AI is writing your medical records now. It is fast, tireless, and nothing independent is checking a single word. When a note is wrong, the liability lands on you, not the software. Five x Five takes its name from the radio call for a signal received perfectly: loud and clear. That is exactly what it brings to the exam room. The moment care happens, it captures its own record of what was said and cryptographically seals it, time-stamped and tamper-proof. The black box for the exam room. So the day a record is ever questioned, you don't have to be believed. You can prove it.
A clinician reaches for an AI scribe because the documentation burden is crushing, and it helps. The quiet assumption underneath is that they will read every line it writes. But between a full panel, constant interruptions, and burnout, that careful read is the first thing the day takes away. The note enters the chart, and no one is told it can be checked. This is not a lapse in care. It is a gap in the system, and it is the gap Five x Five closes.
We do not replace your scribe, and we never need its transcript. Five x Five captures its own record at the moment of care and seals it. Any scribe's note is then held up to that sealed truth.
As care happens, the encounter becomes a structured, time-stamped record. One tap seals it with a cryptographic hash computed from its exact contents.
The scribe's note is checked against the sealed source. Every line gets one of three verdicts. No fourth answer, and nothing to take on faith.
Caught before sign-off. The note records that the patient denies self-harm. The sealed source shows they reported it. That single reversal is the error that follow-up care is built on, and it never reaches the chart unflagged.
The clinician, the institution, and the patient are protected by the same record, sealed once. Tap to see both sides of it.
The independent check runs as care happens, so no one has to review every line. If a note is ever questioned, you hold a sealed record that already flagged it. A complete, audit-ready trail, and early signal before a problem becomes a settlement.
You hold a verifiable copy and can confirm it yourself, without taking the vendor's word for it. A parent can hold a verified copy of a child's record, the exact thing the new provincial law is being written to protect.
Here is the record and its seal. An authorized holder can recompute the hash and confirm nothing was altered. Change one character and watch the seal break. This runs live, right now, in your browser.
In every pro sport, a disputed call goes to the booth. There is a camera, there is a tape, and the replay settles it. In medicine right now, the call gets made with no camera, and then the tape gets deleted. The problem is not that AI scribes make errors. Every source below proves they do. The problem is that no one can prove what the record got wrong, because the source is already gone. We do not sell accuracy. We sell proof.
K.M. Hogan trained at Waterford Hospital, completing a clinical practicum run by the Department of Ethics, with work in inpatient psychiatry. She has worked in EAP and crisis counselling. She was trained to chart accurately and to spot the gaps: the moments when what was said and what was written diverge. That training is the foundation of this product.
Five x Five was born when her own child was cleared for surgery on the strength of an AI scribe's note. The note was wrong. She caught it by reading the chart herself. The gap between a clinical record no one checks and a liability no one has priced is the product.
She built GNOSIS on a simple premise: the right decision disappears the moment it is made, inside the rooms that later have to answer for it. Five x Five is the record that refuses to let that happen silently.
Builds the surfaces and systems. Computer Engineering degree from Memorial University of Newfoundland, Master of Technology Management in progress. AWS Certified Cloud Practitioner. Before GNOSIS, software development at the National Research Council of Canada.
Keeps the record honest. Master of Science in Data Science in progress at Memorial University of Newfoundland. Data quality at Siftmed. Her job is making sure every detail holds up under review.
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