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Dashboard Design For Trials

根据研究编号、中心数、目标/当前入组人数和不良事件数等参数，生成包含研究概览、入组进度、受试者分布、AE 监测、数据质量和时间线的 HTML 仪表盘布局草图；脚本使用 Python 标准库输出静态文件。

aipoch-ai
aipoch-ai
热度 83.13
最近更新 2026-05-11 17:33:38
分类：数据依赖：代码执行 / 读写文件应用场景：UI 原型 / 报告撰写 / 健康 / 模板

安装技能

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执行命令安装此 skill： npx secure-skills-cli add dashboard-design-for-trials
Metadata
name
dashboard-design-for-trials
description
Design dashboard layout sketches for clinical trials showing enrollment progress and adverse event rates
version
1.0.0
category
Visual
tags
[]
author
AIPOCH
license
MIT
status
Draft
risk_level
Medium
skill_type
Tool/Script
owner
AIPOCH
reviewer
last_updated
2026-02-06

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