Ground the output
If a claim cannot be traced to source signal or the chart, it should not enter the record.
Andrew Napier, MD, FAAEM · San Ramon, California
About
Built in the space between clinical pressure and product reality.
Before medical school I served as an Army combat medic and deployed to Afghanistan. That still shapes how I think about medicine and product design: systems have to keep working after the clean demo ends.
I now practice emergency medicine in the East Bay while building clinical AI and airway tools. Staying close to real patients, departments, and physician cleanup work is part of the product method.
The throughline
Make the failure visible. Then design around it.
At Sayvant, I lead clinical AI for emergency and hospital medicine documentation. The work is less about making models sound smart and more about keeping them inside the record, preserving uncertainty, and making failures inspectable by physicians.
At IntuBlade, I built a single-use USB-C video laryngoscope around a different constraint: video airway management works, but cost, logistics, and device availability keep it out of too many ambulances.
Both efforts now have a public research trail—production documentation QA, ScribeBench, acute-care decision support, and video-linked airway telemetry—with deliberately bounded claims and reproducible evidence.
Field record
A working timeline
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Army combat medic
Led battalion-level medical operations and treated more than 300 documented casualties during a deployment to Afghanistan.
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Physician and department leader
Trained in emergency medicine, then led quality, throughput, documentation, and sepsis improvement work in a 70,000-visit department.
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Built IntuBlade
Turned an airway problem into patented, FDA-regulated Class I hardware used by 400+ EMS agencies across 41 states.
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Co-founded Sayvant
Leads clinical AI product and evaluation work for documentation systems used across 100+ care sites and more than 1.1M charts.
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Stanford clinical informatics
Completed the MCiM program with work in physician-grounded evaluation, acute-care decision support, and production AI quality.
Clinical AI
Fluency is not the same as reliability.
Preserve uncertainty
Clinicians need ambiguity represented honestly, not rounded into false confidence.
Design the review loop
Evaluation, escalation, and post-launch monitoring are product surfaces—not cleanup tasks.
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