run counterfactuals, interrogate and investigate much more quickly, and go beyond situations where they already know the answer. This ability to make predictions and quickly assess results is at the core of a learning health system. Clinicians and researchers can predict an outcome, observe what happens, compare it against experience, and adjust future care protocols in response. And this can all happen rapidly.

With the mathematical methods in place, McCall noted, the priorities for big data analytics and evidence generation are shifting. Since mathematics can be scaled to any level and performed on any data set, the challenge now is finding data sources that are comprehensive and up to date. She underscored the need to link and share data from a variety of sources, such as pharmaceutical companies, hospitals, pharmaceutical benefit managers and payers. With data coming from several sources, there is also the need to understand context, and metadata take on an added importance.


Pearl, J. 2009. Causality: Models, reasoning and inference. New York: Cambridge University Press.

Phillips, C. V. 2003. Quantifying and reporting uncertainty from systematic errors. Epidemiology 14(4):459-466.

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