National Academy of Medicine

Making the Case for Continuous Learning from Routinely Collected Data

By Sally Okun, Deven McGraw, Paul Stang, Eric Larson, Donald Goldmann, Joel Kupersmith, Rosemarie Filart, Rose Marie Robertson, Claudia Grossmann, Michael Murray
April 15, 2013 | Discussion Paper

In “Making the Case for Continuous Learning from Routinely Collected Data,” the authors suggest that in order to achieve better health, patients and clinicians will need to view every health care encounter as providing an opportunity to improve outcomes. The paper cites widely reported examples of routinely collected digital health data being applied to improve services, inform patients, avoid harm, and speed research. Developed by individual participants from the IOM’s Clinical Effectiveness Research Innovation Collaborative, it asserts that patients and the public are the most effective advocates for resetting expectations that their data be used to advance knowledge and support continuous learning. Citing examples of efforts to engage patients and clinicians in continuous learning efforts, the authors see broader application of these approaches as critical to ensuring the success of a learning health system in achieving better care, lower costs and improved health.


Disclaimer: The views expressed in this paper are those of the authors and not necessarily of the authors’ organizations, the National Academy of Medicine (NAM), or the National Academies of Sciences, Engineering, and Medicine (the National Academies). The paper is intended to help inform and stimulate discussion. It is not a report of the NAM or the National Academies. Copyright by the National Academy of Sciences. All rights reserved.