Systematic review of health research using internet search data

Abstract

Novel types of digital data, including internet search data, have potential to improve understanding of early predictors of serious health conditions and enable timely management. While many studies have used aggregate anonymized search trends in this way, what is less clear is the predictive or diagnostic value of online searches at the individual level. While an increasing number of studies have used these kinds of data, this research method is still emerging. We therefore undertook a systematic review of published research that has assessed the predictive or diagnostic value of individual internet search data. MEDLINE and Embase were searched through March 2024 for studies utilising individual internet search data to predict or diagnose patient disease status. Due to the heterogeneous nature of the design, methodology and reported outcomes of included studies, a narrative synthesis of studies and pre-specified outcomes was performed. Study quality was assessed with the Newcastle-Ottawa Scale and PROBAST tool. Twenty-three studies met the inclusion criteria. Conditions of interest encompassed mental health, neurological conditions, malignancies, and miscellaneous healthcare presentations. Data on individuals’ search history were obtained from search engines using anonymous search queries (Bing, Yahoo!) or from consented participants (Google) where consent rates ranged from 20–70%. Wide variability in AUROC (range: <0.53 to > 0.99), sensitivity (range: 0.44 to 0.81) and F1 score (0.36 to 0.80) were reported. Studies noted a range of predictive linguistic, temporal, and other features (e.g., spelling error frequency). This review demonstrated that the use of individual internet search data holds diagnostic and predictive potential, with evidence of strong associative features. However, there was significant variability regarding conditions of interest, methodology, and predictive models used. Given the common use of internet searches by patients as part of their healthcare journeys, individual search data holds significant potential, and justifies further research, including the use of established diagnoses.