James Mitchell, PhD
Assistant Professor at the University of Colorado Anschutz. Focusing on Human-Computer Interaction, Clinical Decision Support, and Artificial Intelligence.
About Me
I am an Assistant Professor in the Department of Biomedical Informatics at the University of Colorado Anschutz. My research sits at the intersection of human-computer interaction, clinical decision support, and artificial intelligence, designing user-centred systems that improve how clinicians access and act on information at the point of care.
I completed my PhD at Keele University, working in partnership with University Hospital North Midlands NHS Trust as part of a funded project to develop bedside mobile clinical guidelines.
Current projects include LLM-based feedback systems for clinical environments, privacy-preserving NLP for clinical acronym disambiguation, drug-drug interaction decision support, and wearable-based anxiety detection. I collaborate with Stanford, University of Utah, Vanderbilt, Keele University, and more.
Research Projects
ClarifAI
An LLM-based system for collecting structured AI feedback in hard-to-reach clinical environments, enabling continuous improvement of decision-support tools at the point of care.
Learn more →PLACID
A privacy-preserving LLM framework for inferring and disambiguating clinical acronyms at scale, improving EHR readability and reducing documentation ambiguity.
Learn more →Bedside Clinical Guidelines
A user-centred mobile application delivering evidence-based clinical guidelines at the point of care, developed in close collaboration with University Hospital North Midlands NHS Trust.
Learn more →Selected Publications
PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation
Aithal, Kotz & Mitchell (2026), arXiv; accepted poster, AMIA Symposium 2026
Navigating Complexity: Enhancing Pediatric Diagnostics With Large Language Models
Mitchell & Bennett (2024), Pediatric Critical Care Medicine, 25(6):577–580
A Rapid Review on Current and Potential Uses of Large Language Models in Nursing
Hobensack et al. (2024), International Journal of Nursing Studies, 154, 104753
Artificial Intelligence-Based Technologies in Nursing: A Scoping Literature Review of the Evidence
von Gerich et al. (2022), International Journal of Nursing Studies