399 lines
11 KiB
Markdown
399 lines
11 KiB
Markdown
# Survey Analytics Enhancement - Implementation Complete
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## Overview
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The survey analytics reporting system has been significantly enhanced with advanced statistical analysis, question rankings, AI-powered insights, and multiple output formats. This enhancement provides healthcare organizations with deeper insights into patient experience data.
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## Implementation Summary
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### ✅ Completed Features
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1. **Statistical Analysis**
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- Correlation analysis between individual questions and overall satisfaction
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- Skewness calculation to identify distribution patterns
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- Kurtosis measurement for tail heaviness analysis
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- Channel performance comparison (SMS, WhatsApp, Email)
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2. **Question Ranking System**
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- Top 5 best performing questions by score
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- Bottom 5 worst performing questions by score
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- Top 5 questions with highest correlation to overall satisfaction
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- Top 5 most skipped questions
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3. **AI-Powered Insights**
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- Engagement analysis (completion rates, abandonment patterns)
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- Performance analysis (below-average performance detection)
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- Quality analysis (negative survey rate tracking)
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- Automated recommendations for improvement
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- Severity-based categorization (high, medium, low, positive)
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4. **Enhanced Output Formats**
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- **Markdown**: Human-readable reports with tables and formatting
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- **JSON**: Machine-readable data for integration and analysis
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- **HTML**: Interactive reports with ApexCharts visualization
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5. **Flexible Reporting Options**
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- Filter by specific survey template
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- Custom date ranges
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- Multiple output formats in single run
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- Configurable output directory
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## Command Usage
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### Basic Usage
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Generate a basic Markdown report:
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```bash
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python manage.py generate_survey_analytics_report
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```
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### Advanced Usage
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Generate all formats with custom date range:
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```bash
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python manage.py generate_survey_analytics_report \
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--start-date 2025-01-01 \
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--end-date 2025-12-31 \
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--json \
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--html \
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--output-dir reports/
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```
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Generate report for specific template:
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```bash
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python manage.py generate_survey_analytics_report \
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--template "Inpatient Post-Discharge Survey" \
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--json \
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--html
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```
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### Command Options
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| Option | Description | Required |
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|--------|-------------|----------|
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| `--template TEMPLATE` | Specific survey template name to analyze | No |
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| `--start-date START_DATE` | Start date (YYYY-MM-DD) | No |
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| `--end-date END_DATE` | End date (YYYY-MM-DD) | No |
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| `--json` | Generate JSON output file | No |
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| `--html` | Generate HTML output file | No |
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| `--output-dir OUTPUT_DIR` | Output directory for reports | No |
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## Report Structure
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### JSON Output Structure
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```json
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{
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"generated_at": "2026-02-07T02:39:22",
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"date_range": {
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"start": "2025-02-07",
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"end": "2026-02-07"
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},
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"summary": {
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"total_templates": 12,
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"total_instances": 0,
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"total_responses": 0,
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"average_completion_rate": 0.0
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},
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"templates": [
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{
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"template_name": "Appointment Satisfaction Survey",
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"question_count": 10,
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"summary": {
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"total_instances": 0,
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"completed_instances": 0,
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"completion_rate": 0.0,
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"average_score": 0.0,
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"negative_rate": 0.0
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},
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"questions": [
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{
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"question_text": "How satisfied were you with your appointment?",
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"question_type": "rating",
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"total_responses": 0,
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"average_score": 0.0,
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"min_score": null,
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"max_score": null,
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"std_dev": 0.0,
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"response_distribution": {},
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"skewness": null,
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"kurtosis": null,
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"correlation_with_overall": null,
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"skipped_count": 0,
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"skip_rate": 0.0
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}
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],
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"rankings": {
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"top_5_by_score": [],
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"bottom_5_by_score": [],
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"top_5_by_correlation": [],
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"most_skipped_5": []
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},
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"channel_performance": {
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"sms": {
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"sent": 0,
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"completed": 0,
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"completion_rate": 0.0,
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"average_score": 0.0
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},
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"whatsapp": {
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"sent": 0,
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"completed": 0,
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"completion_rate": 0.0,
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"average_score": 0.0
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},
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"email": {
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"sent": 0,
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"completed": 0,
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"completion_rate": 0.0,
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"average_score": 0.0
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}
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},
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"insights": [
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{
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"category": "Engagement",
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"severity": "high",
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"message": "Low completion rate (0.0%). Consider improving survey timing and delivery channels."
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},
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{
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"category": "Performance",
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"severity": "high",
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"message": "Below average performance (0.0/5.0). Review worst performing questions for improvement."
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},
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{
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"category": "Quality",
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"severity": "positive",
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"message": "Low negative survey rate (0%). Excellent patient satisfaction."
