AI/NLP engineer with strong healthcare document processing experience.

Hello! Are you already using ready-made models for classifying types of medical documents, or do you plan to train your own from scratch?

I will discuss the deadlines and budget more precisely in personal correspondence.

Here’s how I will execute this project:
1. I will deploy an OCR pipeline (AWS Textract or LlamaParse) to extract text from PDFs and faxes.
2. I will apply an LLM (for example, LangChain) to parse clinical fields into structured JSON with confidence scoring.
3. I will add validation with source references and automatic tagging of missing fields.

… Thank you for considering my proposal. I look forward to the opportunity to collaborate with you!

Show original

Translate

we already have an almost ready healthcare document ai pipeline that can be adapted quickly for your poc, and i am online here to discuss the sample set now (:

for the first task, i estimate 10 days and 2500 usd for a controlled poc - document type recognition, ocr, field extraction to json, confidence scores, source page references, and missing-field handling without guessing.

similar healthcare and ai work
- https://business.ingello.com/rapport - healthcare process automation and structured clinical workflow logic
- https://business.ingello.com/lita-doctor - medical platform experience with doctor-side workflows and structured records
- https://business.ingello.com/vorfahr - ai automation case, relevant for extraction pipelines and agent-based processing

… AI extraction should be built as separate layers - ocr, document classification, schema extraction, validation, confidence scoring, and review of uncertain fields.

i would use python backend, aws textract or google document ai where useful, and llm extraction with strict schemas, source anchoring, and no free guessing.

for hipaa-aware handling, i would keep storage, access control, audit logs, and de-identification separated before model processing where required.

two quick questions before i lock the estimate more tightly
- how many sample document types are in the poc set - mds, faxed snf notes, claims, care plans, or something else
- do you already have target json schemas, or should we define them from the documents

our flh page - https://systems-fl.ingello.com

i can start with poc architecture and a first extraction protype after receiving the de-identified samples... small note, clinical document ai usually looks smaller on paper than it becomes in production =/

We have experience in processing medical documents and implementing NLP solutions for extracting structured data from complex reports, including MDS and long-term care forms. We achieve this through custom OCR pipelines and LLM models tailored to the specifics of medical terminology to ensure high parsing accuracy. We are ready to discuss the details of integration into your system.

Show original

Translate

Good day, I am ready to complete your task quickly and efficiently. I have extensive experience in creating various parsers. Please write to me in private messages to discuss the details. I would be happy to help :)

Show original

Translate