Medical Record
Summarization at Scale
Automating summarization of 1000+ page medical records with long-context LLMs and clinical domain adaptation for healthcare providers.
Project Overview
A healthcare provider faced a significant bottleneck: clinicians spent hours summarizing 1000+ page medical records to extract critical information for patient care decisions. This manual process was time-consuming, error-prone, and limited scalability.
We developed an LLM-powered summarization system using long-context models, visit-level segmentation, and clinical domain adaptation to automatically extract and organize critical information from complex medical documents.
The Challenge
- •Processing 1000+ page documents efficiently
- •Maintaining clinical accuracy and terminology
- •Handling multi-modal medical data
- •Visit-level segmentation and organization
Our Solution
- •Long-context LLM architecture for document processing
- •Clinical domain adaptation fine-tuning
- •Visit-level segmentation pipeline
- •Multi-modal document handling
Results & Impact
Page Documents
Successfully summarized in seconds
Accuracy Rate
Clinical information retention
Processing Time
Down from 3+ hours manually
Scalability
More patients served per clinician
Technical Implementation
Core Technologies
Infrastructure
Architecture Highlights
- •Document Preprocessing: Intelligent extraction of structured and unstructured data from medical documents
- •Long-Context LLM: Processing 1000+ pages in a single context window
- •Clinical Domain Adaptation: Fine-tuned models on medical terminology and concepts
- •Visit Segmentation: Automatic organization by clinical visits and time periods
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