Explore how MiimansaAI transforms messy, unstructured clinical text into verified, standard-coded data — from ingestion and deep trait detection to human validation and FHIR export.
Every document passes through five rigorous processing layers designed for enterprise accuracy and compliance.
Accepts raw clinical text, uploaded PDFs, DOCX protocol drafts, HL7 CDA documents, or streaming REST API payloads. Normalizes text layouts while flagging initial PHI boundaries.
Specialized clinical NER model detects entity span boundaries across Diagnoses, Medications, Tests/Procedures, Anatomy, and PHI. Simultaneously runs trait detection for Negation, Temporality, Certainty, and Subject.
Maps each extracted entity to canonical concept identifiers in ICD-10-CM, SNOMED CT, RxNorm, and LOINC. Resolves clinical acronyms, abbreviations, and synonyms using candidate generation and vector similarity.
Enterprise buyers always ask: "What happens when the model is uncertain?" MiimansaAI includes a built-in reviewer workspace. Low-confidence extractions (<90%) are queued for one-click confirmation or override by clinical data managers.
Serializes verified data into HL7 FHIR R4 resource bundles (Condition, MedicationStatement, Observation), CSV spreadsheets, or JSON webhooks into downstream CTMS, EDC, or EHR databases.
Keyword taggers miss context. MiimansaAI's trait layer determines whether an entity is affirmed, negated, past history, or assigned to a family member.
Identifies negative scope trigger words ("no evidence of", "denies", "rules out") to prevent false-positive diagnostic coding.
Classifies time boundaries: active admission diagnosis, historical medical condition, scheduled future procedure, or discontinued medication.
Detects clinical hedging ("possible", "likely", "suspected") vs definitive confirmed diagnoses.
Distinguishes conditions belonging to the subject patient vs family history (e.g. maternal history of cancer).
No AI is 100% perfect on ambiguous medical narratives. Enterprise buyers need transparency and control over what touches production databases.
Simple HTTP requests in, structured FHIR & JSON response out.
curl -X POST https://api.miimansa.ai/v1/extract \
-H "Authorization: Bearer sk_live_89a02f9021a" \
-H "Content-Type: application/json" \
-d '{
"document_type": "protocol_draft",
"text": "Patient has history of Stage IV NSCLC. No evidence of active brain metastases.",
"output_format": "fhir_r4",
"redact_phi": true
}'Given that compliance is the top adoption barrier for healthcare IT leaders, MiimansaAI builds security directly into the pipeline core.
Detects and suppresses all 18 HIPAA Protected Health Information (PHI) identifiers prior to passing clinical text to entity extraction models.
Sign Business Associate Agreements (BAAs) for Team & Enterprise tiers. Deployable in dedicated AWS/Azure VPCs or isolated on-premise enclaves.
Immutable audit logs track every document ingestion, model confidence score, human review modification, and FHIR export event.