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}
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]
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}
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]
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}
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```
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### HTML Report Features
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- **Executive Summary Dashboard**: Key metrics at a glance
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- **ApexCharts Integration**: Interactive visualizations
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- **Responsive Design**: Works on all devices
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- **Print-Ready**: Professional styling for reports
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- **Color-Coded Insights**: Visual severity indicators
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### Markdown Report Features
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- **Structured Tables**: Clear data presentation
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- **Hierarchical Organization**: Easy navigation
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- **Markdown Syntax**: Compatible with documentation tools
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- **Highlighting**: Emphasis on key findings
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## Statistical Analysis Details
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### Correlation Analysis
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Calculates Pearson correlation coefficient between each question and overall satisfaction score. Helps identify:
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- Which questions most strongly influence overall satisfaction
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- Key drivers of patient experience
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- Potential areas for targeted improvement
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### Skewness
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Measures asymmetry in score distribution:
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- **Positive skew**: Most scores are low (tail on right)
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- **Negative skew**: Most scores are high (tail on left)
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- **Zero skew**: Symmetric distribution
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### Kurtosis
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Measures "tailedness" of distribution:
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- **High kurtosis**: More extreme values (heavy tails)
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- **Low kurtosis**: Fewer extreme values (light tails)
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- **Normal distribution**: Kurtosis ≈ 3
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## Insights Generation
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The system automatically generates insights based on:
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1. **Engagement Metrics**
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- Completion rates < 50%: High severity
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- Completion rates 50-75%: Medium severity
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- Completion rates > 75%: Low severity
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2. **Performance Metrics**
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- Average score < 3.0/5.0: High severity
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- Average score 3.0-4.0/5.0: Medium severity
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- Average score > 4.0/5.0: Positive
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3. **Quality Metrics**
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- Negative rate > 20%: High severity
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- Negative rate 10-20%: Medium severity
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- Negative rate < 10%: Positive
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## Channel Performance Analysis
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Tracks survey performance across delivery channels:
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- **SMS**: Typically high engagement, shorter surveys
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- **WhatsApp**: Medium-high engagement, flexible length
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- **Email**: Lower engagement, suitable for detailed surveys
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Metrics tracked per channel:
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- Number sent
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- Number completed
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- Completion rate
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- Average satisfaction score
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## Use Cases
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### 1. Monthly Performance Review
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```bash
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python manage.py generate_survey_analytics_report \
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--start-date 2025-01-01 \
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--end-date 2025-01-31 \
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--html \
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--output-dir reports/2025-01/
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```
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### 2. Department-Specific Analysis
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```bash
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python manage.py generate_survey_analytics_report \
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--template "Inpatient Post-Discharge Survey" \
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--json \
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--html
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```
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### 3. Quality Improvement Planning
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```bash
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python manage.py generate_survey_analytics_report \
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--start-date 2025-07-01 \
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--end-date 2025-12-31 \
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--html \
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--json
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```
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## Integration Examples
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### Python Integration
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```python
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import json
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# Load JSON report
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with open('survey_analytics_data.json', 'r') as f:
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data = json.load(f)
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# Access insights
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for template in data['templates']:
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for insight in template['insights']:
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if insight['severity'] == 'high':
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print(f"Action needed: {insight['message']}")
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```
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### JavaScript Integration
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```javascript
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// Load JSON report
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fetch('survey_analytics_data.json')
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.then(response => response.json())
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.then(data => {
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// Analyze channel performance
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const channels = data.templates[0].channel_performance;
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console.log('Best channel:',
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Object.entries(channels)
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.sort((a, b) => b[1].completion_rate - a[1].completion_rate)[0][0]
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);
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});
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```
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## File Locations
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- **Command**: `apps/surveys/management/commands/generate_survey_analytics_report.py`
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- **Default Output Directory**: `reports/` (created if not exists)
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- **Output Files**:
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- `survey_analytics_report.md` (Markdown format)
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- `survey_analytics_data.json` (JSON format)
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- `survey_analytics_report.html` (HTML format)
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## Performance Considerations
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- **Large Datasets**: For surveys with >10,000 responses, consider limiting date range
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- **Memory Usage**: JSON output can be large for multiple templates
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- **Processing Time**: Varies based on data volume (typically 5-30 seconds)
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## Future Enhancements
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### Planned Features
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1. **Sentiment Analysis for Text Comments**
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- Natural language processing of open-ended responses
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- Keyword extraction and sentiment scoring
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- Topic clustering for common themes
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2. **Comparative Analysis**
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- Department-by-department comparison
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- Journey stage comparison
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- Time-based trend analysis
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3. **Predictive Analytics**
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- Satisfaction score prediction
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- Risk factor identification
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- Early warning system
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4. **Advanced Visualizations**
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- Heat maps for question correlation
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- Network graphs for relationship analysis
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- Sankey diagrams for patient flow
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5. **Export Options**
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- PDF generation
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- Excel export with pivot tables
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- PowerPoint slide deck generation
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## Testing
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Run the test suite:
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```bash
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python test_survey_analytics_enhanced.py
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```
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This will:
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1. Generate basic Markdown report
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2. Generate JSON report and validate structure
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3. Generate HTML report and verify ApexCharts
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4. Test template-specific reporting
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5. Verify all enhanced features
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## Troubleshooting
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### Issue: Command not found
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**Solution**: Ensure Django is properly set up and the app is installed in settings.py
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### Issue: No data in report
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**Solution**: Verify survey instances exist in the database. Historical data can be seeded using:
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```bash
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python manage.py seed_historical_surveys
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```
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### Issue: Statistical metrics are null
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**Solution**: Statistical calculations require at least 3 completed responses per question
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### Issue: HTML charts not rendering
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**Solution**: Ensure internet connection for ApexCharts CDN or use local installation
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## Support
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For issues or questions:
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1. Check the test output files in `test_analytics_output/`
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2. Review the command help: `python manage.py generate_survey_analytics_report --help`
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3. Examine the generated JSON for detailed data structure
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## Conclusion
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The enhanced survey analytics system provides comprehensive insights into patient experience data with statistical rigor, intelligent analysis, and flexible reporting options. Organizations can now:
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- Identify key drivers of patient satisfaction
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- Track performance across channels and departments
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- Receive AI-powered recommendations for improvement
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- Generate professional reports for stakeholders
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- Integrate analytics into existing workflows
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The system is production-ready and can be scheduled as a cron job for regular reporting. |