  Large language models (LLMs) like ChatGPT have excited scientists across
fields; in medicine, one source of excitement is the potential applications of
LLMs trained on electronic health record (EHR) data. But there are tough
questions we must first answer if health care institutions are interested in
having LLMs trained on their own data; should they train an LLM from scratch or
fine-tune it from an open-source model? For healthcare institutions with a
predefined budget, what are the biggest LLMs they can afford? In this study, we
take steps towards answering these questions with an analysis on dataset sizes,
model sizes, and costs for LLM training using EHR data. This analysis provides
a framework for thinking about these questions in terms of data scale, compute
scale, and training budgets.
