Dashboard Design for Trials
Design layout sketches for clinical trial data monitoring panels, displaying recruitment progress, AE incidence rates, and other key metrics.
Features
- Generate HTML layout sketches for clinical trial Dashboards
- Support multiple chart types: progress bars, line charts, pie charts, bar charts, etc.
- Customizable study protocol, site count, key metrics
- Responsive design, adaptable to different screen sizes
Usage
python scripts/main.py [options]
Parameters
| Parameter | Type | Default | Required | Description |
|---|
| ----------- | ------ | --------- | ---------- | ------------- |
--study-id | string | STUDY-001 | No | Study ID |
--study-name | string | Clinical Trial A | No | Study Name |
--sites | int | 10 | No | Number of sites |
--target-enrollment | int | 100 | No | Target enrollment count |
--current-enrollment | int | 45 | No | Current enrollment count |
--ae-count | int | 12 | No | Adverse event count |
--output | string | dashboard.html | No | Output HTML file path |
Examples
# Generate default Dashboard
python scripts/main.py
# Customize study parameters
python scripts/main.py \
--study-id "PHASE-III-2024" \
--study-name "Phase III Clinical Trial of New Drug for Type 2 Diabetes" \
--sites 15 \
--target-enrollment 300 \
--current-enrollment 120 \
--ae-count 25 \
--output my_dashboard.html
Output
Generates an HTML Dashboard containing the following modules:
- Study Overview Card - Study ID, name, status
- Recruitment Progress - Overall progress bar, site-by-site progress comparison
- Subject Distribution - Gender, age distribution pie charts
- AE Monitoring - Adverse event incidence rate, severity distribution
- Data Quality - CRF completion rate, query count
- Timeline - Study milestones, estimated completion date
Dependencies
- Python 3.7+
- No additional dependencies (pure standard library generates HTML/CSS/JS)
Author
Skill ID: 194
Risk Assessment
| Risk Indicator | Assessment | Level |
|---|
| ---------------- | ------------ | ------- |
| Code Execution | Python/R scripts executed locally | Medium |
| Network Access | No external API calls | Low |
| File System Access | Read input files, write output files | Medium |
| Instruction Tampering | Standard prompt guidelines | Low |
| Data Exposure | Output files saved to workspace | Low |
Security Checklist
- [ ] No hardcoded credentials or API keys
- [ ] No unauthorized file system access (../)
- [ ] Output does not expose sensitive information
- [ ] Prompt injection protections in place
- [ ] Input file paths validated (no ../ traversal)
- [ ] Output directory restricted to workspace
- [ ] Script execution in sandboxed environment
- [ ] Error messages sanitized (no stack traces exposed)
- [ ] Dependencies audited
Prerequisites
No additional Python packages required.
Evaluation Criteria
Success Metrics
- [ ] Successfully executes main functionality
- [ ] Output meets quality standards
- [ ] Handles edge cases gracefully
- [ ] Performance is acceptable
Test Cases
- Basic Functionality: Standard input → Expected output
- Edge Case: Invalid input → Graceful error handling
- Performance: Large dataset → Acceptable processing time
Lifecycle Status
- Current Stage: Draft
- Next Review Date: 2026-03-06
- Known Issues: None
- Planned Improvements:
- Performance optimization
- Additional feature support