  Advances in large language models (LLMs) provide new opportunities in
healthcare for improved patient care, clinical decision-making, and enhancement
of physician and administrator workflows. However, the potential of these
models importantly depends on their ability to generalize effectively across
clinical environments and populations, a challenge often underestimated in
early development. To better understand reasons for these challenges and inform
mitigation approaches, we evaluated ClinicLLM, an LLM trained on [HOSPITAL]'s
clinical notes, analyzing its performance on 30-day all-cause readmission
prediction focusing on variability across hospitals and patient
characteristics. We found poorer generalization particularly in hospitals with
fewer samples, among patients with government and unspecified insurance, the
elderly, and those with high comorbidities. To understand reasons for lack of
generalization, we investigated sample sizes for fine-tuning, note content
(number of words per note), patient characteristics (comorbidity level, age,
insurance type, borough), and health system aspects (hospital, all-cause 30-day
readmission, and mortality rates). We used descriptive statistics and
supervised classification to identify features. We found that, along with
sample size, patient age, number of comorbidities, and the number of words in
notes are all important factors related to generalization. Finally, we compared
local fine-tuning (hospital specific), instance-based augmented fine-tuning and
cluster-based fine-tuning for improving generalization. Among these, local
fine-tuning proved most effective, increasing AUC by 0.25% to 11.74% (most
helpful in settings with limited data). Overall, this study provides new
insights for enhancing the deployment of large language models in the
societally important domain of healthcare, and improving their performance for
broader populations.
