Predictive Analytics: Helping Guide the Implementation Research Agenda at
                        the National Heart, Lung, and Blood Institute

Engelgau, Michael M.; Khoury, Muin J.; Roper, Rebecca; Curry, Jennifer S.; Mensah, George A.

doi:10.1016/j.gheart.2019.02.003

Cited by 6 publications

(7 citation statements)

References 28 publications

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“…The use of machine learning and decision support tools are increasingly adapted to specific health systems. Engelgau and colleagues recently discussed how tools of big data analytics can help identify barriers and facilitators for optimal implementation of interventions within the contexts of health and community policies, health systems, and community resources [ 29 ].…”

Section: Current Applications In Pphmentioning

confidence: 99%

The intersection of genomics and big data with public health: Opportunities for precision public health

et al. 2020

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Section: Current Applications In Pphmentioning

confidence: 99%

The intersection of genomics and big data with public health: Opportunities for precision public health

et al. 2020

Self Cite

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“…Predictive analytics uses extensive data, modeling, and algorithms to predict individual and population events and has a long history in commercial industries [ 3 ]. For better or worse, commercial industries have developed innovative techniques to mine demographic, socioeconomic, and consumer behavior data as part of the forecasting and analytics process.…”

Section: Social and Behavioral Determinants Of Health And Predictive mentioning

confidence: 99%

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

Tan¹,

Hatef²,

Taghipour³

et al. 2020

JMIR Med Inform

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In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

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“…The use of machine learning and decision support tools (13) adapted to specific health care delivery systems could enhance implementation of evidence-based guidelines. As discussed by Engelgau et al (14), tools of predictive analytics and big data can help identify major challenges for implementation including the identification of key barriers and facilitators within the socioecological context, various health and community policies, delivery strategies within health systems (e.g., physical infrastructure, availability of interventions), and community contexts (e.g., community resources, social deprivation, and economic issues). Predictive analytics based on big data offer new approaches to pinpoint key barriers and facilitators across the community context and lend insights into promising implementation strategies.…”

Section: More Precision Implementation By Placementioning

confidence: 99%

Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health?

Khoury¹,

Engelgau²,

Chambers³

et al. 2018

Public Health Genomics

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The field of public health genomics has matured in the past two decades and is beginning to deliver genomic-based interventions for health and health care. In the past few years, the terms precision medicine and precision public health have been used to include information from multiple fields measuring biomarkers, environmental and other variables to provide tailored interventions. In the context of public health, precision implies delivering the right intervention to the right population at the right time, with the goal of improving health for all. In addition to genomics, precision public health can be driven by "big data" as identified by volume, variety, and variability in biomedical, sociodemographic, environmental, geographic, and other information. Most current big data applications in health are in elucidating pathobiology and tailored drug discovery. We explore how big data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, and efforts to promote uptake of evidence-based interventions, by including more extensive information related to place, person and time. We use selected examples drawn from child health, cardiovascular disease and cancer to illustrate the promise and current methodologic and analytic challenges of big data to fulfill the promise of precision public health.

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Predictive Analytics: Helping Guide the Implementation Research Agenda at the National Heart, Lung, and Blood Institute

Cited by 6 publications

References 28 publications

The intersection of genomics and big data with public health: Opportunities for precision public health

The intersection of genomics and big data with public health: Opportunities for precision public health

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities

Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health?

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