LLM-Assisted Taxonomy and Temporal Analysis of Provider Questions About HIV in provider-to-provider telehealth

IntroductionOngoing education in HIV care is limited for many healthcare providers working in rural and non-academic settings, which can reduce patients access to high-quality care. To guide targeted tele-mentoring and continuing education, we analyzed questions submitted by clinicians during Extension for Community Healthcare Outcomes (ECHO) sessions to characterize learning needs and thematic trends. MethodsWe reviewed 78 clinical questions submitted during Project ECHO sessions and developed a structured classification of topics raised by clinicians. Using text-embedding representations and large language models (LLMs), we explored automated approaches to categorize questions and identify thematic clusters. Analyses compared the distribution of topics across professional roles to detect role-specific learning needs. ResultsDistinct topic patterns emerged by clinician type. Physicians and pharmacists most often asked about initiating and modifying antiretroviral therapy (ART). Nurse practitioners focused on ART and adherence support, while allied health professionals and PAs raised social-support and care-navigation issues. Medication-related questions frequently highlighted adherence concerns and ART change considerations. DiscussionECHO questions reveal clear, role-dependent learning needs that can inform targeted tele-mentoring. LLM-based embeddings provided a practical, scalable way to classify questions and monitor trends, supporting more tailored HIV training for different provider groups.