Risk Prediction Tools

The Need for Greater Transparency

Published in: Anesthesiology, Volume 128, pages 244-246 (February 2018). doi: 10.1097/ALN.0000000000002021

Posted on RAND.org on January 15, 2021

by Laurent G. Glance, Andrew W. Dick, Turner M. Osler

Read More

Access further information on this document at Anesthesiology

This article was published outside of RAND. The full text of the article can be found at the link above.

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. 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.

Research conducted by

This report is part of the RAND Corporation external publication series. Many RAND studies are published in peer-reviewed scholarly journals, as chapters in commercial books, or as documents published by other organizations.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.