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Medical record processing cost calculator

Extract structured data from clinical notes and EHR free text.

Medical record processing extracts structured data from clinical notes and EHR free text — diagnoses, medications, procedures, vitals — where a single patient record can run 20–28k tokens of dense clinical narrative, yielding the 90/10 input/output split and 32k context requirement. Output is a compact structured JSON payload per record.

Best-effort latency is acceptable for overnight batch pipelines, but extraction accuracy is non-negotiable. cachedPromptPercent of 55 captures the stable extraction schema and medical ontology mappings reused across every record. HIPAA compliance requires a signed BAA with your inference provider before processing real patient data — validate this before routing to any public API endpoint. Models with strong biomedical pretraining (Llama-3 70B+ fine-tunes, Mistral-Large) outperform general instruction models on ICD-10 and medication entity extraction. Quantized models should be validated carefully; hallucination rates on medical terminology can increase meaningfully at INT4.

Recommended models

Highest accuracy on biomedical entity extraction; minimizes hallucinated diagnoses in clinical text.
Strong 70B baseline for clinical NER; good tradeoff between accuracy and cost for overnight batches.
Low hallucination rate on structured extraction; reliable on medication and dosage field parsing.
Competitive long-context extraction accuracy; handles dense multi-section EHR notes well.
Strong structured output reliability at 32k context; cost-effective for high-volume medical batch jobs.