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Related Experiment Video

Updated: Jul 26, 2026

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Precision Health Analytics With Predictive Analytics and Implementation Research: JACC State-of-the-Art Review.

Thomas A Pearson1, Robert M Califf2, Rebecca Roper3

  • 1College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida.

Journal of the American College of Cardiology
|July 18, 2020
PubMed
Summary

Predictive analytics, using data science, can improve understanding and control of heart, lung, blood, and sleep disorders. Implementation science research is crucial to integrate these advances into healthcare effectively.

Keywords:
exposomegenomeimplementation researchpredictive analyticssocial determinants

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Area of Science:

  • Data Science
  • Implementation Science
  • Public Health

Background:

  • Predictive analytics leverages complex health data for disease management.
  • Heart, lung, blood, and sleep disorders can benefit from advanced data analysis.
  • Implementation science is key to translating data science into clinical practice.

Purpose of the Study:

  • To explore predictive analytics within implementation science for health.
  • To highlight precision medicine and public health applications.
  • To identify future research and training needs.

Main Methods:

  • State-of-the-Art Review based on a National Heart, Lung, and Blood Institute workshop.
  • Exploration of predictive analytics in the context of implementation science.
  • Discussion of precision medicine and precision public health.

Main Results:

  • Predictive analytics offers opportunities for understanding and controlling major health conditions.
  • Implementation research is necessary to define benefits, harms, reach, and sustainability.
  • Resource utilization implications need to be understood for policy.

Conclusions:

  • Precision medicine and precision public health are key applications of predictive analytics.
  • Further research and training are needed to advance predictive analytics in clinical medicine and public health.
  • Integrating predictive analytics requires a focus on implementation science principles.