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

TL;DR

This study demonstrates that GPT-4 can effectively extract reasons for contraceptive switching from clinical notes, outperforming baseline models and revealing key factors influencing patient decisions across demographics.

Contribution

We show that GPT-4's zero-shot capabilities can accurately identify reasons for contraceptive switching from unstructured clinical notes, surpassing traditional models and enabling demographic-specific insights.

Findings

GPT-4 achieved microF1 scores of 0.849 and 0.881 for start and stop reasons.

Human evaluation showed 91.4% accuracy with minimal hallucinations.

Identified key reasons such as patient preference, adverse events, and insurance.

Abstract

Prescription contraceptives play a critical role in supporting women's reproductive health. With nearly 50 million women in the United States using contraceptives, understanding the factors that drive contraceptives selection and switching is of significant interest. However, many factors related to medication switching are often only captured in unstructured clinical notes and can be difficult to extract. Here, we evaluate the zero-shot abilities of a recently developed large language model, GPT-4 (via HIPAA-compliant Microsoft Azure API), to identify reasons for switching between classes of contraceptives from the UCSF Information Commons clinical notes dataset. We demonstrate that GPT-4 can accurately extract reasons for contraceptive switching, outperforming baseline BERT-based models with microF1 scores of 0.849 and 0.881 for contraceptive start and stop extraction, respectively. Human evaluation of GPT-4-extracted reasons for switching showed 91.4% accuracy, with minimal hallucinations. Using extracted reasons, we identified patient preference, adverse events, and insurance as key reasons for switching using unsupervised topic modeling approaches. Notably, we also showed using our approach that "weight gain/mood change" and "insurance coverage" are disproportionately found as reasons for contraceptive switching in specific demographic populations. Our code and supplemental data are available at https://github.com/BMiao10/contraceptive-switching.