AI-Assisted Healthcare Document Extraction
How Quantal AI transformed unstructured medical documents into structured intake data without replacing the client's existing workflow.
Our Client
How Quantal AI transformed unstructured medical documents into structured intake data without replacing the client's existing workflow.
A healthcare operations platform needed to convert unstructured medical forms and patient documents into structured data that could flow directly into its existing intake workflow. Documents arrived as PDFs and image files while the receiving system required structured information across multiple categories including patient details, contact information, payer information, medications, diagnoses, admissions and assessment data.
Quantal AI proposed building a deployable AI-powered document extraction service that would act as an integration layer between the unstructured documents and the client's existing intake system retrieving documents directly from Amazon S3, extracting structured data using Google Gemini and validating all outputs before passing them downstream.
The objective was to automate document processing without replacing the client's current infrastructure by preserving existing workflows while eliminating manual extraction effort and improving data accuracy across all processed documents.
Industry
Healthcare
Services Provided
AI Document Processing and Workflow Automation
Tech Stack
Python, FastAPI, Celery, Redis, Google Gemini, Amazon S3, PyMuPDF, Pillow, Docker, Docker Compose, Flower, JWT
Outcome
Automated data sourcing and enrichment, improved data quality, faster outreach cycles, and scalable CRM workflows
System Design
Proposed Architecture
The service retrieves documents from S3, processes PDFs and supported image formats, and uses Google Gemini for page-aware information extraction.
The extracted data is then validated against a predefined schema before being sent to the client's existing import system.
1
S3 Document
2
Document Processing
3
AI Extraction
4
Validation
5
Structured Intake
Our Challenge
The Challenge
A healthcare operations platform needed to convert medical forms and patient documents into structured information that could be passed into its existing intake workflow. Documents arrived as PDFs and image files, while the receiving system required structured information across areas such as:
- Patient details
- Contact information
- Payer information
- Medications
- Diagnoses
- Admissions
- Assessment data
The challenge was to automate document extraction while working within the client's existing infrastructure, including documents stored in Amazon S3 and an established import process.
Our Solution
The Solution
Quantal AI built a deployable AI-powered document extraction service that acts as an integration layer between unstructured documents and the client's existing intake system.
Proven Outcome
Results & Impact
The solution creates a deployable bridge between unstructured medical documents and structured healthcare intake.
It enables the client to:
01
Reduce manual document processing
Automate the extraction of information from medical forms and patient documents.
02
Preserve existing systems
Introduce AI-powered extraction without replacing the client's current document storage or intake platform.
03
Standardize structured data
Validate extracted information against a defined schema before it enters the downstream workflow.
04
Improve workflow visibility
Allow users and operators to track processing status and task outcomes.
05
Create a foundation for future automation
The integration layer can evolve as document types and intake requirements change.
Deliverables
Built for Reliable Processing
The solution was designed to handle document processing asynchronously while keeping the client-facing API responsive.
Key capabilities include:
Asynchronous processing using FastAPI, Celery and Redis
PDF and image support for common document formats
Page-aware AI extraction using Google Gemini
Schema-led validation to keep extracted information structured
Secure API access using bearer-token authentication
Task tracking and progress updates
Operational monitoring through logs, health checks and Flower
Retry handling for selected transient AI processing failures
Under the Hood
Our Technology Stack
Python
FastAPI
Celery
Redis
Google Gemini
Amazon S3
PyMuPDF
Pillow
Docker
Docker Compose
Flower
JWT
Why Choose Us
Why Businesses Choose Quantal AI for Document Extraction
01
Proven experience building AI-powered document extraction systems for healthcare operations platforms.
02
Deep expertise in LLM integration, asynchronous processing and schema-led validation for production environments.
03
Scalable architecture designed to handle high-volume document processing without disrupting existing workflows.
04
Seamless integration into existing infrastructure, Amazon S3 storage and intake systems without replacement or rebuilding.
If you are looking to automate document extraction, structure unstructured medical data and integrate AI into your existing workflow without replacing your current systems, we can help design and implement a solution according to your document processing requirements.
The Expert Behind This Project
Meet the Expert
Meet Nagadia
Senior AI Engineer/Developer
4+ years of experience across Artificial Intelligence, Machine Learning, NLP, LLMs, RAG, Data Science, and AI-powered application development.
NLP
Machine Learning
Artificial Intelligence
Data Science
LLM
OpenAI
RAG
Python
SQL
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Want Results Like These?
Let's Turn Unstructured Documents into Structured Data
Have unstructured medical forms or patient documents that need to flow into your existing intake workflow? Let's discuss how we can build a deployable extraction solution around your current infrastructure.
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Call us
+1 315 809 3225 -
Email us
contact@quantaltech.ai