💊 AI Dosing Error Pattern Recognition Simulator

Prescription Entered Into the Dispensing System

Every dose, unit, and frequency starts as raw entered text.

• 420: Median entries per pharmacy/day (prescriptions processed)
• 1 in 200: Manual entry error rate (before AI review)
• ~15%: Decimal-point error share (of dosing errors)
• 6: Fields captured per order (dose, unit, freq, route, weight, form)

Structured capture of the order

Dose, unit, frequency, and route are parsed into fields.

Why entry is the highest-risk step

Most dosing errors originate at transcription, not calculation.

Comparing Against Known Dosing-Error Patterns

The AI checks the order against a curated library of past errors.

• 1,200+: Error patterns in library (curated signatures)
• 3: Pattern categories (decimal, unit, weight-based)
• Weekly: Library update cadence (new cases added)
• <80ms: Lookup latency (per order)

Signature matching approach

Each order is embedded and compared to known error vectors.

False positive control

Only high-similarity matches surface for review.

Scanning for 10× Decimal Shifts and Unit Confusion

The model tests whether the dose looks like a shifted decimal or swapped unit.

• 10×: Common decimal shift (most frequent multiple)
• mg ↔ mcg: Unit-pair confusions (most frequent swap)
• 94%: Detection sensitivity (on labeled test set)
• 0.3s: Median scan time (per prescription)

Decimal-shift detection

Dose is checked against 0.1× and 10× neighbors of the norm.

Unit confusion detection

mg, mcg, and mL are cross-checked against drug defaults.

Verifying Pediatric Dose Against Patient Weight

Weight-based doses are recalculated and compared to the order.

• ~80%: Pediatric orders weight-based (of dosing)
• ±10%: Typical mg/kg tolerance (accepted variance)
• 1 in 500: Weight-mismatch error rate (orders flagged)
• <1s: Recalculation time (per order)

Recomputing the mg/kg dose

Entered dose divided by recorded weight, compared to norms.

Stale-weight risk

Outdated weight entries silently distort safe dosing.

Matching Error Pattern Identified and Flagged

A confident match routes the order to a pharmacist before dispensing.

• 85%: Auto-flag confidence threshold (triggers hard stop)
• ~40%: Pharmacist review time saved (vs manual triage)
• 1 in 3,000: Reported error interceptions (orders processed)
• <1s: Time to flag (end-to-end)

Routing the flagged order

High-confidence matches are held from dispensing automatically.

Human-in-the-loop confirmation

A pharmacist confirms or overrides before release.