Computational Precision Health

by on

Identifying Reasons for Contraceptive Switching from Real-World Data Using Large Language Models

Flow chart illustrating the study overview: to assess the ability for GPT4 to extract contraceptive switching values from clinical notes, and to identify key reasons for switching using unsupervised clustering methods.

Many factors related to medication switching are only captured in unstructured clinical notes and can be difficult to extract. Dr. Irene Chen and colleagues evaluated the abilities of GPT-4, with no previous training, to identify reasons women changed contraceptives, demonstrating that GPT-4 could accurately extract reasons for switching.