“Human-in-the-Loop” Thematic Analysis: Using R to Apply Current Recommendations for Incorporating LLMs into Qualitative Research
Do you want to explore current research recommendations in the literature on incorporating LLMs into qualitative/mixed methods data analysis, or are you interested in implementing LLMs into your qualitative/mixed methods workflow? If so, this short course is designed for you!
In this course we will examine current recommendations from the literature regarding integrating AI (specifically LLMs) into the process of qualitative coding. Acknowledging that a wide range of qualitative approaches exist, all examples and recommendations in this course will be based on the 6 Phase Thematic Analysis Framework proposed by Braun & Clarke 2006 to simplify the course scope.
No perquisites in qualitative methods or coding are required for this course – just bring your computer & your curiosity!
This session provides an overview of some of the upcoming changes to the National Institutes of Health (NIH) grant application requirements, including new Common Forms that will be required for due dates on or after January 25, 2026.
In particular, this presentation will focus on the Biosketch and Current and Pending (Other) Support forms, which will need to be completed using the SciENcv tool. It will include demonstrations of SciENcv as well as ORCID iD creation and linking steps. The presentation aims to prepare researchers for the new NIH requirements by providing detailed instructions and resources to ensure compliance.
This program is offered via Zoom by the Health Sciences Library and led by Katherine Howell, MSLIS.
Healthcare AI
Join the TraCS Data Science Lab as we talk about the experience of implementing healthcare AI. Healthcare AI is generating unprecedented investment and regulatory momentum — yet the vast majority of AI pilots never reach frontline care.
This seminar cuts through the hype to examine why, tracing the full clinical AI deployment pipeline from model development through post-deployment monitoring and the overlooked governance, workflow, and implementation challenges that derail efforts. Using real-world case studies — including the widely-deployed Epic Sepsis Model — we'll explore how dataset shift, alert fatigue, and clinician behavior consistently outweigh algorithmic performance as barriers to impact.
Whether you're a clinician, informaticist, or health system leader, this talk will reframe how you think about AI in medicine: the future won't be decided by better models, but by better implementation.
Speaker:
David Friedlander, MD, MPH
Assistant Professor, Department of Urology
TDSL Faculty Scholar, TraCS Data Science Lab
UNC School of Medicine
Seminars in the NC TraCS Data Science Lab Seminar Series cover a range of topics related to health care data science, clinical data, data engineering, and working in these areas at UNC-Chapel Hill. These hybrid seminars are usually held monthly on the third Tuesday of each month from 12:30-1:30 p.m. in the NC TraCS suite on the 2nd floor of Brinkhous-Bullitt or via Zoom.