The Potential Impact of Artificial Intelligence on Healthcare Spending / Nikhil Sahni, George Stein, Rodney Zemmel, David M. Cutler.

Format:

Book

Author/Creator:

Sahni, Nikhil.

Contributor:

National Bureau of Economic Research.

Stein, George.

Zemmel, Rodney.

Cutler, David M.

Series:

Working Paper Series (National Bureau of Economic Research) no. w30857.

NBER working paper series no. w30857

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2023.

Summary:

The potential of artificial intelligence (AI) to simplify existing healthcare processes and create new, more efficient ones is a major topic of discussion in the industry. Yet healthcare lags other industries in AI adoption. In this paper, we estimate that wider adoption of AI could lead to savings of 5 to 10 percent in US healthcare spending--roughly $200 billion to $360 billion annually in 2019 dollars. These estimates are based on specific AI-enabled use cases that employ today's technologies, are attainable within the next five years, and would not sacrifice quality or access. These opportunities could also lead to nonfinancial benefits such as improved healthcare quality, increased access, better patient experience, and greater clinician satisfaction. We further present case studies and discuss how to overcome the challenges to AI deployments. We conclude with a review of recent market trends that may shift the AI adoption trajectory toward a more rapid pace.

Notes:

Print version record

January 2023.