MiimansaAI ingests physician notes, trial protocols, and case narratives — tagging clinical entities, detecting contextual traits (Negation, Temporality, Certainty), and normalizing to ICD-10, SNOMED CT, RxNorm, & LOINC.
MiimansaAI compresses complex clinical entity extraction into a streamlined 3-stage flow for enterprise developers and data management teams.
Stream raw clinical text, upload PDF/DOCX protocols, or pipe HL7 CDA / FHIR narratives directly via REST API. Automatic PHI detection prepares documents for secure processing.
Deep clinical NER model tags medical spans and resolves traits (Negation, Temporality, Certainty). Automatically links extracted concepts to canonical ICD-10, SNOMED, & RxNorm codes.
Output validated data as HL7 FHIR R4 resource bundles, CSV tables, or JSON webhooks directly into your CTMS, EDC, EDC database, or enterprise data warehouse.
Don't settle for arbitrary AI tags. MiimansaAI maps every extracted phrase directly to standard medical terminologies recognized by clinical reviewers, regulators, and EHR platforms.
Extracts acute, chronic, and differential diagnoses with severity and staging context.
Captures brand/generic names, strength, administration route, frequency, and discontinued status.
Normalizes blood counts, CT scans, biopsies, and surgical interventions to canonical codes.
Maps precise anatomical locations (e.g. "proximal left anterior descending artery").
Suppresses names, dates, phone numbers, and record IDs prior to external processing.
A naive tagger mistakes "no evidence of infection" for an active infection. MiimansaAI attaches contextual trait metadata to every entity span, preventing critical diagnostic errors downstream.
Instead of competing with general cloud vendors on infrastructure scale, MiimansaAI focuses on specialized, bottlenecked workflows in clinical research and pharmacovigilance.
Accelerate protocol drafting for Biotech & CRO medical writing teams. Automatically verify entity consistency, exclusion criteria, and terminology standardization.
Automate MedDRA and ICD-10 coding from unstructured case safety reports (ICSRs). Instantly parse drug name, severity, reaction timeline, and causality.
Screen EHR unstructured notes to identify trial candidates and curate real-world evidence (RWE) registries with high precision.
While general-purpose LLMs struggle with hallucinations, negation flips, and non-standard medical abbreviations, domain-trained clinical NLP models achieve state-of-the-art precision on medical NER, SNOMED mapping, and HIPAA de-identification tasks.
Flexible per-document pricing designed for CROs, biotechs, and health systems.
For small teams & protocol pilots
For CROs & clinical data teams
For Pharma & Health Systems
Request a 1-on-1 walk-through with our clinical NLP specialists and get instant sandbox API keys.