Lower riskClearer operations

In development project proof

FlapTrack

Catch a failing surgical flap early — clinical-grade monitoring built to NHS standards from the ground up.

Read it like a buyer.

  1. 01Before
  2. 02System
  3. 03Proof
  4. 04Stage

Before the build

When early signs of a failing flap get missed, surgeries fail. Nurses watch the bedside with paper charts and gut feel, and there's no reliable way to get the surgeon back in time. That's a broken system, not short staffing.

System shipped

Catch a failing surgical flap early — clinical-grade monitoring built to NHS standards from the ground up.

What changed

A clinical-grade foundation for catching failing flaps before they're lost. The hard engineering is done — patient records, safety rules, and the path to NHS approval — and it's ready for a hospital pilot.

Workflow modules

Lower riskClearer operations

FlapTrack

Patient Records Built to NHS Standards

Workflow: Patient Records Built to NHS Standards

Before: Clinical software built on a sloppy records structure can't talk to NHS systems and won't pass review. Getting this right first is the hardest, most important call — fix it later and you rebuild everything.

Outcome: A records foundation that fits NHS systems and holds up under review — built once, correctly, before any screens. Everything else in FlapTrack is built on top of it.

System: Patient records covering the whole flap-surgery journey, built to the standard the NHS uses. Safety checks live inside the records themselves, so bad data can't sneak in. Each hospital only sees its own patients — that separation is locked in by the structure, not left to app code to remember.

Proof: Patient records structured the way the NHS expects — so the system can plug into NHS tools later.

See the module
Lower riskClearer operations

FlapTrack

Automatic Red/Amber/Green Safety Alerts

Workflow: Automatic Red/Amber/Green Safety Alerts

Before: Deciding whether to call the surgeon now or wait can't hang on gut feel — that's inconsistent and slow. A safety call this important needs clear rules that always fire the same way and can't be talked out of.

Outcome: Safety alerts that fire the same way every time, leave a clear trail, and can be reviewed by a Clinical Safety Officer without reading any code.

System: Reads the bedside observations and gives a Red, Amber, or Green result using fixed safety rules — no guessing, no AI making it up. The same readings always give the same answer, and every alert scenario has a test proving it still works. The rules run on the server, so the result can't be faked or skipped from the app.

Proof: Automatic safety alerts that flag a worsening flap to the surgeon — built-in rules that can't be skipped.

See it in the full system
Lower riskClearer operations

FlapTrack

Standard NHS Clinical Codes

Workflow: Standard NHS Clinical Codes

Before: Clinical software that records conditions as free text can't connect to NHS systems or be searched properly, and it fails review. The codes have to go in while the records are designed, not bolted on later.

Outcome: Conditions are recorded in standard NHS codes, not loose text — so connecting to NHS systems and passing a safety review is a clear next step, not a rewrite.

System: Records flap-monitoring conditions using 157 codes from SNOMED CT, the standard NHS clinical terminology. Each code is tracked back to where it came from. They're candidate codes — unverified, awaiting Clinical Safety Officer sign-off before any clinical use — and the trail makes that review easy to follow.

Proof: 157 candidate clinical codes in the standard NHS terminology — each tracked and ready for safety review.

See it in the full system
Lower riskClearer operations

FlapTrack

Clinical Homework & NHS Approval Plan

Workflow: Clinical Homework & NHS Approval Plan

Before: Health software that doesn't understand the real clinical work, or have a plan for NHS approval, fails at the first review. That groundwork has to come before any product code.

Outcome: The clinical and approval groundwork is written down and checkable: it reflects real surgical practice, and the route to NHS approval is clear to reviewers, investors, and clinical partners.

System: Built on how 20 flap types are actually monitored across 3 specialties, grounded in 5+ peer-reviewed papers and NHS guidance, with 24+ rival systems studied. A plan maps out every NHS approval the product will need and where it stands on each — none earned yet, but the path is written down, not guessed.

Proof: Modelled on 20 flap types across 3 specialties, backed by real research and a clear plan for every NHS approval.

See it in the full system

Proof it is real

Schema tables
33
Enum types
42
Stored functions
16
Triggers
33
Per-hospital access rules
58
Tables locked to each hospital
27
Candidate clinical codes (unverified, pending safety sign-off)
157
Distinct flap types modelled
20
Specialties covered
3
Peer-reviewed papers and NHS protocols grounding the domain model
5+
Systems in competitor analysis
24+

Systems this proves I can build

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