TY  - JOUR
AU  - Ehrett, Carl
AU  - Hegde, Sudeep
AU  - Andre, Kwame
AU  - Liu, Dixizi
AU  - Wilson, Timothy
PY  - 2024
DA  - 2024/11/19
TI  - Leveraging Open-Source Large Language Models for Data Augmentation in Hospital Staff Surveys: Mixed Methods Study
JO  - JMIR Med Educ
SP  - e51433
VL  - 10
KW  - data augmentation
KW  - large language models
KW  - medical education
KW  - natural language processing
KW  - data security
KW  - ethics
KW  - AI
KW  - artificial intelligence
KW  - data privacy
KW  - medical staff
AB  - Background: Generative large language models (LLMs) have the potential to revolutionize medical education by generating tailored learning materials, enhancing teaching efficiency, and improving learner engagement. However, the application of LLMs in health care settings, particularly for augmenting small datasets in text classification tasks, remains underexplored, particularly for cost- and privacy-conscious applications that do not permit the use of third-party services such as OpenAI’s ChatGPT. Objective: This study aims to explore the use of open-source LLMs, such as Large Language Model Meta AI (LLaMA) and Alpaca models, for data augmentation in a specific text classification task related to hospital staff surveys. Methods: The surveys were designed to elicit narratives of everyday adaptation by frontline radiology staff during the initial phase of the COVID-19 pandemic. A 2-step process of data augmentation and text classification was conducted. The study generated synthetic data similar to the survey reports using 4 generative LLMs for data augmentation. A different set of 3 classifier LLMs was then used to classify the augmented text for thematic categories. The study evaluated performance on the classification task. Results: The overall best-performing combination of LLMs, temperature, classifier, and number of synthetic data cases is via augmentation with LLaMA 7B at temperature 0.7 with 100 augments, using Robustly Optimized BERT Pretraining Approach (RoBERTa) for the classification task, achieving an average area under the receiver operating characteristic (AUC) curve of 0.87 (SD 0.02; ie, 1 SD). The results demonstrate that open-source LLMs can enhance text classifiers’ performance for small datasets in health care contexts, providing promising pathways for improving medical education processes and patient care practices. Conclusions: The study demonstrates the value of data augmentation with open-source LLMs, highlights the importance of privacy and ethical considerations when using LLMs, and suggests future directions for research in this field. 
SN  - 2369-3762
UR  - https://mededu.jmir.org/2024/1/e51433
UR  - https://doi.org/10.2196/51433
DO  - 10.2196/51433
ID  - info:doi/10.2196/51433
ER  -