Amazon Comprehend Medical is a HIPAA-eligible natural language processing service from AWS that reads unstructured medical text and turns it into structured data. Pre-trained machine learning models pick out conditions, medications, procedures and diagnoses from sources such as doctors' notes, clinical trial reports and radiology reports, then map them to ICD-10-CM, RxNorm and SNOMED CT codes. A separate PHI detection API finds and tags the 18 protected health information fields defined by Safe Harbor. Billing is usage based: NERe costs $0.01 per 100-character unit, tiering down to $0.005 and $0.001 at higher monthly volumes, and new users get a free tier of 85,000 units during the first month.
Amazon Comprehend Medical suits engineering and data teams at hospitals, health systems, payers, pharmaceutical companies and clinical research organizations. A developer at a health-tech startup can call the API to code clinical notes without training a model from scratch. Compliance and privacy officers can use the PHI detection API to find identifiers before data is shared. Data scientists building population-health or cohort-selection pipelines get structured entities and relationships from free text. Since access is through APIs and the AWS Pricing Calculator, it fits teams that already work inside AWS and can estimate cost per chart.
Typical scenarios include insurance claim processing, where claim capture, validation and approval workflows are automated for healthcare insurers. Population health groups analyze unstructured records to spot gaps in care and improve hospital efficiency. Drug safety teams run pharmacovigilance work by rapidly identifying adverse effects of pharmaceutical products to meet regulatory requirements. Clinical trial teams perform medical cohort analysis to select the right group of patients faster and at lower cost. A pilot of 1,000 five-page charts, each about 1,700 characters per page, comes to 85,000 units, which gives a concrete way to size a first project before moving to production volumes.
