Hospital Customization System - Execution Time Breakdown Analysis

Analysis Date: August 5, 2025
Data Source: frequency_based_evaluation_20250804_210752.json
Total Evaluation Time: 332.73 seconds (5.5 minutes)

📊 Overall Time Distribution

Total Execution Summary

Total Evaluation Runtime: 332.73 seconds
Number of Queries: 6 queries
Average Time per Query: 55.5 seconds
Fastest Query: 47.0 seconds (medium_1)
Slowest Query: 64.1 seconds (broad_1)
Standard Deviation: ±6.2 seconds

⏱️ Query-by-Query Time Breakdown

Query 1: broad_1 - Cardiac Palpitations

Query: "Patient presents with palpitations and is concerned about acute coronary syndrome"
⏱️ Total Execution Time: 64.13 seconds (SLOWEST)

Time Breakdown:

Hospital Guidelines Search: 6.476 seconds (10.1%)
Medical Advice Generation: 57.036 seconds (89.0%)
Processing Overhead: ~0.6 seconds (0.9%)

Performance Analysis:

Retrieved 24 hospital guidelines
Generated comprehensive cardiac assessment protocol
High generation time due to complex ACS evaluation steps

Query 2: broad_2 - Dyspnea/Heart Failure

Query: "Patient experiencing dyspnea with suspected heart failure"
⏱️ Total Execution Time: 56.85 seconds

Time Breakdown:

Hospital Guidelines Search: 5.231 seconds (9.2%)
Medical Advice Generation: 50.912 seconds (89.5%)
Processing Overhead: ~0.7 seconds (1.3%)

Performance Analysis:

Retrieved 53 hospital guidelines (HIGHEST)
Generated detailed heart failure management protocol
Moderate generation time despite high guideline count

Query 3: medium_1 - Severe Headache/SAH

Query: "67-year-old male with severe headache and neck stiffness, rule out subarachnoid hemorrhage"
⏱️ Total Execution Time: 47.00 seconds (FASTEST)

Time Breakdown:

Hospital Guidelines Search: 4.186 seconds (8.9%)
Medical Advice Generation: 42.149 seconds (89.7%)
Processing Overhead: ~0.7 seconds (1.4%)

Performance Analysis:

Retrieved 36 hospital guidelines
Generated focused neurological emergency protocol
Fastest execution demonstrates optimal query specificity

Query 4: medium_2 - Chest Pain/ACS

Query: "Patient with chest pain requiring evaluation for acute coronary syndrome"
⏱️ Total Execution Time: 52.85 seconds

Time Breakdown:

Hospital Guidelines Search: 4.892 seconds (9.3%)
Medical Advice Generation: 47.203 seconds (89.3%)
Processing Overhead: ~0.8 seconds (1.4%)

Performance Analysis:

Retrieved 24 hospital guidelines
Generated structured ACS evaluation workflow
Good balance between specificity and comprehensive coverage

Query 5: specific_1 - Spinal Cord Compression

Query: "Patient experiencing back pain with progressive limb weakness, suspected spinal cord compression"
⏱️ Total Execution Time: 54.12 seconds

Time Breakdown:

Hospital Guidelines Search: 3.784 seconds (7.0%)
Medical Advice Generation: 49.681 seconds (91.8%)
Processing Overhead: ~0.7 seconds (1.2%)

Performance Analysis:

Retrieved 18 hospital guidelines (LOWEST)
Generated specialized spinal emergency protocol
High generation time relative to guidelines suggests complex medical content

Query 6: specific_2 - Eclampsia

Query: "28-year-old pregnant woman with seizures and hypertension, evaluate for eclampsia"
⏱️ Total Execution Time: 57.64 seconds

Time Breakdown:

Hospital Guidelines Search: 4.127 seconds (7.2%)
Medical Advice Generation: 52.831 seconds (91.7%)
Processing Overhead: ~0.7 seconds (1.1%)

Performance Analysis:

Retrieved 22 hospital guidelines
Generated obstetric emergency management protocol
Highest generation time proportion due to specialized medical content

📈 Performance Pattern Analysis

1. Time Distribution by Query Type

Hospital Guidelines Search Time:

Broad Queries: Average 5.85 seconds (9.6% of total time)
Medium Queries: Average 4.54 seconds (9.1% of total time)
Specific Queries: Average 3.96 seconds (7.1% of total time)

Pattern: More specific queries require less search time, indicating efficient ANNOY index performance.

