🏥 The Challenge: Turning Paper Chaos into Usable Data
A growing healthcare network in the U.S. was facing a daily flood of unstructured documents: handwritten intake forms, scanned PDFs, inconsistent notes. Every week, thousands of files had to be manually reviewed and entered into their Epic EHR system by admin staff—slowing down operations, inflating costs, and introducing risk of error in patient records.
They knew AI could help—but didn’t have the in-house talent to deliver fast.
🚀 Our Approach: Assemble a High-Impact, Domain-Aligned Team
We focused on what we do best: building the right team to solve the problem.
In under two weeks, we assembled and deployed a fully integrated team of engineers, data experts, and healthcare-savvy professionals—working as an extension of the client’s operations, in their time zone, and fully aligned with their goals.
🧑💻 The Team We Put Together
We built a specialized, compact squad that combined deep technical skills with healthcare context:
- Machine Learning Engineer
Led the design and training of deep learning models using PyTorch, Hugging Face, and domain-specific datasets like MIMIC-III. - Data Engineer
Focused on OCR processing (Tesseract, LayoutParser), document parsing, and structured data pipelines. - Backend Developer
Built the API interfaces and microservice architecture using FastAPI, Docker, and PostgreSQL. - Healthcare Product Manager
Aligned all efforts with clinical workflows and regulatory requirements (HIPAA, HL7). - QA Analyst
Reviewed outputs, defined test cases, and ensured model accuracy and reliability in production.
All of them were senior-level professionals, handpicked by Southteams, and embedded within the client’s existing tech processes.
🔧 How It Worked: A Deep Learning Pipeline Built for Healthcare
The system was designed to convert scanned intake forms into clean, structured EHR-ready records.
Key Technologies Used
- Languages: Python, JavaScript
- Frameworks: PyTorch, Hugging Face Transformers, spaCy
- OCR & Parsing: Tesseract, LayoutParser, Amazon Textract (for benchmarking)
- Backend & Infra: FastAPI, Docker, AWS ECS, PostgreSQL, Redis, GitHub Actions, Terraform
Architecture Highlights
- Preprocessing & OCR
Intelligent layout parsing and handwriting recognition using hybrid OCR models. - NLP & Entity Recognition
Fine-tuned BioBERT models to extract medical terms—conditions, medications, allergies—even when written in shorthand or with inconsistent phrasing. - Normalization & Coding
Entities mapped to standardized formats like ICD-10 and RxNorm, with custom rule sets for validation and flagging anomalies. - EHR Integration
Final structured data exported in HL7-compatible format, reviewed through a custom dashboard, and pushed to the client’s Epic system.
✅ What We Achieved
By embedding the right team and focusing on outcomes, we helped the client shift from hours of manual data entry to automated processing—at scale.
- ⏱ 10x faster processing than manual workflows
- 🧠 92%+ accuracy with human-in-the-loop validation
- 💰 Significant cost savings by freeing admin teams
- 🧑⚕️ Faster doctor response times and smoother care coordination
- 🧾 Fewer documentation errors, lowering downstream risk
💡 Why It Works
Modern healthcare moves fast—but to unlock the full power of AI, organizations need more than tools. They need the right team, built for their domain, their timeline, and their culture. That’s where we come in.
At Southteams, we help companies scale fast by building dedicated, remote-first engineering teams in Latin America. Teams that understand your business, speak your language, and deliver real results—without the overhead of hiring in-house.
📬 Ready to build your dream team for your next AI challenge?
Let’s talk. We’ll help you move from idea to impact—faster than you think.
