MediFlow — Clinical Document Intelligence & FHIR Pipeline

Clinical environments face immense documentation overhead, with physicians averaging 4+ hours daily on manual EHR data entry. Clinical notes arrive as unstructured PDFs, scanned handwritten forms, and discharge summaries from disparate external EHR systems using incompatible schemas. Manual transcription teams struggle with error rates, HIPAA compliance overhead, and multi-day referral processing delays. A production system must reliably extract complex medical entities, disambiguate clinical terms, and write structured FHIR R4 resources directly back to target EHRs with zero data loss.

MediFlow is a three-stage NLP pipeline purpose-built for clinical document processing. Stage 1 (Ingestion): Documents arrive via HL7 FHIR R4 API endpoints, secure SFTP drops, or direct uploads. A document classifier routes each file through OCR or direct text extraction. Stage 2 (Extraction): A hybrid NLP engine combines spaCy's medical NER models — trained on i2b2 and MIMIC-III corpora — with Claude API for complex clinical reasoning tasks like medication reconciliation, ICD-10 code suggestion, and ambiguous lab value interpretation. Extracted entities are normalized against SNOMED CT and RxNorm vocabularies. Stage 3 (Write-Back): Structured FHIR R4 resources are validated against the HL7 specification and written back to the target EHR via authenticated API calls. Celery manages async processing queues so high-volume surges don't create backlogs.