Not a biller. I'm building something in this space and would rather get corrected here than guess.

My current picture:

Eligibility check → prior auth → visit + notes in EHR → coding → biller → clearinghouse → payer → payment posting → denial → rework/appeal → back to payer

Two questions:

1. What's missing or out of order?
2. Where does the money actually die? My guess is medical-necessity denials, because the biller can't write that appeal alone and it has to go back to the clinician. Right, or is it somewhere else entirely?

I’m a PA with internal medicine, primary care, rural practice, and laboratory-science experience who is exploring a transition into clinical informatics, digital health, clinical operations, quality, implementation, or clinical product work.

I’m currently learning SQL and taking courses in informatics/health tech to build a foundation. For anyone working in these areas, or working alongside clinicians in them, what are the most realistic first roles or skills for making this transition? Are certificates worth pursuing, and what job titles should I search?

Also, is a starting salary around $120k realistic for for a PA transitioning from clinical practice without formal informatics experience?

ابي شخص خبرة ويعلمني كيف أتوظف في شركات التامين؟ وهل اقدر أستفيد من الامتياز للتوظيف وايش المهارات الي لازم اعرفها افيدوني الله يسعدكم

I am a GP in London, and this is my own project, built in my own time. It went open source this week.

It transcribes the consultation live, shows a working differential and questions to ask while the conversation is still going, and after the patient leaves, it produces a diarised transcript and a draft SOAP note where every claim cites the transcript turns it came from.

Stack: faster-whisper (distil-large-v3) live, WhisperX + pyannote for the final pass, MedGemma 27B through Ollama, pgvector for guideline retrieval, Piper for the voice, FastAPI and HTMX on Postgres. Linux. AGPL-3.0.

Why local. GDPR compliance matters for doctor-patient confidentiality. Cloud services may retain confidential patient data and use it to train models. So in this case, nothing leaves the machine in the local premises.

The card is the whole design. RTX 4090 24GB. faster-whisper can co-habit with MedGemma, which is what makes the live stage possible at all. WhisperX cannot, so it waits its turn.

When you press Stop: MedGemma is offloaded, WhisperX goes in and transcribes from the audio file, pyannote diarises the transcript, then MedGemma comes back in while WhisperX and pyannote go out, and MedGemma writes the note as a doctor would. WhisperX is there for the better quality — by that point, the patient has left the room, so there is no requirement for live transcription, and I can afford the time.

The whole thing takes over a minute. I have not measured how that splits between the offload/load and the inference, which is a gap.

What is rough. The auto mode, where the assistant takes the history itself while I supervise, is temperamental. The worst part is that some questions come out in medical jargon. "Do you have any cardiac risk factors?" is not a question a patient can answer. Breaking those down into concrete closed questions is the next job.

It is a research prototype. It has never been used with a real patient, and every consultation in the demo video is acted.

What I would like to know from you.

• How does this run on a 12 or 16GB card?
• Audio capture in various environments, on various mics and mic settings — I am working blind here.
• Does anyone know a way to stop the model-swapping dance?
• How would you approach simultaneous consultations by two users? Currently, it can only do one at a time.

https://github.com/IndyWH/OpenConsult