Big Data’s Role in Precision Public Health

Abstract

Precision public health is an emerging practice to more granularly predict and understand public health risks and customize treatments for more specific and homogeneous subpopulations, often using new data, technologies, and methods. Big data is one element that has consistently helped to achieve these goals, through its ability to deliver to practitioners a volume and variety of structured or unstructured data not previously possible. Big data has enabled more widespread and specific research and trials of stratifying and segmenting populations at risk for a variety of health problems. Examples of success using big data are surveyed in surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease. Using novel big data or big data approaches has risks that remain to be resolved. The continued growth in volume and variety of available data, decreased costs of data capture, and emerging computational methods mean big data success will likely be a required pillar of precision public health into the future. This review article aims to identify the precision public health use cases where big data has added value, identify classes of value that big data may bring, and outline the risks inherent in using big data in precision public health efforts.

Using Constructive Research to Structure the Path to Transdisciplinary Innovation and Its Application for Precision Public Health with Big Data Analytics

“…velocity, and variety, such as data streaming from medical devices, environment sensors, and GPS locators, big data analytics has generated optimism for the potential of its value for health research and interventions (Dolley, 2018). Analytics on big data can be performed at two different stages: 1) in real-time as the streaming data is arriving, known as stream processing, real-time, or online analytics and 2) with persistent historical data after the data has been stored through knowledge discovery or data mining (McGregor, 2013a;Palem, 2014).…”

“…Analytics on big data can be performed at two different stages: 1) in real-time as the streaming data is arriving, known as stream processing, real-time, or online analytics and 2) with persistent historical data after the data has been stored through knowledge discovery or data mining (McGregor, 2013a;Palem, 2014). However, Dolley's (2018) review demonstrates several isolated examples of multidisciplinary and interdisciplinary research and innovation with big data analytics in health that have not been able to evolve to transdisciplinary research and innovation to more broadly create value for precision public health. A systematic approach is required to transcend beyond and integrate disciplinary paradigms to enable the broader impact.…”

“…One complex societal challenge that has emerged in recent years is that of "precision public health". The term was coined in Australia by Tarun Weeramanthri in 2013 (Dolley, 2018) and is considered to be "a new field driven by technological advances that enable more precise descriptions and analyzes of individuals and population groups, with a view to improving the overall health of populations" (Baynam et al, 2017).…”

Using big data to promote precision oral health in the context of a learning healthcare system

“…23 This approach requires the usage of big data in order to predict risk profiles, improve our understanding of the pathogenesis of disease, and develop targeted treatment strategies, among other applications. 24 In this article, we will be discussing the potential role of big data in promoting the oral health of the public. The implementation of big data into oral health research and practice is still a developing process; however, successful implementation of big data practices can overcome the challenges preventing the integration of precision medicine and oral health.…”

Artificial intelligence or manufactured stupidity? The need for injury informaticians in the big data era

“…We are now in an age where there are more health data than ever before collected (the ‘big data’ era2), at an individual person-linked level across the population, to a fine-grained clinical/contextual level of detail (with the spread of integrated electronic medical records, or iEMRs), using a multitude of rapidly expanding data types and sources, many of which are available near real-time (ie, the four defining characteristics of ‘big data’ being volume, variety, veracity and value3). A recent article reported that 33 zettabytes (33 trillion gigabytes) of data were created worldwide in 2018, and that it is anticipated that by 2020 there will be 44 zettabytes (44 trillion gigabytes) of health data alone created!4 Building the core infrastructure for compilation, storage, handling and integration of these health data (eg, data lakes, business vaults, data marts and data warehouses) is occupying vast amounts of time and resources of health departments, with the demand for data engineers, data architects, data scientists, health informaticians and the like surpassing the market supply.…”