Background: The public launch of OpenAI's ChatGPT platform generated immediate interest in the use of large language models (LLMs). Health care institutions are now grappling with establishing policies and guidelines for the use of these technologies, yet little is known about how health care providers view LLMs in medical settings. Moreover, there are no studies assessing how pediatric providers are adopting these readily accessible tools.
Objective: The aim of this study was to determine how pediatric providers are currently using LLMs in their work as well as their interest in using a Health Insurance Portability and Accountability Act (HIPAA)-compliant version of ChatGPT in the future.
Methods: A survey instrument consisting of structured and unstructured questions was iteratively developed by a team of informaticians from various pediatric specialties. The survey was sent via Research Electronic Data Capture (REDCap) to all Boston Children's Hospital pediatric providers. Participation was voluntary and uncompensated, and all survey responses were anonymous.
Results: Surveys were completed by 390 pediatric providers. Approximately 50% (197/390) of respondents had used an LLM; of these, almost 75% (142/197) were already using an LLM for nonclinical work and 27% (52/195) for clinical work. Providers detailed the various ways they are currently using an LLM in their clinical and nonclinical work. Only 29% (n=105) of 362 respondents indicated that ChatGPT should be used for patient care in its present state; however, 73.8% (273/368) reported they would use a HIPAA-compliant version of ChatGPT if one were available. Providers' proposed future uses of LLMs in health care are described.
Conclusions: Despite significant concerns and barriers to LLM use in health care, pediatric providers are already using LLMs at work. This study will give policy makers needed information about how providers are using LLMs clinically.
Keywords: AI; ChatGPT; LLM; OpenAI; artificial intelligence; chatbot; digital tools; large language model; machine learning; medical informatics applications; pediatric; surveys and questionnaires.
©Susannah Kisvarday, Adam Yan, Julia Yarahuan, Daniel J Kats, Mondira Ray, Eugene Kim, Peter Hong, Jacob Spector, Jonathan Bickel, Chase Parsons, Naveed Rabbani, Jonathan D Hron. Originally published in JMIR Formative Research (https://formative.jmir.org), 12.09.2024.