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Big Data and Disease Prevention: From Quantified Self to Quantified Communities
Meredith A Barrett1,2, Olivier Humblet1,2, Robert A Hiatt3
11 Center for Health and Community, University of California-San Francisco , San Francisco, California.
Big Data
|July 22, 2016
Summary
Big data offers significant potential for disease prevention by identifying risk factors and improving health interventions. Leveraging big data can lead to healthier behaviors and environments, reducing chronic disease burden.
Area of Science:
- Public Health
- Epidemiology
- Health Informatics
Background:
- Big data is primarily recognized for its role in enhancing medical care.
- Its crucial, yet underappreciated, function in disease prevention is gaining attention.
- Modifiable risk factors like diet, physical activity, and pollution significantly contribute to chronic diseases.
Purpose of the Study:
- To explore the applications of big data in disease prevention.
- To identify new sources of big data relevant to population health.
- To present case studies demonstrating the use of big data for prevention.
Main Methods:
- Review of new big data sources in population health.
- Analysis of big data applications for identifying disease risk factors.
- Case study development to illustrate big data utilization in prevention.
Main Results:
- Big data facilitates the discovery of population, subpopulation, and individual risk factors.
- It enhances the effectiveness of interventions promoting healthier behaviors and environments.
- New data sources and applications for big data in prevention are identified.
Conclusions:
- Big data is a powerful tool for proactive disease prevention, complementing medical care.
- Effective implementation requires addressing significant obstacles.
- Harnessing big data can significantly reduce the burden of chronic diseases.
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