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Report quality review

Skill aizech/clinical-skills/skills/analytics-quality/report-quality-review

A collection of AI agent skills focused on medical imaging and healthcare workflows. Built for radiologists, healthcare IT professionals, and researchers who want AI coding agents to help with imaging workflows, clinical documentation, AI integration, and medical research. Works with Claude Code, Codex, Cursor, Windsurf, and many other agents.

Install
npx -y skills add aizech/clinical-skills --skill report-quality-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Monitors and improves radiology report quality through systematic audit and feedback. Use when user mentions "report quality review", "discrepancy audit", "report completeness", "addendum analysis", or needs quality assurance.

SKILL.md

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Report Quality Review Skill

Triggers

  • "report quality review"
  • "discrepancy audit"
  • "report completeness"
  • "addendum analysis"
  • "quality improvement"
  • "peer review"
  • "report turnaround"

Parameters

  • review_type (required): Type of quality review
    • completeness - Required element adherence
    • discrepancy - Error and miss analysis
    • turnaround - TAT compliance monitoring
    • communication - Critical result documentation
    • attribution - Report signature verification
    • cqi - Continuous quality improvement tracking
  • modality (optional): Filter by imaging type
  • time_range (optional): Review period - defaults to last 30 days
  • urgency (optional): Filter by clinical setting (ED, inpatient, outpatient)

Quality Metrics Tracked

  • Completeness: Required elements, comparison documentation, impression presence
  • Accuracy: Discrepancy rates, addendum rates, amended findings
  • Timeliness: TAT by setting, protocol compliance, pending report alerts
  • Communication: Critical result documentation, escalation compliance
  • Format: Structured data presence,标准化 terminology use

Output Format

Returns structured JSON with:

  • Quality score by metric category
  • Trend analysis (improving/declining)
  • Individual radiologist feedback (anonymized aggregates)
  • Improvement recommendations
  • Peer review learning points

Usage Examples

review_type: completeness
modality: CT
time_range: last_month

review_type: discrepancy
time_range: last_quarter
urgency: ED

Integration Points

  • RIS for report content and timestamps
  • PACS for comparison study tracking
  • Communication logs for critical result verification
  • Peer review system for discrepancy classification

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.