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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.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Integrate AI detection into PACS workflow. Also use when setting up, configuring, or optimizing AI detection systems for medical imaging. Also covers Aidoc, Nvidia Clara, Zebra Medical, MaxQ AI, and Qure AI integration.
QA AI outputs, detect false positives/negatives, and validate AI results. Also use when evaluating AI system performance, reviewing AI-assisted findings, or conducting quality assurance on AI detection and reporting tools.
Guidance for AI-assisted structured reporting tools. Also use when the user mentions AI reporting, automated templating, speech-to-report, or wants to configure or optimize AI-assisted radiology reporting systems (RadAI, Abba, DeepRad).
Use LLM APIs for radiology tasks. Also use when integrating medical LLMs (MedPaLM, MedLM, Google Health, Amazon HealthLake) for report analysis, clinical reasoning, or radiology AI workflows.
Assesses medical image quality against clinical standards and identifies optimization opportunities. Use when user mentions "image quality audit", "artifact review", "dose analysis", "protocol deviation", "quality metrics", "diagnostic adequacy", or "technique optimization".
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.
| name | radiology-metrics |
| description | 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. |
metric_type (required): Type of metrics to retrieve
turnaround_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 volumestime_range (optional): Analysis period - defaults to last 30 daysgroup_by (optional): Segmentation - radiologist, modality, location, urgencycompare_previous (optional): Include period-over-period comparisonReturns structured JSON with:
metric_type: turnaround_time
time_range: last_month
group_by: [modality, urgency]
metric_type: productivity
time_range: last_quarter
compare_previous: true