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

Thomas A Pearson; Robert M Califf; Rebecca Roper; Michael M Engelgau; Muin J Khoury; Carmela Alcantara; Craig Blakely; Cheryl Anne Boyce; Marishka Brown; Thomas L Croxton; Kathleen Fenton; Melissa C Green Parker; Andrew Hamilton; Lorens Helmchen; Lucy L Hsu; David M Kent; Amy Kind; John Kravitz; George John Papanicolaou; Mattia Prosperi; Matt Quinn; LeShawndra N Price; Paula K Shireman; Sharon M Smith; Rhonda Szczesniak; David Calvin Goff Jr; George A Mensah

doi:10.1016/j.jacc.2020.05.043

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

J Am Coll Cardiol. 2020 Jul 21;76(3):306-320. doi: 10.1016/j.jacc.2020.05.043.

Authors

Thomas A Pearson¹, Robert M Califf², Rebecca Roper³, Michael M Engelgau³, Muin J Khoury⁴, Carmela Alcantara⁵, Craig Blakely⁶, Cheryl Anne Boyce³, Marishka Brown⁷, Thomas L Croxton⁷, Kathleen Fenton³, Melissa C Green Parker³, Andrew Hamilton⁸, Lorens Helmchen⁹, Lucy L Hsu¹⁰, David M Kent¹¹, Amy Kind¹², John Kravitz¹³, George John Papanicolaou¹⁰, Mattia Prosperi¹⁴, Matt Quinn¹⁵, LeShawndra N Price³, Paula K Shireman¹⁶, Sharon M Smith¹⁷, Rhonda Szczesniak¹⁸, David Calvin Goff Jr¹⁰, George A Mensah¹⁹

Affiliations

¹ College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida. Electronic address: tapearson@ufl.edu.
² School of Medicine and Duke Clinical Research Institute, Duke University, Durham, North Carolina.
³ Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
⁴ Office of Genomics and Precision Public Health, Centers for Disease Control and Prevention, Atlanta, Georgia.
⁵ Columbia University School of Social Work, New York, New York.
⁶ School of Public Health and Information Science, University of Louisville, Louisville, Kentucky.
⁷ Division of Lung Diseases, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
⁸ AllianceChicago, Chicago, Illinois.
⁹ Health Policy and Management, Milken Institute School of Public Health, George Washington University, Washington, DC.
¹⁰ Division of Cardiovascular Sciences, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
¹¹ Predictive Analytics and Comparative Effectiveness (PACE) Center, Sackler School of Graduate Biomedical Sciences, Tufts University, Tufts Medical Center, Boston, Massachusetts.
¹² Department of Medicine Health Services and Care Research Program, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin.
¹³ Geisinger Health System, Danville, Pennsylvania.
¹⁴ College of Medicine and College of Public Health and Health Professions, University of Florida Health Science Center, Gainesville, Florida.
¹⁵ Health Technology, Telemedicine and Advanced Technology Research Center, Frederick, Maryland.
¹⁶ School of Medicine, University of Texas Health Science Center at San Antonio and South Texas Veterans Health Care System, San Antonio, Texas.
¹⁷ Division of Blood Diseases and Resources, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland.
¹⁸ Division of Biostatistics & Epidemiology, Division of Pulmonary Medicine, Cincinnati Children's Hospital, Department of Pediatrics, University of Cincinnati, Cincinnati, Ohio.
¹⁹ Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland. Electronic address: george.mensah@nih.gov.

PMID: 32674794
DOI: 10.1016/j.jacc.2020.05.043

Abstract

Emerging data science techniques of predictive analytics expand the quality and quantity of complex data relevant to human health and provide opportunities for understanding and control of conditions such as heart, lung, blood, and sleep disorders. To realize these opportunities, the information sources, the data science tools that use the information, and the application of resulting analytics to health and health care issues will require implementation research methods to define benefits, harms, reach, and sustainability; and to understand related resource utilization implications to inform policymakers. This JACC State-of-the-Art Review is based on a workshop convened by the National Heart, Lung, and Blood Institute to explore predictive analytics in the context of implementation science. It highlights precision medicine and precision public health as complementary and compelling applications of predictive analytics, and addresses future research and training endeavors that might further foster the application of predictive analytics in clinical medicine and public health.

Keywords: exposome; genome; implementation research; predictive analytics; social determinants.

Publication types

Research Support, N.I.H., Extramural
Review

MeSH terms

Cardiology*
Delivery of Health Care / methods*
Humans
Periodicals as Topic*
Precision Medicine / methods*
Prognosis
Public Health*