Electronic Medical Record Document Processing System

Challenge

Our client, a healthcare technology company, needed a system capable of reliably extracting key information from electronic medical records (EMRs) that often combine structured text, tables, handwritten notes, and image-based content.

The main challenge was to transform these highly variable documents into structured, analyzable data while maintaining compliance with healthcare standards such as HIPAA and ensuring interoperability with hospital systems.

Solution

We developed an intelligent document processing system that combines advanced OCR, large language models (LLMs), and modern retrieval techniques to handle complex medical records with high accuracy and compliance.

The system automatically identifies and extracts key data—such as diagnoses, procedures, medications, and treatment outcomes—while filtering out non-essential information like patient instructions and nursing notes.

Semantic Understanding and Terminology Normalization

Medical terminology varies widely between hospitals and practitioners. Our LLM-based semantic processing module—powered by GPT-4o and Gemini 2.5 Pro—addresses this issue by interpreting the meaning of terms, not just their wording.

Extracted entities are semantically linked to standardized medical taxonomies like ICD-10 and SNOMED CT, enabling consistent, analyzable data across sources.

Handwritten and Table Recognition

Many medical documents still include handwritten doctor notes or embedded tables with lab results. To handle this complexity, we integrated Azure AI Document Intelligence (2024 release) for robust table recognition, handwriting interpretation, and form understanding.

For additional flexibility and cost optimization, we also implemented open-source OCR solutions (PaddleOCR, TrOCR) as fallback options—ensuring improved performance even on challenging or low-quality scans.

Intelligent Data Retrieval and Querying

The system includes a Retrieval-Augmented Generation (RAG) component that allows clinicians and administrators to query the extracted data using natural language.

For example:

“Find patients with abnormal lung CT results and corresponding treatment costs.”

This turns previously unstructured EMRs into a searchable, knowledge-rich dataset.

Compliance and Integration

The system is built with FHIR-compliant APIs for seamless interoperability with hospital EHR systems.

 It also implements HIPAA-compliant audit logging, PHI redaction, and secure access controls to ensure full traceability and compliance.

An active learning feedback loop enables medical staff to correct extraction errors, continuously improving the accuracy of the models.

The platform is deployed on a scalable microservices architecture using Azure Kubernetes Service (AKS).

Results

• 80% reduction in manual data entry time through automated extraction and structuring of complex EMRs.
• Improved accuracy by leveraging context-aware AI models, significantly reducing transcription errors and inconsistencies.
• Enhanced clinical insights, with a semantically enriched, searchable knowledge base for analytics and decision support.
• Regulatory compliance ensured through an end-to-end HIPAA-compliant workflow with full auditability.