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Updated: Jun 3, 2025

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study
Published on: March 14, 2017
Association between video consultation use and general practitioner and practice characteristics: a cross-sectional
Thim Prætorius1, Eskild Klausen Fredslund2, Daniel Cæsar Torp2
1Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark thipra@rm.dk.
Background:
Video consultations disrupt how general practice provides care and how patients receive it. A step towards understanding the use of video consultation is to study the association between user status and GP and practice characteristics.
Aim:
To study the association between GP and general practice characteristics and video consultations user status (user, never user, and former user).
Design & Setting:
An anonymous, web-based, cross-sectional survey was distributed to all 1674 Danish general practices (single-handed, collaborative, and partnership forms) contracting with and working on a collective agreement with the public funder.
Method:
Multinomial logistic regression was used to correlate video consultation user status and (1) general practice characteristics, and GPs' (2a) objective characteristics and (2b) subjective attitudes towards video consultations and organisational change.
Results:
The study sample included 416 GPs. Comparing users of video consultations with never users, operating as a partnership practice (relative risk ratio [RRR] 0.22; 95% confidence interval [CI] = 0.06 to 0.85; P<0.05) and practices with six or more practice staff (RRR 0.05; 95% CI = 0.01 to 0.28; P<0.001) were significantly more likely to be users. GPs with a high degree of tech savviness (RRR 0.02; 95% CI = 0.001 to 0.17; P<0.001) and openness to organisational change (RRR 0.26; 95% CI = 0.08 to 0.85; P<0.05) were significantly more likely to use video consultations.
Conclusion:
Characteristics of general practice and GPs are associated with video consultation user status (being a user, never user, or former user). Future research should use a difference-in-difference study design and register data to make causality claims.
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