Approved knowledge, in natural language
Lumen allows clinicians to ask questions about approved institutional protocols, procedures, diagnostic criteria, and reporting standards — and get an answer grounded in a visible, approved source.
Grounded answers, not generated policy
The system retrieves and explains approved clinical knowledge. It distinguishes facility-specific criteria from system defaults and keeps the approved source material visible — it never replaces clinical judgment.
Synthetic examples · not real patient data
“What criteria are we using for a >70% internal carotid artery stenosis?”
“What's our protocol for a renal artery study in a patient with a prior stent?”
“What's our protocol for contrast-enhanced carotid ultrasound?”
Where the knowledge comes from
The difference between this and a general-purpose chatbot is not the model. It's that every answer is traceable to a document your facility approved, and that the document is the thing you control.
Your documents
The corpus is material your facility has reviewed and approved. Nothing is pre-loaded from the open internet.
Versioned
“Vascular Lab Criteria, v3” is a real version, not a label. Approve v4 and every answer moves with it.
Yours overrides ours
Where a facility document and a system default disagree, the facility wins — and the answer says which one applied.
Documentation, not patients
The knowledge layer retrieves from approved documents. It is not connected to the patient record: no exam values, measurements, or images are part of what it reads.
“AI should make clinical knowledge easier to access. It should never quietly become the authority.”
Ask Lumen a real facility protocol question
Bring one of your own facility's approved protocols to a demo and see how it's retrieved.