evaluation/results/20250805/execution_time_breakdown.md

# 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)
1. **Response Caching**: Cache similar medical condition responses
2. **Template-based Generation**: Use templates for common medical protocols
3. **Parallel Processing**: Generate multiple response sections simultaneously

### Medium-term Optimizations (10-15 second improvement)
1. **Model Quantization**: Use quantized version of Llama3-Med42-70B
2. **Streaming Generation**: Start response generation during guideline retrieval
3. **Smart Truncation**: Limit generation length based on query complexity

### Long-term Optimizations (15+ second improvement)
1. **Custom Medical Model**: Fine-tune smaller model on hospital-specific content
2. **Hardware Acceleration**: GPU-based inference optimization
3. **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**:
1. **broad_1** (64.1s): Comprehensive ACS evaluation protocol with detailed steps
2. **specific_2** (57.6s): Complete eclampsia management with seizure protocols
3. **broad_2** (56.9s): Heart failure assessment with multiple diagnostic pathways

**Efficiency Leaders**:
1. **medium_1** (47.0s): Focused SAH protocol - optimal specificity
2. **medium_2** (52.9s): Structured chest pain evaluation - balanced approach

---

## 📋 Summary and Recommendations

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

### Strategic Recommendations
1. **Focus optimization efforts on LLM inference** rather than retrieval systems
2. **Use medium-specificity queries as benchmark** for optimal performance
3. **Implement progressive response generation** to improve perceived performance
4. **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