TY - JOUR
T1 - Precision Health Analytics With Predictive Analytics and Implementation Research
T2 - JACC State-of-the-Art Review
AU - Pearson, Thomas A.
AU - Califf, Robert M.
AU - Roper, Rebecca
AU - Engelgau, Michael M.
AU - Khoury, Muin J.
AU - Alcantara, Carmela
AU - Blakely, Craig
AU - Boyce, Cheryl Anne
AU - Brown, Marishka
AU - Croxton, Thomas L.
AU - Fenton, Kathleen
AU - Green Parker, Melissa C.
AU - Hamilton, Andrew
AU - Helmchen, Lorens
AU - Hsu, Lucy L.
AU - Kent, David M.
AU - Kind, Amy
AU - Kravitz, John
AU - Papanicolaou, George John
AU - Prosperi, Mattia
AU - Quinn, Matt
AU - Price, Le Shawndra N.
AU - Shireman, Paula K.
AU - Smith, Sharon M.
AU - Szczesniak, Rhonda
AU - Goff, David Calvin
AU - Mensah, George A.
N1 - Funding Information:
The authors give special thanks to Mary Gipson (University of Florida) for her technical support and coordination while compiling this report.
PY - 2020/7/21
Y1 - 2020/7/21
N2 - 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.
AB - 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.
KW - exposome
KW - genome
KW - implementation research
KW - predictive analytics
KW - social determinants
UR - http://www.scopus.com/inward/record.url?scp=85087500774&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85087500774&partnerID=8YFLogxK
U2 - 10.1016/j.jacc.2020.05.043
DO - 10.1016/j.jacc.2020.05.043
M3 - Review article
C2 - 32674794
AN - SCOPUS:85087500774
VL - 76
SP - 306
EP - 320
JO - Journal of the American College of Cardiology
JF - Journal of the American College of Cardiology
SN - 0735-1097
IS - 3
ER -