Large language models for health record data extraction and summarization
A large proportion of critical information in electronic health records is in the form of text. Reviewing this text in anticipation of providing clinical care can be very time-consuming.
In this work, we explore methods for incorporating large language models into clinical workflows to improve the efficiency and effectiveness of chart review. Through this work, we seek to equip clinicians with tools that enable thorough and efficient chart review, improve decision-making and enable clinicians to spend more time with the people in their care.
Collaborating faculty
- Ben J. Hinton, Ph.D.
- Tanner S. Hunt
- Kevin J. Peterson, Ph.D., M.S.
- Mindy P. Rice, M.B.A
- Alexander J. Ryu, M.D.