Editorial Views  |   February 2018
Risk Prediction Tools: The Need for Greater Transparency
Author Notes
  • From the Departments of Anesthesiology and Public Health Sciences, University of Rochester School of Medicine, Rochester, New York (L.G.G.); RAND Health, RAND, Boston, Massachusetts (L.G.G., A.W.D.); and the Department of Surgery, University of Vermont, Burlington, Vermont (T.M.O.).
  • Corresponding articles on pages 247 and 283.
    Corresponding articles on pages 247 and 283.×
  • Accepted for publication August 10, 2017.
    Accepted for publication August 10, 2017.×
  • Address correspondence to Dr. Glance: laurent_glance@urmc.rochester.edu
Article Information
Editorial Views / Cardiovascular Anesthesia / Gastrointestinal and Hepatic Systems / Patient Safety
Editorial Views   |   February 2018
Risk Prediction Tools: The Need for Greater Transparency
Anesthesiology 2 2018, Vol.128, 244-246. doi:10.1097/ALN.0000000000002021
Anesthesiology 2 2018, Vol.128, 244-246. doi:10.1097/ALN.0000000000002021
LAST year Amazon captured 40% of online sales partly as a result of accurate personalized predictions designed to help consumers discover what they want using a machine learning algorithm.1,2  In health care, we aspire to the same goal: accurate personalized predictions, although in the healthcare arena we are interested in predicting outcomes of medical consequence to our patients rather than their next consumer whim. This revolution in predictive power has changed the nature of online buying, and now seems poised to transform medical practice. Our challenge is to ensure that this transition to personalized medicine is safely implemented. If risk prediction tools are to be used in clinical care, it is essential that they be vetted with the same care as any new drug or medical device.
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