Artificial intelligence for improved hospital efficiency and patient flow
To address unprecedented hospital bed and staffing shortages, Mayo Clinic has implemented several novel processes to ensure that people arriving in its emergency departments are matched with the best combination of inpatient and outpatient care. The lab team has been studying the use of artificial intelligence (AI) to optimize hospital resource distribution and facilitate timely emergency department disposition. Accordingly, the lab team has developed AI models and deployed them in practice.
Through this work, the lab team aims to ensure that people who come to a Mayo Clinic emergency department are matched with the best inpatient and outpatient resources for their needs as efficiently as possible. The lab team is assessing the impact of one AI tool in practice through a randomized controlled trial. The lab also is developing additional AI models to support operational efficiency.
Collaborating faculty
- Shant Ayanian, M.D.
- Daniel Chiang, M.D.
- Christopher A. Dinh, M.D.
- Sagar Dugani, M.D., Ph.D.
- Heather A. Heaton, M.D., M.S.
- Derick D. Jones, M.D., M.B.A
- Riddhi S. Parikh, M.B.B.S.
- Ray Qian, M.D.
- Alexander J. Ryu, Ph.D.
Relevant publications
- Ryu AJ, Ayanian S, Quian R, Core MA, Heaton HA, Lamb MW, Parikh RS, Boyum JP, Garza EL, Condon JL, Peters SG. A clinician's guide to running custom machine-learning models in an electronic health record environment. Mayo Clinic Proceedings. 2023.
- Ryu AJ, Romero-Brufau S, Quian R, Heaton HA, Nestler DM, Ayanian S, Kingsley TC. Assessing the generalizability of a clinical machine learning model across multiple emergency departments. Mayo Clinic Proceedings. 2022.