title: Making big data useful for health care: a summary of the inaugural mit critical data conference.
abstract: With growing concerns that big data will only augment the problem of unreliable research, the Laboratory of Computational Physiology at the Massachusetts Institute of Technology organized the Critical Data Conference in January 2014. Thought leaders from academia, government, and industry across disciplines-including clinical medicine, computer science, public health, informatics, biomedical research, health technology, statistics, and epidemiology-gathered and discussed the pitfalls and challenges of big data in health care. The key message from the conference is that the value of large amounts of data hinges on the ability of researchers to share data, methodologies, and findings in an open setting. If empirical value is to be from the analysis of retrospective data, groups must continuously work together on similar problems to create more effective peer review. This will lead to improvement in methodology and quality, with each iteration of analysis resulting in more reliability.
authors.affiliation: MIT Critical Data Conference 2014 Organizing Committee, Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, United States.
authors.forename: Omar
authors.identifier: http://orcid.org/0000-0001-8518-9006
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authors.lastname: Badawi
authors.forename: Thomas
authors.identifier: http://orcid.org/0000-0002-6806-7506
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authors.lastname: Brennan
authors.forename: Leo Anthony
authors.identifier: http://orcid.org/0000-0001-6712-6626
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authors.lastname: Celi
authors.forename: Mengling
authors.identifier: http://orcid.org/0000-0002-5338-6248
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authors.lastname: Feng
authors.forename: Marzyeh
authors.identifier: http://orcid.org/0000-0001-6349-7251
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authors.lastname: Ghassemi
authors.forename: Andrea
authors.identifier: http://orcid.org/0000-0003-2025-6591
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authors.lastname: Ippolito
authors.forename: Alistair
authors.identifier: http://orcid.org/0000-0003-0140-0797
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authors.lastname: Johnson
authors.forename: Roger G
authors.identifier: http://orcid.org/0000-0002-6318-2978
authors.initials: RG
authors.lastname: Mark
authors.forename: Louis
authors.identifier: http://orcid.org/0000-0002-3187-8030
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authors.lastname: Mayaud
authors.forename: George
authors.identifier: http://orcid.org/0000-0002-7418-3212
authors.initials: G
authors.lastname: Moody
authors.forename: Christopher
authors.identifier: http://orcid.org/0000-0003-4172-4446
authors.initials: C
authors.lastname: Moses
authors.forename: Tristan
authors.identifier: http://orcid.org/0000-0003-2150-1747
authors.initials: T
authors.lastname: Naumann
authors.forename: Marco
authors.identifier: http://orcid.org/0000-0002-3696-8852
authors.initials: M
authors.lastname: Pimentel
authors.forename: Tom J
authors.identifier: http://orcid.org/0000-0002-5676-7898
authors.initials: TJ
authors.lastname: Pollard
authors.forename: Mauro
authors.identifier: http://orcid.org/0000-0002-1470-6966
authors.initials: M
authors.lastname: Santos
authors.forename: David J
authors.identifier: http://orcid.org/0000-0003-4855-4283
authors.initials: DJ
authors.lastname: Stone
authors.forename: Andrew
authors.identifier: http://orcid.org/0000-0003-0973-5639
authors.initials: A
authors.lastname: Zimolzak
keywords: big data; knowledge creation; machine learning; open data; unreliable research