# Medical AI (Docucoder)

URL: https://qualixsolutions.com/case-studies/docucoder/

We are the eyes and ears of the operating room

Docucoder captures the procedure in real time and delivers a ready to review, CPT coded surgical report by the end of the case. No paperwork relay, no lost detail.

- Industry: Healthcare
- Type: AI SaaS
- Timeline: 90 Days
- Stack: React, Material UI, Vite, Next.js, Node.js, Express, MongoDB, WebSockets, FastAPI, Deepgram, LangChain, OpenAI, Claude, AWS

docuCODER takes time to bill from an industry average of 3 to 7 days down to zero. Surgical documentation is complete the moment the procedure ends.

- 10 to 15% — cardiology denial rate
- 5 to 8% — revenue lost to docs
- $0 — hardware required
- Days — to go live, in parallel

#### The problem

Clinical coding isn't a documentation problem. It's a relay race problem.

The detail that sets the CPT code is generated by the physician mid procedure, but captured and coded later, by different people. Every handoff drops detail, and in interventional coding a dropped detail (laterality, device, anatomical site, supply) is the difference between the correct code and a rejected bill. On top of that sits the cognitive load of holding the procedure in memory while documenting it, then carrying that same mental tax into coding the bill.

- Real dollars lost per procedure from undercoding
- Slower approvals from incomplete context
- Mental tax on three people reconstructing intent

Did you know?

- American hospitals lost **$68 billion** in 2022 from billing mistakes, wrong codes, rejected claims, and lost payments.
- Revenue leakage from coding errors can reach **10 to 15% of a practice's annual earnings.** For a $10M practice, that's **$1M to $1.5M lost every year**.
- Roughly **80% of U.S. medical bills** contain at least one error, whether an incorrect code, a duplicate charge, or missing information.

#### The design

The decision that shaped everything: capture and code at the point of care

The physician does the one thing they already do, which is talk through the procedure. While they talk, the case resolves itself on screen. Procedures get recognised and matched to the hospital's own coding rules, missing billable elements surface as live flags, and supplies are scanned in on the spot. By the time the case ends, the coded, defensible record is essentially done.

#### Development

Inside the build - what the demo doesn't show

The relay collapses into one continuous session because there's no handoff to survive. It's built as a chain of agents working off the physician's voice, with a human scriber in the loop clearing flags.

1. Transcription: speech to timestamped text
2. Classifier: procedure vs chatter
3. Procedure match: against hospital rules
4. Gap detection: flags missing elements
5. Supply OCR: lot, mfr, recall scan
6. Human in loop: scriber clears flags

**The unforeseen requirement:**

- HIPAA surfaced mid build.
- PHI masking, tamper proof login, audit and change logs, retention rules for audio and transcripts.
- BAA contracts with every external API.

This reshaped the security posture, not just one feature.

**What we said no to**

- The client wanted multilingual processing early. We convinced him to ship English first and version it, prioritising accuracy on one path over breadth on many.

**The hardest thing to validate (QA)**

- AI accuracy in two layers, transcription and live procedure generation per timestamp, plus a new guest user, websocket "rooms" model so multiple staff could speak into one recording session.

#### Delivery

What the client can now do

Walk out of the room with a coded, billable, defensible record instead of a pile of notes someone else has to decode days later. Output quality compounds. The first report versus the hundredth, as tenant specific onboarding carries the surgeon's voice into the final code.

- Early signal: Post launch demand expanded the product. A CPT coded report request from a coder, CPT 2026 cardiac regs, an AI next steps checklist, supply recall vision alerts, and charge sheet generation. People asking for more is the trust signal.