SepsisGuard AI, early warning nurses trust
Spots patients going downhill with sepsis early, without burying nurses in false alarms the way most of these systems do.
Sepsis kills more people in American hospitals than anything else. Several of the warning systems hospitals already bought are quietly failing, because they cry wolf so often that staff stop listening.
This one is built around that problem. Fewer alerts, ones you can trust, early enough to do something about.
The problem
- Sepsis affects about 1.7 million adults in the US every year and plays a part in more than 350,000 deaths. That is more than breast cancer and prostate cancer together
- Some widely used warning systems have been independently tested and found to catch only a fraction of real cases, while firing dozens of false alarms for every true one
- Nurses learn to ignore them. That is a rational response to a tool that is wrong most of the time, and it makes the ward less safe than having no alert at all
- Every hour of delay in treating sepsis raises the risk of death
How it works
It watches the things that actually change when someone is going downhill: heart rate, breathing, blood pressure, temperature, oxygen, blood test results, and how much urine they are passing.
It is tuned deliberately toward fewer, better alerts rather than catching everything. A system nurses trust and act on beats one that is technically more sensitive and universally ignored.
Every alert says what triggered it and which numbers moved, so the nurse can check the reasoning rather than take it on faith. The nurse confirms or dismisses it, and that feedback improves the system.
It never orders anything or changes treatment. It raises a hand.
What matters when you install it
- Tune it on your own patients. A model trained at another hospital will not behave the same way on your patients
- Track how many alerts turn out to be real, every month, and show the number to the nurses. Trust is the whole product
- Agree what happens when it fires before you switch it on. An alert with no agreed response is just noise
- Keep every alert and outcome, so you can prove it is helping and spot when it stops
Built with live monitoring feeds, Claude for the written explanations, and connections into hospital record systems.
Interested in building something like this?
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