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

arXiv: https://arxiv.org/abs/2402.03597 · HTML: https://mlsanity.com/paper/2402.03597/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [LLMs Accelerate Annotation for Medical Information Extraction](https://mlsanity.com/paper/2312.02296/) (2023)
- [The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs](https://mlsanity.com/paper/2303.12961/) (2023)
- [Developing a general-purpose clinical language inference model from a large corpus of clinical notes](https://mlsanity.com/paper/2210.06566/) (2022)

## Built on by (most cited first, 4 of 4)

- [The Data Addition Dilemma](https://mlsanity.com/paper/2408.04154/) (2024)
- [A Collection of Innovations in Medical AI for patient records in 2024](https://mlsanity.com/paper/2503.05768/) (2025)
- [The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)](https://mlsanity.com/paper/2403.02558/) (2024)
- [Revealing Treatment Non-Adherence Bias in Clinical Machine Learning Using Large Language Models](https://mlsanity.com/paper/2502.19625/) (2025)

## Abstract (arXiv)

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.