EHR Semantic Compressor

Overview

AI-powered EHR summarization using Transformer architecture to extract key clinical information from lengthy medical records. This skill processes lengthy Electronic Health Record (EHR) documents and generates structured, clinically accurate summaries.

Technical Difficulty: High

When to Use

• - Input contains lengthy EHR documents (1600+ words) requiring summarization
• Clinical records need structured extraction of key information
• Quick review of patient history, medications, allergies, or diagnoses is needed
• Medical documentation requires compression while maintaining accuracy

Core Features

1. 1. Fast Processing: Process lengthy EHR documents (1600+ words) in 10-20 seconds
2. Structured Summaries: Generate bullet-point summaries (200-300 words)
3. Critical Information Extraction:

- Patient allergies and adverse reactions - Family medical history - Current and past medications - Diagnoses and conditions - Vital signs and lab results - Procedures and surgeries

1. 4. Clinical Accuracy: Maintains completeness of medical information

Usage

Basic Usage

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Input Format

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Output Format

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Parameters

Parameter	Type	Default	Required	Description
INLINECODE0, INLINECODE1	string	-	Yes	Input EHR document text file path
INLINECODE2, INLINECODE3

string | - | No | Output JSON file path | | --max-length | int | 300 | No | Maximum summary length in words | | --extract-sections | string | all | No | Comma-separated sections to extract | | --format | string | json | No | Output format (json, markdown, text) |

Technical Details

Architecture

• - Base Model: Transformer-based encoder-decoder architecture
• Medical Domain Adaptation: Fine-tuned on clinical text corpora
• Section Extraction: Rule-based + ML hybrid approach for structured data
• Processing Pipeline: Text segmentation -> Summarization -> Section extraction -> Output formatting

Dependencies

See references/requirements.txt for complete list.

Key dependencies:

• - transformers >= 4.30.0
• torch >= 2.0.0
• spacy >= 3.6.0
• scispacy >= 0.5.3

Performance

• - Processing Time: 10-20 seconds for 1600+ word documents
• Memory: Requires ~2GB RAM
• Output Length: 200-300 words (configurable)
• Compression Ratio: ~85-90%

References

• - references/requirements.txt - Python dependencies
• INLINECODE9 - Clinical summarization guidelines
• INLINECODE10 - Example input format
• INLINECODE11 - Example output format

Safety & Compliance

• - No external API calls or service dependencies
• All processing performed locally
• No patient data transmitted outside the system
• Error messages are semantic and do not expose technical details

Testing

Run unit tests:
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Error Handling

All errors return semantic messages:

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Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access

No external API calls | Low | | File System Access | Read input files, write output files | Medium | | Instruction Tampering | Standard prompt guidelines | Low | | Data Exposure | Output files saved to workspace | Low |

Security Checklist

• - [ ] No hardcoded credentials or API keys
• [ ] No unauthorized file system access (../)
• [ ] Output does not expose sensitive information
• [ ] Prompt injection protections in place
• [ ] Input file paths validated (no ../ traversal)
• [ ] Output directory restricted to workspace
• [ ] Script execution in sandboxed environment
• [ ] Error messages sanitized (no stack traces exposed)
• [ ] Dependencies audited

Prerequisites

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Evaluation Criteria

Success Metrics

• - [ ] Successfully executes main functionality
• [ ] Output meets quality standards
• [ ] Handles edge cases gracefully
• [ ] Performance is acceptable

Test Cases

1. 1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• - Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:

- Performance optimization - Additional feature support