⚠️ Physician-Reported LLM Error Pattern Simulator

Physicians Report Errors Caught in LLM-Generated Content

Frontline clinicians catch mistakes during routine chart and note review.

• 1,200+: Reporting physicians tracked (placeholder network figure)
• 2.4: Avg. reports per physician/mo (short placeholder note)
• 6: Tools covered (placeholder tool count)
• <5 min: Median time to report (placeholder capture speed)

Where errors get caught

Errors surface during routine chart review, not audits.

Reports Accumulate Into a Shared Pattern Database

Each report becomes one row in a growing shared dataset.

• 8,400+: Reports logged to date (placeholder cumulative count)
• 34: Sites contributing (placeholder site count)
• 9: Fields per report (placeholder schema size)
• ~12%: Dedup rate (placeholder overlap figure)

Building the shared record

Raw reports are normalized into one structured table.

Reports Sorted Into Recurring Error-Type Buckets

Free-text reports are mapped onto five standing categories.

• 5: Category buckets (placeholder taxonomy size)
• 0.81 κ: Inter-rater agreement (placeholder agreement stat)
• ~60%: Auto-tagged share (placeholder automation rate)
• ~40%: Manual review queue (placeholder queue share)

The five recurring buckets

Dosage, interactions, guidelines, diagnosis, and citations.

Category Frequency Reveals the Most Common Error Types

Counting reports per bucket exposes the dominant failure mode.

• Dosage Errors: Leading category (typical) (placeholder ranking note)
• ~30%: Top-category share (placeholder concentration stat)
• Weekly: Trend refresh cadence (placeholder refresh cadence)
• Z > 2: Outlier detection (placeholder threshold note)

Reading the frequency chart

Bar size and color together flag priority categories.

Pattern Data Directs Review Scrutiny and Model Fixes

Highest-frequency, highest-risk buckets get review priority first.

• 2: Priority categories flagged (placeholder priority count)
• +35%: Review time reallocated (placeholder reallocation stat)
• 18: Model fix tickets opened (placeholder ticket count)
• -25%: Repeat-error reduction target (placeholder target figure)

Closing the loop

Findings feed both review checklists and model updates.