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Viewpoint |

Fusing Randomized Trials With Big Data The Key to Self-learning Health Care Systems?

Derek C. Angus, MD, MPH1,2
[+] Author Affiliations
1Department of Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
2Associate Editor, JAMA
JAMA. 2015;314(8):767-768. doi:10.1001/jama.2015.7762.
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This Viewpoint proposes ways that electronic health records and other “big data” can be integrated with randomized trial designs to leverage the strengths of both for research inferences.

Randomized clinical trials (RCTs) have revolutionized medicine by providing evidence on the efficacy and safety of drugs, devices, and procedures. Today, more than 40 000 RCTs are reported annually, their quality continues to increase, and oversight mechanisms ensure adequate protection of participants. However, RCTs have at least 4 related problems: (1) they are too expensive and difficult; (2) their findings are too broad (average treatment effect not representative of benefit for any given individual) and too narrow (trial population and setting not representative of general practice); (3) randomizing patients can make patients and physicians uncomfortable, especially when comparing different types of existing care; and (4) there are often long delays before RCT results diffuse into practice.

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