Radiology metrics
Skill aizech/clinical-skills/skills/analytics-quality/radiology-metrics
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.
npx -y skills add aizech/clinical-skills --skill radiology-metricsAssembled 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
Copied from the file, not written here
Retrieves and analyzes operational metrics from radiology information systems. Use when user mentions "radiology KPIs", "productivity metrics", "turnaround time analysis", "workload distribution", or needs operational analytics.
SKILL.md
1.9 KB, as published. Nobody here has run it
Radiology Metrics Skill
Triggers
- "radiology KPIs"
- "productivity metrics"
- "turnaround time analysis"
- "workload distribution"
- "RVU tracking"
- "department dashboard"
- "volume forecasting"
Parameters
metric_type(required): Type of metrics to retrieveturnaround_time- Report TAT analysisproductivity- Studies per radiologist, modality countsutilization- Equipment and room utilization ratesquality- Accuracy, discrepancy, critical result ratesfinancial- RVUs, revenue, cost per studyvolume- Historical and forecasted volumes
time_range(optional): Analysis period - defaults to last 30 daysgroup_by(optional): Segmentation - radiologist, modality, location, urgencycompare_previous(optional): Include period-over-period comparison
Data Sources
- RIS (Radiology Information System) for report data
- PACS for study volumes and workflow timestamps
- Scheduler for utilization and capacity data
- Historical databases for trend analysis
Output Format
Returns structured JSON with:
- Primary metrics (requested type)
- Trend indicators (vs. previous period)
- Benchmark comparisons where available
- Anomaly alerts for statistical outliers
- Recommended actions for improvement areas
Usage Examples
metric_type: turnaround_time
time_range: last_month
group_by: [modality, urgency]
metric_type: productivity
time_range: last_quarter
compare_previous: true
Error Handling
- Missing RIS data: Report partial results with data gap indicators
- Insufficient history: Expand time range suggestion
- Permission denied: Request escalated access with justification