About Glia
In the nervous system, glial cells don't fire signals themselves — they support, nourish, and protect the neurons that do. That's the idea behind this site. Healthcare has no shortage of brilliant clinicians, researchers, and technologists. What it often lacks is the connective tissue: clear thinking about how data, AI, and interoperability can be made to actually work in practice. Glia exists to provide some of that support — helping the neurons of medicine do their best.
Why I write here
Healthcare AI and data are at an inflection point. The tools are genuinely powerful. The standards are maturing. And the gap between what's technically possible and what gets implemented well — safely, equitably, and usefully — remains frustratingly wide.
I write here because I've spent my career sitting at that gap, on both sides of it. I want clinicians to understand what AI can and can't do in their workflow. I want technologists to understand what clinicians actually need. And I want administrators and policy people to understand what it takes to get data right before anything else can work. There's no shortage of hype in this space. I'd rather offer something more useful.
Background
After training as a physician, I exchanged my stethoscope for a computer keyboard and earned a PhD in biomedical informatics focused on health AI (PDF link) — before it became a buzzword. That combination of clinical training and deep technical expertise in AI and data has shaped everything I've done since.
I'm a faculty member in Internal Medicine (Epidemiology) at the University of Utah School of Medicine and an adjunct faculty member in Biomedical Informatics. I conduct healthcare AI research across both the University of Utah Health and the Salt Lake City VA Medical Center. I write this blog in my personal capacity.
My research lies at the intersection of artificial intelligence, data standardization, and answering complex healthcare questions in infectious diseases, epidemiology, oncology, mental health, and other clinical specialties. My current research involves the use of AI for healthcare natural language processing (NLP).
Before returning to academia, I spent over 15 years in industry building healthcare AI and data infrastructure at scale. At 3M Healthcare (now called Solventum), I worked on clinical terminology, data standardization, and NLP — the invisible but essential foundation that makes AI in healthcare possible. At Truveta, I worked on data standardization efforts using human-in-the-loop AI to harmonize real-world clinical data across health systems.
Most recently, I led Nightingale Open Science incubated at the University of Chicago, where I focused on data acquisition, fundraising, and multimodal AI research using images, waveforms, and videos to investigate what clinical AI can learn from diverse data types.
Along the way, I served as an elected U.S. representative on the SNOMED International Technical Committee and advised a U.S. Congressional Committee on improving interoperability between VA and DoD medical records. I'm a Fellow of the American Medical Informatics Association (FAMIA).
What I write about
This site is for clinicians, technologists, and healthcare administrators who want to think carefully about AI, data quality, interoperability, and patient safety — without the hype and without oversimplification. I also consult with and advise healthcare organizations and technology companies on these topics. The articles here reflect the questions I get asked most, and the ones I think matter most.
Get in touch
If something here is useful to you, I'd like to know. If you have questions, disagreements, or something you'd like me to write about, I'd like to know that too. You can reach me through the contact form, or find more about me at:
LinkedIn | X / Twitter | ORCID | Google Scholar | University of Utah faculty page
Senthil K. Nachimuthu, MD, PhD, FAMIA.