Document type

Medical Record Intelligence

Extract clinical insights from medical documentation with AI designed for healthcare workflows.

At a glance

Process clinical documentation including notes, lab results, and medical records with HIPAA-compliant AI. OdysseyGPT handles medical records with citation-backed extraction, workflow-ready outputs, and review paths for low-confidence cases.

Key Takeaways

  • Common extraction targets include Patient demographics, Diagnoses and ICD codes, Medications and dosages.
  • Extract medical entities including conditions, medications, procedures, and anatomical terms.
  • Extract data for retrospective studies and clinical trials.

Common fields

  • Patient demographics
  • Diagnoses and ICD codes
  • Medications and dosages
  • Lab results and vitals
  • Procedures and CPT codes
  • Provider notes and assessments

Processing capabilities

  • Clinical NER: Extract medical entities including conditions, medications, procedures, and anatomical terms.
  • Code Mapping: Map extracted information to ICD-10, CPT, SNOMED, and other medical coding systems.
  • Lab Result Parsing: Structure lab results with reference ranges and flag abnormal values.
  • Medication Extraction: Capture drug names, dosages, frequencies, and routes of administration.
  • Timeline Construction: Build patient timelines from episodic records.
  • De-identification: Remove PHI for research and analytics use cases.

Questions answered

What should teams extract from medical records?

Start with Patient demographics, Diagnoses and ICD codes, Medications and dosages, Lab results and vitals, then expand into workflow-specific fields as your downstream systems require more structure.

What are the common risks when automating medical records?

Extract medical entities including conditions, medications, procedures, and anatomical terms. Map extracted information to ICD-10, CPT, SNOMED, and other medical coding systems.

What is the recommended automation flow?

Ingest the document, extract the fields that matter, route low-confidence outputs for human review, and publish the validated output into the target workflow or system of record.

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