Abstract Large language model (LLM) chat tools have the potential to transform healthcare workflows by improving efficiency and reducing administrative burdens. While prior research has predominantly focused on clinicians, non-clinician healthcare staff constitute the majority of the workforce, and their real-world chat tool use remains uncharacterized. This retrospective, cross-sectional study analyzed de-identified chat logs from a secure, HIPAA-compliant LLM chat tool deployed at an academic