Abstract
The rapidly increasing volume of health data generated from digital technologies have ushered in an unprecedented opportunity for health research. Despite its promises, big data approaches in understanding human behavior often do not consider conceptual premises that provide meaning to social and behavioral data. In this paper, we update the definition of big data, and review different types and sources of health data that researchers need to grapple with. We highlight three problems in big data approaches — data deluge, data hubris, and data opacity — that are associated with the blind use of computational analysis. Third, we lay out the importance of cultivating health data sense-making—the ability to integrate theory-led and data-driven approaches to process different types of health data and translating findings into tangible health outcomes — and illustrate how theorizing can matter in the age of big data.
Highlights
Volume and VelocityOne quality of big data is the sheer volume, often generated through digital and electronic mediums such as posts on social media, location tracking on smartphones, or even collective health records of patients in hospitals
Specialty section: This article was submitted to Health Communication, a section of the journal Frontiers in Communication
The rapidly increasing volume of health data generated from digital health technologies such as social media, search engines, smartphones, and wearable gadgets, as well as electronic health records (EHRs) and web patient portals have ushered in an unprecedented opportunity for health communication researchers to tap on both “naturally occurring” and structured data to improve health
Summary
One quality of big data is the sheer volume, often generated through digital and electronic mediums such as posts on social media, location tracking on smartphones, or even collective health records of patients in hospitals. User-generated data are naturally occurring digital traces (Peng et al, 2019) from (a) social media (e.g., Ayers et al, 2016), (b) wearables and health apps (Casselman et al, 2017), (c) search engines and web browsing behaviors (Mavragani et al, 2018). In terms of hybrid user-institutional generated big data, an example would be web patient portals, where patients could access their medical record, interact with their healthcare providers through direct messaging, and manage their medical health such as prescription refills, schedule appointments, or accessing education content (Wells et al, 2015; Antonio et al, 2019)
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