Medical Advice Generation Time:

Broad Queries: Average 53.97 seconds (89.3% of total time)
Medium Queries: Average 44.68 seconds (89.5% of total time)
Specific Queries: Average 51.26 seconds (91.8% of total time)

Pattern: Generation time dominates across all query types, with specific queries showing highest proportion.

2. Guidelines Retrieved vs Time Correlation

Query Type	Avg Guidelines	Avg Search Time	Efficiency (guidelines/sec)
Broad	38.5	5.85s	6.58
Medium	30.0	4.54s	6.61
Specific	20.0	3.96s	5.05

Finding: Medium queries show optimal search efficiency, while specific queries have lower throughput but higher precision.

3. System Performance Bottlenecks

Primary Bottleneck: LLM Generation (89.7% of total time)

Root Cause: Llama3-Med42-70B model inference time
Impact: Dominates execution regardless of retrieval efficiency
Optimization Potential: Caching, model quantization, or parallel processing

Secondary Factor: Hospital Guidelines Search (8.8% of total time)

Root Cause: ANNOY index traversal and BGE-Large-Medical embedding computation
Impact: Minimal but consistent across all queries
Current Performance: Excellent (sub-7 second search across 4,764 chunks)

🚀 Performance Optimization Opportunities

Short-term Optimizations (5-10 second improvement)

Response Caching: Cache similar medical condition responses
Template-based Generation: Use templates for common medical protocols
Parallel Processing: Generate multiple response sections simultaneously

Medium-term Optimizations (10-15 second improvement)

Model Quantization: Use quantized version of Llama3-Med42-70B
Streaming Generation: Start response generation during guideline retrieval
Smart Truncation: Limit generation length based on query complexity

Long-term Optimizations (15+ second improvement)

Custom Medical Model: Fine-tune smaller model on hospital-specific content
Hardware Acceleration: GPU-based inference optimization
Distributed Processing: Multi-node generation for complex queries

🔍 Medical Content Generation Analysis

Content Quality vs Time Trade-off

High-Quality Medical Content Indicators (correlate with longer generation times):

Multi-step diagnostic workflows
Specific medication dosages and routes
Risk stratification protocols
Emergency management procedures
Patient-specific considerations

Queries with Premium Content Generation:

broad_1 (64.1s): Comprehensive ACS evaluation protocol with detailed steps
specific_2 (57.6s): Complete eclampsia management with seizure protocols
broad_2 (56.9s): Heart failure assessment with multiple diagnostic pathways

Efficiency Leaders:

medium_1 (47.0s): Focused SAH protocol - optimal specificity
medium_2 (52.9s): Structured chest pain evaluation - balanced approach

📋 Summary and Recommendations

Key Findings

LLM Generation dominates runtime (89.7% average) - primary optimization target
Hospital search is highly efficient (8.8% average) - ANNOY index performing excellently
Medium queries show optimal balance - shortest time with comprehensive coverage
Content quality justifies generation time - clinical-grade protocols require complex processing

Strategic Recommendations

Focus optimization efforts on LLM inference rather than retrieval systems
Use medium-specificity queries as benchmark for optimal performance
Implement progressive response generation to improve perceived performance
Maintain current generation quality - time investment produces clinical-value content

Target Performance Goals

Current: 55.5 seconds average
Short-term target: 45-50 seconds (10-20% improvement)
Long-term target: 35-40 seconds (30-35% improvement)
Quality standard: Maintain current clinical-grade content depth

Analysis Generated: August 5, 2025
Data Source: OnCall.ai Hospital Customization Evaluation System
Report Version: v1.0 - Execution Time Analysis Edition