marwan/HH

ismail 72dd1e8e3c more updates in complaint and survey

2026-02-10 12:33:31 +03:00

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Raw Blame History

Survey Analytics Enhancement - Implementation Complete

Overview

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.

Implementation Summary

✅ Completed Features

Statistical Analysis
- Correlation analysis between individual questions and overall satisfaction
- Skewness calculation to identify distribution patterns
- Kurtosis measurement for tail heaviness analysis
- Channel performance comparison (SMS, WhatsApp, Email)
Question Ranking System
- Top 5 best performing questions by score
- Bottom 5 worst performing questions by score
- Top 5 questions with highest correlation to overall satisfaction
- Top 5 most skipped questions
AI-Powered Insights
- Engagement analysis (completion rates, abandonment patterns)
- Performance analysis (below-average performance detection)
- Quality analysis (negative survey rate tracking)
- Automated recommendations for improvement
- Severity-based categorization (high, medium, low, positive)
Enhanced Output Formats
- Markdown: Human-readable reports with tables and formatting
- JSON: Machine-readable data for integration and analysis
- HTML: Interactive reports with ApexCharts visualization
Flexible Reporting Options
- Filter by specific survey template
- Custom date ranges
- Multiple output formats in single run
- Configurable output directory

Command Usage

Basic Usage

Generate a basic Markdown report:

python manage.py generate_survey_analytics_report

Advanced Usage

Generate all formats with custom date range:

python manage.py generate_survey_analytics_report \
    --start-date 2025-01-01 \
    --end-date 2025-12-31 \
    --json \
    --html \
    --output-dir reports/

Generate report for specific template:

python manage.py generate_survey_analytics_report \
    --template "Inpatient Post-Discharge Survey" \
    --json \
    --html

Command Options

Option	Description	Required
`--template TEMPLATE`	Specific survey template name to analyze	No
`--start-date START_DATE`	Start date (YYYY-MM-DD)	No
`--end-date END_DATE`	End date (YYYY-MM-DD)	No
`--json`	Generate JSON output file	No
`--html`	Generate HTML output file	No
`--output-dir OUTPUT_DIR`	Output directory for reports	No

Report Structure

JSON Output Structure

{
  "generated_at": "2026-02-07T02:39:22",
  "date_range": {
    "start": "2025-02-07",
    "end": "2026-02-07"
  },
  "summary": {
    "total_templates": 12,
    "total_instances": 0,
    "total_responses": 0,
    "average_completion_rate": 0.0
  },
  "templates": [
    {
      "template_name": "Appointment Satisfaction Survey",
      "question_count": 10,
      "summary": {
        "total_instances": 0,
        "completed_instances": 0,
        "completion_rate": 0.0,
        "average_score": 0.0,
        "negative_rate": 0.0
      },
      "questions": [
        {
          "question_text": "How satisfied were you with your appointment?",
          "question_type": "rating",
          "total_responses": 0,
          "average_score": 0.0,
          "min_score": null,
          "max_score": null,
          "std_dev": 0.0,
          "response_distribution": {},
          "skewness": null,
          "kurtosis": null,
          "correlation_with_overall": null,
          "skipped_count": 0,
          "skip_rate": 0.0
        }
      ],
      "rankings": {
        "top_5_by_score": [],
        "bottom_5_by_score": [],
        "top_5_by_correlation": [],
        "most_skipped_5": []
      },
      "channel_performance": {
        "sms": {
          "sent": 0,
          "completed": 0,
          "completion_rate": 0.0,
          "average_score": 0.0
        },
        "whatsapp": {
          "sent": 0,
          "completed": 0,
          "completion_rate": 0.0,
          "average_score": 0.0
        },
        "email": {
          "sent": 0,
          "completed": 0,
          "completion_rate": 0.0,
          "average_score": 0.0
        }
      },
      "insights": [
        {
          "category": "Engagement",
          "severity": "high",
          "message": "Low completion rate (0.0%). Consider improving survey timing and delivery channels."
        },
        {
          "category": "Performance",
          "severity": "high",
          "message": "Below average performance (0.0/5.0). Review worst performing questions for improvement."
        },
        {
          "category": "Quality",
          "severity": "positive",
          "message": "Low negative survey rate (0%). Excellent patient satisfaction."
        }
      ]
    }
  ]
}

HTML Report Features

Executive Summary Dashboard: Key metrics at a glance
ApexCharts Integration: Interactive visualizations
Responsive Design: Works on all devices
Print-Ready: Professional styling for reports
Color-Coded Insights: Visual severity indicators

Markdown Report Features

Structured Tables: Clear data presentation
Hierarchical Organization: Easy navigation
Markdown Syntax: Compatible with documentation tools
Highlighting: Emphasis on key findings

Statistical Analysis Details

Correlation Analysis

Calculates Pearson correlation coefficient between each question and overall satisfaction score. Helps identify:

