Report quality review

Skill aizech/clinical-skills/.continue/skills/report-quality-review

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.From its SKILL.md

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

4 stars4 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.

SKILL.md

2.0 KB, 396 tokens by cl100k_base, as published. Nobody here has run it

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

What ships with it: 1 file

3.2 KB alongside SKILL.md

evals/

evals.json3.2 KB

Gives 0 of the 12 instructions most docs writing skills give in 396 tokens

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Announce the skill at startin 54 of 1637, across 26 files
Convert legacy doc files before editingin 45 of 1637, across 7 files
Predict questions readers might askin 42 of 1637, across 4 files
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Test the document with a fresh context-less instancein 42 of 1637, across 3 files
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Apply surgical edits during refinementin 41 of 1637, across 2 files
Offer structured workflow or freeformin 40 of 1637, across 1 file
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Said here and by no other author read

select a required review type
filter reviews by imaging modality
set review period to last 30 days by default
filter reviews by clinical urgency setting
track required elements and comparison documentation
track discrepancy and addendum rates

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.