Diagnostic Agreement Between Telemedicine on Social Networks and Teledermatology Centers

Sophia Serhrouchni; Alexandre Malmartel


Ann Fam Med. 2021;19(1):24-29. 

In This Article


Design and Inclusion

A retrospective observational study was conducted on images published by GPs on Twitter (a social network open to the public allowing the exchange of short messages and images) and MedPics (a French-language application for sharing medical images and collaborative clinical cases reserved for health care professionals) (Figure 1). On Twitter included images were published from January through June 2016 by a GP and tagged with the hashtags #dermatotoctoc, #doctoctoc or #docstoctoc (toc toc is French for the English phrase knock knock). These hashtags have been used by French health care professionals to ask medical questions of the community since 2012. On MedPics, the last 50 images published by a GP in the Dermatology section prior to and through June 1, 2016, were included. The exclusion criteria were publication by a user who was not identified as a GP in their profile and the absence of a response from a graduate doctor based on their profile. Since the same image could be tagged with several hashtags, duplicate images on Twitter were excluded. These criteria were established, after discussion with the teledermatologists participating in the study, to ensure it's feasibilty and enable the inclusion and analysis of 50 to 60 images.

Figure 1.

Sources and distribution of study images.


The social network diagnosis was used as the leading diagnosis for each image and defined as the most frequently suggested diagnosis among graduate physicians on Twitter and MedPics. The lead diagnosis was unique and selected by criteria in the following order: the dominant diagnosis suggested by dermatologists, then the dominant diagnosis among other responding doctors, and excluding responses given by the GP who published the image.

The TDS diagnosis was the diagnosis given by the responses of the TDS of 1 of 2 teaching hospitals. The diagnosis selected had to respond to the question asked by the requesting GP on Twitter and MedPics and was the main diagnosis (in case of concomitant pathologies).

The reference diagnosis of the image was established by an expert committee of 12 dermatologists from a teaching hospital (Saint Louis Hospital, Paris, France). This diagnosis had to be unique. It was used to define the percentage of exact diagnoses for Twitter, MedPics, and TDS relative to the reference diagnoses.

Specific management recommendations such as the need for dermatological consultations to perform a biopsy or for a dermatological follow-up, were collected.


Included images were stratified on the social networks (Twitter or MedPics), then randomized before being sent to the TDS of 2 teaching hospitals (Henri Mondor Hospital, Créteil, France and Tenon Hospital, Paris, France) (Figure 1).

Diagnoses collection in TDS was carried out under the usual operating conditions, which meant sending an e-mail to the dedicated address of the TDS, including the image with all the clinical information available on Twitter and MedPics, between 9:00 AM to 6:00 PM in accordance with the schedules of the teledermatologists who had agreed to participate in the study.

For each image, the questions asked of the TDS and the expert committee were the same questions asked on Twitter and MedPics.

Statistical Analyses

The diagnoses were grouped by type of pathology to enable analyses to be carried out. They were performed with an α risk of 5% using R, Version 3.1.1 (R Project for Statistical Computing). Agreement analyses were conducted using Cohen's κ method and the 95% CIs were determined by a bootstrap.[11] Chi square and Student tests were used to compare categorical and quantitative variables, respectively. McNemar tests were used to compare paired categorical data.

The primary analysis consisted of studying diagnostic agreement between social network diagnoses and TDS diagnoses using Cohen's κ coefficient. Secondary analyses included studying (1) the agreement between social networks (Twitter and MedPics) and the expert committee, and (2) the number of correct diagnoses in social networks (Twitter and MedPics) and in TDS. Analyses were performed on all images, then images from Twitter and MedPics separately, and finally among the subgroup of images with at least 1 response by a dermatologist on Twitter and MedPics, and among the subgroup with no dermatologist's response.


The analyses were based on public anonymized data, complying with terms of use and privacy policies for both Twitter and MedPics. No exact quotations were included in this article. A registration to the French Commission for Data Protection and Liberties (Commission Nationale de l'Informatique et des Libertés) was made before the completion of this study (CNIL; #1990661). As the data published on social networks are public and the study was carried out retrospectively, approval by an ethics committee was not necessary.