AI/NLP engineer with strong healthcare document processing experience.

Budget: 25000 UAH Deadline: 8 days

⚙️ OCR + NLP pipeline for medical documents is my daily work. I know where such projects usually stumble: unclear scans, various form formats, mixed terminology language.

I will extract structured data from long-term care reports and forms using Python — with preliminary cleaning of the OCR output and an NLP model for field extraction.

Input: scans/photos of documents
Text recognition (Tesseract or an approach tailored to your data)
NLP entity extraction into structured JSON
Validation of results on a control sample

I have done similar work with complex medical reports — the main benefit is that after setting up the pipeline, it automatically fills in the fields without manual work.

I can start today; I only need a sample of document samples to begin.

Write to me — and I will draft a plan for the first stage tailored to your formats in 10 minutes. Expand

Budget: 12345 UAH Deadline: 3 days

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!

Budget: 27000 UAH Deadline: 10 days

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 =/ Expand

Budget: 15000 UAH Deadline: 20 days

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.

Budget: 1000 UAH Deadline: 1 day

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 :)