HOSPITAL_CUSTOMIZATION_ANALYSIS_SUMMARY.md

Hospital Customization System - Tag Structure & Keyword Analysis

Executive Summary

The hospital customization system implements a sophisticated two-stage retrieval architecture with 21 medical PDFs, 134 unique medical tags, and 4,764 text chunks processed through BGE-Large-Medical embeddings and ANNOY indices.

System Architecture

Core Components

• Embedding Model: BGE-Large-Medical (1024 dimensions)
• Search Method: Two-stage ANNOY retrieval with angular similarity
• Document Processing: 256-character chunks with 25-character overlap
• Tag Structure: 134 medical concepts (symptoms + diagnoses + treatments)

Processing Pipeline

1. Stage 1: Tag-based document filtering using medical concept embeddings
2. Stage 2: Chunk-level retrieval within relevant documents
3. Filtering: Top-P (0.6) + minimum similarity (0.25) thresholds

Tag Structure Analysis

Keyword Distribution

Category	Count	Examples
Symptoms	45 tags	palpitations, dyspnea, syncope, chest pain
Diagnoses	44 tags	meningitis, acute coronary syndrome, heart failure
Ambiguous/Mixed	45 tags	Complex medical terms spanning categories

Frequency Patterns

• High Frequency (3+ occurrences): palpitations, dyspnea, syncope
• Medium Frequency (2 occurrences): chest pain, emotional distress, fever, meningitis
• Low Frequency (1 occurrence): 121 specific medical terms

Document Coverage Analysis

Top Documents by Content Volume

1. Chest Pain Guidelines (1,053 chunks) - Comprehensive cardiac evaluation
2. Atrial Fibrillation Guidelines (1,047 chunks) - Complete arrhythmia management
3. Stroke Management (703 chunks) - Acute neurological emergencies
4. Wilson's Disease (415 chunks) - Specialized genetic condition
5. Hereditary Angioedema (272 chunks) - Rare immune disorder

Dual Coverage (Symptoms + Diagnoses)

All 21 PDFs contain both symptom and diagnosis keywords, with top documents having:

• Spinal Cord Emergencies: 5 symptoms, 7 diagnoses (12 total)
• Dizziness Approach: 4 symptoms, 8 diagnoses (12 total)
• Headache Management: 3 symptoms, 6 diagnoses (9 total)

Recommended Test Query Strategy

1. Broad Query Testing (High-Frequency Keywords)

• "palpitations" - Expected: 3 documents
• "dyspnea" - Expected: 3 documents  
• "syncope" - Expected: 3 documents
• "meningitis" - Expected: 2 documents
• "acute coronary syndrome" - Expected: 2 documents

2. Medium Specificity Testing

• "chest pain" - Expected: 2 documents
• "heart failure" - Expected: 2 documents
• "fever" - Expected: 2 documents

3. Specific Query Testing (Low-Frequency)

• "back pain" - Expected: 1 document (Spinal Cord Emergencies)
• "spinal cord compression" - Expected: 1 document
• "vertebral fracture" - Expected: 1 document

4. Combined Query Testing

• "palpitations chest pain" - Expected: Multiple documents
• "dyspnea heart failure" - Expected: Cardiac-focused results
• "fever meningitis" - Expected: Infection-focused results

5. Semantic Similarity Testing

• "emergency cardiac arrest" - Tests semantic matching beyond exact keywords
• "patient presenting with acute symptoms" - Tests broad medical query handling
• "rare genetic disorder" - Tests specialized condition retrieval

System Performance Characteristics

Expected Behavior

• Stage 1 Filtering: Should identify 5-20 relevant tags per query
• Document Selection: Should narrow to 2-8 relevant documents
• Stage 2 Retrieval: Should return 3-10 high-quality chunks
• Similarity Thresholds: 25% minimum, Top-P filtering at 60%

Quality Indicators

• High Precision: Specific queries should return 1-2 documents
• Good Recall: Broad queries should find all relevant documents
• Semantic Matching: Related terms should retrieve appropriate content
• Fallback Robustness: System should handle edge cases gracefully

Key Insights for Testing

1. Frequency-Based Test Coverage

• Use high-frequency terms to test broad retrieval capabilities
• Use medium-frequency terms to validate balanced precision/recall
• Use low-frequency terms to test specific document targeting

2. Medical Domain Validation

• BGE-Large-Medical embeddings should excel at medical concept similarity
• System should handle medical terminology variations and synonyms
• Diagnostic reasoning chains should be retrievable through symptom queries

3. Two-Stage Architecture Benefits

• Tag-based filtering reduces search space efficiently
• Chunk-level retrieval provides precise content extraction
• Fallback mechanisms ensure robustness for edge cases

Recommendations for Query Testing

1. Start with high-frequency keywords to validate basic system functionality
2. Test symptom→diagnosis pathways using medically coherent combinations
3. Validate edge cases with non-exact but semantically related queries
4. Monitor performance metrics including precision, recall, and response times
5. Test fallback behavior when primary retrieval fails

This analysis provides a comprehensive foundation for understanding and testing the hospital customization system's tag structure and retrieval capabilities.