Extracts text from a document (PDF or image) and converts it into multiple FHIR resources,
returned as a transaction Bundle. Combines document text extraction with multi-resource detection.
Automatically detects Patient, Condition, MedicationRequest, Observation, and other resource types.
Resources are linked with proper references (e.g., Conditions reference the Patient).

Patient identifier handling. US Core requires Patient.identifier (a business identifier such as an MRN). When the source text contains an identifier, it is extracted with an appropriate URI system. When the source text does not contain a detectable identifier, a synthetic one is generated with system: "urn:phenoml:lang2fhir-generated-id" and a UUID value so the bundle remains FHIR-valid and US Core conformant. Callers who need a tenant-specific namespace should rewrite the synthetic system after extraction.

Split classifications (optional). config.split_classifications is a caller-defined list, not a fixed taxonomy. Choose each classification id and write a natural-language description for the per-page classifier. For each page, the classifier assigns the best-matching classification or leaves the page ungrouped. Classifications with operation: "group" keep matching pages and label resources extracted from those pages; classifications with operation: "drop" remove matching pages before extraction. The clinical and admin ids in the example are illustrative, not a fixed set.