Goal 1: Promote Human Health

Submitted by (@greg.martin)

Predictive analytics to engage healthy behaviors and maintain health while reducing cost

Predictive Health employs the principle that using modern health testing and predictive analytics will better define true health (not just absence of disease) and, in combination with large-scale data analytics, will facilitate predicting deviations from the healthy trajectory earlier than traditional disease diagnosis, thus allowing more effective and less costly interventions to maintain health. Predictive Health educates ...more »

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5 net votes
9 up votes
4 down votes
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Goal 2: Reduce Human Disease

Submitted by (@rakeshgoyal)

Moonshot: Turning the BMT EMR into a Research Record

The critical challenge is to develop a standards-based BMT electronic medical record (EMR) and integrate research capacity into the architecture of EMR systems. The ultimate goal would be to build de-identified complete data-sets which can be used to support observational studies and clinical trials, improve transplant outcomes and inform public policy.

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57 net votes
71 up votes
14 down votes
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Goal 2: Reduce Human Disease

Submitted by (@societyforvascularsurgery)

Data from regulatory studies a barrier to evidence-based medicine

Alignment of regulatory, healthcare, and research arms of the government is poor. There is a need to improve the design, quality and usefulness of data from regulatory studies to address major clinical questions and also to facilitate scientific inquiry. This is a barrier to evidence based medicine and improved treatments.

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2 net votes
3 up votes
1 down votes
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Goal 4: Develop Workforce and Resources

Submitted by (@teitelbaum)

Credible Data and Analysis of the Biomedical Research Workforce

There is a need for sensible policies that require collection and scientific analysis of credible data relating to the biomedical workforce. The data currently available are weak – for example no one knows, to a factor of 2X, the actual number of postdocs in the United States. The absence of credible human resource and labor market data on the biomedical research workforce is very surprising. NIH could contribute greatly ...more »

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6 net votes
15 up votes
9 down votes
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Goal 2: Reduce Human Disease

Submitted by (@nhlbiforumadministrator)

Harnessing the Tsunami of Patient-Generated Health Data

How can the NHLBI foster the development of effective tools and methodologies to harness the tsunami of patient-generated health data into a valuable resource for conducting patient-centered research? Some challenges to overcome might include a) how to enable the collaboration of the more traditional clinical scientists with scientists from other disciplines such as informatics or computational and data-enabled science ...more »

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-1 net votes
10 up votes
11 down votes
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Goal 3: Advance Translational Research

Submitted by (@nhlbiforumadministrator)

Build a National Surveillance of Chronic CV and Lung Diseases

There is a need to build a robust coordinated surveillance system on the incidence and prevalence of chronic diseases. Surveillance data are needed to: •Describe and monitor the burden, trends, and patterns of these diseases •Set parameters and metrics of research priorities •Identify where to target resources for prevention, treatment, and delivery of care •Track and monitor progress toward public health disease ...more »

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5 net votes
13 up votes
8 down votes
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Goal 3: Advance Translational Research

Submitted by (@jsullivan)

Create a National Action Plan for COPD

Lead a coordinated effort of government, patient advocacy organizations, professional organizations, payers and others to plan and implement a coordinated plan to improve COPD awareness, education for patients and healthcare professionals, treatment strategies, research and data collection, policies and public health infrastructure and programs.

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6 net votes
6 up votes
0 down votes
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Goal 3: Advance Translational Research

Submitted by (@rao000)

Advancing translational research requires timely in-depth analysis of large datasets

1. NHLBI investments over the last decade in terms of genomic approaches have yielded many research findings. 2. Rapid analyses of early data identified the "low hanging" fruit (and perhaps some/many important results were missed); this limited the scope of translation (partly because of relatively limited discovery?) 3. Important data are being generated at much greater rate than the data are processed/analyzed thoughtfully. ...more »

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1 net vote
11 up votes
10 down votes
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