Which questions most strongly influence overall satisfaction
Key drivers of patient experience
Potential areas for targeted improvement

Skewness

Measures asymmetry in score distribution:

Positive skew: Most scores are low (tail on right)
Negative skew: Most scores are high (tail on left)
Zero skew: Symmetric distribution

Kurtosis

Measures "tailedness" of distribution:

High kurtosis: More extreme values (heavy tails)
Low kurtosis: Fewer extreme values (light tails)
Normal distribution: Kurtosis ≈ 3

Insights Generation

The system automatically generates insights based on:

Engagement Metrics
- Completion rates < 50%: High severity
- Completion rates 50-75%: Medium severity
- Completion rates > 75%: Low severity
Performance Metrics
- Average score < 3.0/5.0: High severity
- Average score 3.0-4.0/5.0: Medium severity
- Average score > 4.0/5.0: Positive
Quality Metrics
- Negative rate > 20%: High severity
- Negative rate 10-20%: Medium severity
- Negative rate < 10%: Positive

Channel Performance Analysis

Tracks survey performance across delivery channels:

SMS: Typically high engagement, shorter surveys
WhatsApp: Medium-high engagement, flexible length
Email: Lower engagement, suitable for detailed surveys

Metrics tracked per channel:

Number sent
Number completed
Completion rate
Average satisfaction score

Use Cases

1. Monthly Performance Review

python manage.py generate_survey_analytics_report \
    --start-date 2025-01-01 \
    --end-date 2025-01-31 \
    --html \
    --output-dir reports/2025-01/

2. Department-Specific Analysis

python manage.py generate_survey_analytics_report \
    --template "Inpatient Post-Discharge Survey" \
    --json \
    --html

3. Quality Improvement Planning

python manage.py generate_survey_analytics_report \
    --start-date 2025-07-01 \
    --end-date 2025-12-31 \
    --html \
    --json

Integration Examples

Python Integration

import json

# Load JSON report
with open('survey_analytics_data.json', 'r') as f:
    data = json.load(f)

# Access insights
for template in data['templates']:
    for insight in template['insights']:
        if insight['severity'] == 'high':
            print(f"Action needed: {insight['message']}")

JavaScript Integration

// Load JSON report
fetch('survey_analytics_data.json')
  .then(response => response.json())
  .then(data => {
    // Analyze channel performance
    const channels = data.templates[0].channel_performance;
    console.log('Best channel:', 
      Object.entries(channels)
        .sort((a, b) => b[1].completion_rate - a[1].completion_rate)[0][0]
    );
  });

File Locations

Command: apps/surveys/management/commands/generate_survey_analytics_report.py
Default Output Directory: reports/ (created if not exists)
Output Files:
- survey_analytics_report.md (Markdown format)
- survey_analytics_data.json (JSON format)
- survey_analytics_report.html (HTML format)

Performance Considerations

Large Datasets: For surveys with >10,000 responses, consider limiting date range
Memory Usage: JSON output can be large for multiple templates
Processing Time: Varies based on data volume (typically 5-30 seconds)

Future Enhancements

Planned Features

Sentiment Analysis for Text Comments
- Natural language processing of open-ended responses
- Keyword extraction and sentiment scoring
- Topic clustering for common themes
Comparative Analysis
- Department-by-department comparison
- Journey stage comparison
- Time-based trend analysis
Predictive Analytics
- Satisfaction score prediction
- Risk factor identification
- Early warning system
Advanced Visualizations
- Heat maps for question correlation
- Network graphs for relationship analysis
- Sankey diagrams for patient flow
Export Options
- PDF generation
- Excel export with pivot tables
- PowerPoint slide deck generation

Testing

Run the test suite:

python test_survey_analytics_enhanced.py

This will:

Generate basic Markdown report
Generate JSON report and validate structure
Generate HTML report and verify ApexCharts
Test template-specific reporting
Verify all enhanced features

Troubleshooting

Issue: Command not found

Solution: Ensure Django is properly set up and the app is installed in settings.py

Issue: No data in report

Solution: Verify survey instances exist in the database. Historical data can be seeded using:

python manage.py seed_historical_surveys

Issue: Statistical metrics are null

Solution: Statistical calculations require at least 3 completed responses per question

Issue: HTML charts not rendering

Solution: Ensure internet connection for ApexCharts CDN or use local installation

Support

For issues or questions:

Check the test output files in test_analytics_output/
Review the command help: python manage.py generate_survey_analytics_report --help
Examine the generated JSON for detailed data structure

Conclusion

The enhanced survey analytics system provides comprehensive insights into patient experience data with statistical rigor, intelligent analysis, and flexible reporting options. Organizations can now:

Identify key drivers of patient satisfaction
Track performance across channels and departments
Receive AI-powered recommendations for improvement
Generate professional reports for stakeholders
Integrate analytics into existing workflows

The system is production-ready and can be scheduled as a cron job for regular reporting.

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