Table 1.

Examples of the Potential Applications of Big Data in Precision Public Health, by Person, Place and Time

Source of Big Data	Public Health Assessment (Characterizing population health outcomes and implementation needs and disparities in populations; natural experiments)	Implementation Studies (Conducting multilevel intervention studies to improve implementation and health outcomes)
Place (zip codes, census-tract data, countv-level data, neighborhood characteristics, health systems, and linkages with environmental and socio-economic characteristics)	Use refined geographic area analysis in surveillance (e.g. small area analysis) Link geographic data with other sources of information such as environmental exposures	Target implementation strategies across geographic locations or health systems (e.g. randomizing use of decision support tools to prompt providers/patients in different healthcare systems)
Person (demographic characteristics, genetics, biomarkers, electronic health records, personal devices, social media use)	Use molecular subtypes in cancer surveillance, Use of family history and genetic factors to stratify the population by risk level	Target implementation studies based on characteristics of patients, providers, and policy makers
Time (longitudinal data of individual personal characteristics and environmental/spatial information)	Use longitudinal data in addition to cross sectional information in assessment (e.g. repeated measures in public health surveys)	Collect longitudinal data in implementation studies (e.g. using smart phone apps to provide reminders to medication use, and to measure adherence to hypertension treatment)