Open AccessGoogle Trends in Dermatology: Scoping Review of the Literature
Author(s) -
Torunn Sivesind,
Mindy D Szeto,
William Kim,
Robert P. Dellavalle
Publication year - 2021
Publication title -
jmir dermatology
Language(s) - English
Resource type - Journals
ISSN - 2562-0959
DOI - 10.2196/27712
Subject(s) - specialty , social media , medline , analytics , medicine , dermatology , family medicine , data science , computer science , world wide web , political science , law
Background Google Trends is a powerful online database and analytics tool of popular Google search queries over time and has the potential to inform medical practice and priorities. Objective This review aimed to survey Google Trends literature in dermatology and elucidate its current roles and relationships with the field. Methods A literature search was performed using PubMed to access and review relevant dermatology-related Google Trends studies published within the last 5 years. Results Current research utilizing Google Trends data provides insight related to skin cancer, pruritus, cosmetic procedures, and COVID-19. We also found that dermatology is presently the highest-searched medical specialty—among 15 medical and surgical specialties as well as general practitioners. Google searches related to dermatology demonstrate a seasonal nature for various skin conditions and sun-related topics, depending on a region’s inherent climate and hemi-sphere. In addition, celebrity social media and other viral posts have been found to potentiate Google searches about dermatology and drive public interest. Conclusions A limited number of relevant studies may have been omitted by the simplified search strategy of this study, as well as by restriction to English language articles and articles indexed in the PubMed database. This could be expanded upon in a secondary systematic review. Future re-search is warranted to better understand how Google Trends can be utilized to improve the quality of clinic visits, drive public health campaigns, and detect disease clusters in real time.