Drug Approval in a Learning Health System image

Minnesota Law Review, Forthcoming, July 30, 2018
W. Nicholson Price (Academic Fellow Alumnus)


Read the Full Article

From the article:

The current system of FDA approval seems to make few happy. Some argue FDA approves drugs too slowly; others too quickly. Many agree that FDA—and the health system generally—should gather information after drugs are approved to learn how well they work and how safe they are. This is hard to do. FDA has its own surveillance systems, but those systems face substantial limitations in practical use. Drug companies can also conduct their own studies, but have little incentive to do so, and often fail to fulfil study commitments made to FDA. Proposals to improve this dynamic often suggest gathering more information after approval in various ways and incorporating that information into FDA’s decision-making process, making the information/access tradeoff more nuanced than a sharp binary at approval. The drug approval regime has already begun to move in this direction.

In this Essay I describe parallels between this move and the move to a learning health system more broadly, which blurs the line between health care and health research. A learning health system creates opportunities for broad postapproval studies, both observational and interventional, with lower costs and more systematic applicability than traditional mechanisms. FDA itself has recognized some of these possibilities, especially in its actions to consider uses of real-world evidence as mandated by the 21st Century Cures Act.

Read full article here!

big data fda innovation regulation research w. nicholson price ii