coskun O, Kula S.Telemedicine curriculum in undergraduate medical education: a systematic search and review. Health Technol (Berl). 2021; 11(4):773–81. https://doi.org/10.1007/s12553-021-00559-1.

4. Edirippulige S, Armfield NR.Education and training to support the use of clinical telehealth: a review of the literature. J Telemed Telecare. 2017; 23(2):273–82. https://doi.org/10.1177/1357633X16632968.

5. Henry SG, Fuhrel-Forbis A, Rogers MA, Eggly S.Association between nonverbal communication during clinical interactions and outcomes: a systematic review and meta-analysis. Patient Educ Couns. 2012; 86(3):297–315. https://doi.org/10.1016/j.pec.2011.07.006.

6. Gustin T. Telemedicine etiquette. Atanda A, Lovejoy JF, editors. Telemedicine in Orthopedic Surgery and Sports Medicine. Cham, Switzerland: Springer;2021. p. 65–80. https://doi.org/10.1007/978-3-030-53879-8_6.

7. Liu C, Lim RL, McCabe KL, Taylor S, Calvo RA.A web-based telehealth training platform incorporating automated nonverbal behavior feedback for teaching communication skills to medical students: a randomized crossover study. J Med Internet Res. 2016; 18(9):e246. https://doi.org/10.2196/jmir.6299.

8. Liu C, Calvo RA, Lim R.Improving medical students’ awareness of their non-verbal communication through automated non-verbal behavior feedback. Front in ICT. 2016; 3:11. https://doi.org/10.3389/fict.2016.00011.

9. Liu C, Calvo RA, Lim R, Taylor S. EQClinic: a platform for improving medical students’ clinical communication skills. Yin X, Geller J, Li Y, Zhou R, Wang H, Zhang Y, editors. Health Information Science. Cham, Switzerland: Springe;2016. p. 73–84. https://doi.org/10.1007/978-3-319-48335-1_8.

10. Liu C, Scott KM, Lim RL, Taylor S, Calvo RA.EQClinic: a platform for learning communication skills in clinical consultations. Med Educ Online. 2016; 21:31801. https://doi.org/10.3402/meo.v21.31801.

11. Faucett HA, Lee ML, Carter S.I should listen more: real-time sensing and feedback of non-verbal communication in video telehealth. Proc ACM Hum Comput Interact. 2017; 1(CSCW44):1–19. https://doi.org/10.1145/3134679.

12. Wu K, Liu C, Taylor S, Atkins PW, Calvo RA. Automatic mimicry detection in medical consultations. In : Proceedings of 2017 IEEE Life Sciences Conference (LSC); 2017 Dec 13–15; Sydney, Australia. p. 55–8. https://doi.org/10.1109/LSC.2017.8268142.

13. Wu K, Liu C, Calvo RA.Automatic nonverbal mimicry detection and analysis in medical video consultations. Int J Hum Comput Interact. 2020; 36(14):1379–92. https://doi.org/10.1080/10447318.2020.1752474.

14. Jonas CE, Durning SJ, Zebrowski C, Cimino F.An interdisciplinary, multi-institution telehealth course for third-year medical students. Acad Med. 2019; 94(6):833–7. https://doi.org/10.1097/ACM.0000000000002701.