How does AI extract data from a pre-op clearance document?

AI extracts data from clearance documents using a combination of OCR (for scanned PDFs and phone photos), document layout parsing, and large language model interpretation. Structured fields (lab values, medication names with doses, ICD codes) are extracted with field-aware parsing. Unstructured narrative (the H&P body, specialist notes, discharge summaries) is interpreted by the LLM with explicit instructions to identify ASA-relevant comorbidities, current medications with hold implications, and recent events. The output is a structured patient record that downstream clinical rules apply.

How does AI assign an ASA classification?

AI assigns ASA Physical Status Classification by applying the same criteria the ASA defines: functional limitation, severity of systemic disease, and acuity. The model evaluates each documented comorbidity for whether it's controlled or uncontrolled, mild or severe, and whether it substantively limits activity. Borderline ASA II vs III cases are flagged with rationale. The clinician reviews and can override. Per published validation work, AI ASA assignment matches anesthesia provider assignment in a majority of cases, with the borderline ASA II/III calls being where most disagreement happens — exactly where a clinician should be looking anyway.

Is AI pre-op charting accurate?

Accuracy depends on the quality of the input documents and the specificity of the AI's training. For structured data extraction (medication names, lab values, allergies) accuracy is consistently high. For clinical judgment calls (ASA classification, cancellation-trigger identification, recommended workup) accuracy depends on how well the model has been validated against real cases. Look for tools with public validation studies on real-world clearance data, not synthetic benchmarks. MyPreOp.ai publishes a live validation study with methodology and outcome counts.

AI Anesthesia Pre-Op Charting: How It Actually Works (2026)

Under the hood of AI pre-op charting in 2026. The five stages a clearance document goes through — and what each one is actually doing.

Most anesthesia AI demos hand-wave through the part where the magic happens. Here's the actual pipeline a clearance document goes through, with no marketing varnish.

Stage 1: Document ingestion

A clearance comes in as a PDF, a phone photo, or a fax converted to PDF. Stage 1 normalizes the file: OCR for scanned content, layout extraction for typed documents, image enhancement for low-quality phone photos. The output of stage 1 is searchable text with structural metadata (this is the H&P heading, this is the medication list section, this is a lab table).

Stage 2: Structured field extraction

Structured fields — lab values, medication names with doses, ICD codes, allergies — are extracted with field-aware parsing. This is where the lab fishbone gets populated: WBC, Hgb, Hct, plt for the X; Na/K/Cl/CO2/BUN/Cr/glucose for the BMP grid; PT/INR/PTT for the coag Y. This stage is closer to traditional NLP than to LLM work — high reliability, narrow scope.

Stage 3: Clinical narrative interpretation

The H&P body, specialist notes, and discharge summaries are interpreted by an LLM with explicit instructions to identify ASA-relevant comorbidities, hold implications for current medications, recent events that affect anesthetic plan, and any explicit specialist recommendations. This is the stage where the model's clinical training actually matters — generic LLMs without anesthesia tuning miss the things that matter (GLP-1 hold timing, the three G's for bleeding risk, the ASA II/III cusp criteria).

Stage 4: Clinical rule application

With a structured patient record in hand, the system applies clinical rules: hemoglobin cutoffs by case type, GLP-1 hold verification, BP control assessment, urinalysis screening before implant cases, anticoagulant hold protocol checks. Each rule emits a flag with severity (red, yellow, green) and a rationale. The rule set is the product of years of guideline curation (ASA, ACC/AHA, ASRA) — this is where domain knowledge separates real anesthesia AI from generic medical AI.

Stage 5: Verdict synthesis and two-section PDF

The aggregated flags drive the final verdict: Cleared, Cleared with Conditions, or Not Cleared. The PDF generates two sections: plain language for the coordinator ("please verify the patient held Ozempic for 14 days") and clinical detail for the anesthesia provider (rationale, citations, recommended pre-op orders). The Anesthesia Pre-Op Form is populated from the same structured record in parallel.

Stage 6 (manual): Clinician sign-off

The provider reviews the output. Every flag can be overridden. The verdict can be revised. The chart is signed by a human under the human's license. This is the stage that AI tools cannot and should not skip.

If you want to see this end-to-end on a real case, sign up for MyPreOp.ai and upload one clearance. See the platform overview, the live validation study, or read the walkthrough of the AANA P-1 pre-op form.