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:

1. Study Overview Card - Study ID, name, status
2. Recruitment Progress - Overall progress bar, site-by-site progress comparison
3. Subject Distribution - Gender, age distribution pie charts
4. AE Monitoring - Adverse event incidence rate, severity distribution
5. Data Quality - CRF completion rate, query count
6. 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

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. 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