ABDM M1 / M2 / M3 Compliant FHIR R4 Interoperable

Unified Oncology Context for Tumor Boards in under 60 seconds

EntheoryAI aggregates fragmented patient histories across EMR, PACS DICOM, and LIS lab networks into physician-ready clinical summaries with automated staging conflict detection. Designed specifically for Indian multi-hospital cancer centers.

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0 Data Leakage Cloud Architecture
93% Faster Pre-Board Prep
EntheoryAI Engine v2.4 • Tumor Board Simulator
🏥 Now Accepting Pilot Partners across Indian Multi-Hospital Oncology Networks
🇮🇳 Built for Indian Oncology Teams & ABDM Health Data Exchange Native
🔒 100% Data Residency in India (AES-256 Encrypted)

The High Cost of Fragmented Oncology Data

80% of healthcare data in India remains trapped in handwritten notes, scanned PDFs, and isolated hospital EMR silos.

80%
Unstructured Clinical Data

Physician notes, lab PDFs, and DICOM reports are unindexed and unusable without OCR extraction.

6–20m
Manual Prep Time Per Patient

Coordinators waste critical hours manually chasing medical records across multiple hospitals.

3–10h
Wasted Per Tumor Board

For 15–30 cases per session, oncology departments lose up to 10 hours in administrative prep.

The Full 5-Phase Data Pipeline

Every patient record undergoes rigorous provenance tracking, consent validation, and normalization.

Phase 1

Link & Consent Engine

ABDM Consent Manager Integrated

Accepts patient ABDM Health ID (`patient@abdm`) or hospital MRN. Initiates automated consent artifact generation with NHA ABDM Consent Manager (HIP).

Input: ABDM ID (`patient@abdm`), OTP
Output: Authorized Data Pipe Token
Method: ABDM M1/M2 API handshake
Taxonomy: Demographics & Consent
Phase 2

Fetch & Aggregate Stream

Multi-Modal Ingestion

Pulls records concurrently across connected hospital EMR discharge summaries, CarePACS DICOM radiology studies, Suburban LIS lab systems, and uploaded historical PDFs.

Input: Hospital APIs, DICOM-Web, PDFs
Output: Raw fragmented data bundle
Method: Streaming fetchers with retries
Taxonomy: Clinical Notes, DICOM, Labs
Phase 3 (Core Differentiator)

Process, Normalize & Staging Conflict Engine

Automated Staging Conflict Flagging

Runs clinical OCR on unstructured notes, maps raw lab/radiology terms to LOINC & SNOMED CT codes, converts datasets into FHIR R4 resources, and executes cross-source conflict detection.

Input: Unstructured PDFs & raw feeds
Output: Coded FHIR Bundle with conflict alerts
Method: LayoutLMv3 OCR + SNOMED mapper
Taxonomy: ICD-10, SNOMED, LOINC, RxNorm
Phase 4

Summarize & Enrich Engine

Oncology Specialized NLP

Generates a physician-ready clinical summary card, chronological timeline of treatments, biomarker extraction (EGFR, ALK, PD-L1), and trial eligibility suggestions.

Input: Normalized FHIR dataset
Output: Tumor Board Brief + Biomarker Card
Method: Fine-tuned Medical LLM
Taxonomy: Clinical Briefs & Genomics
Phase 5

Export & Integration Stream

REST API / Webhooks / CSV

Delivers structured output directly to hospital EHR systems, tumor board presentation platforms, or CSV/JSON downloads with full provenance metadata.

Input: Final unified clinical context
Output: FHIR R4 JSON, REST API payload, CSV
Method: Encrypted HTTPS REST endpoints
Taxonomy: Full Export Bundle

Grounded in Global & Indian Data Standards

Every extracted clinical data point carries provenance, verification status, and consent traits.

Patient Demographics ABDM ID

Name, age, gender, MRN, national ABDM Health ID linking.

Source: Hospital EMR Status: Verified
Diagnosis & Condition ICD-10, SNOMED

Cancer subtype, TNM histology, staging notes, comorbidities.

Conflict Flagging Temporal History
DICOM Imaging DICOM, FHIR

CT, MRI, PET-CT scans, PACS metadata, radiologist impressions.

CarePACS DICOM-Web Slice Level Metadata
Lab & Tumor Markers LOINC

CEA, CA-125, CBC, liver/renal function, unit normalization.

Confidence: 99.4% LOINC Code Map

Inspect Native FHIR R4 & API Export Formats

Technical buyers can switch tabs below to see exact payload structures generated by EntheoryAI.

{
  "resourceType": "ImagingStudy",
  "id": "carepacs-ct-chest-88219",
  "meta": {
    "profile": ["http://hl7.org/fhir/StructureDefinition/ImagingStudy"]
  },
  "status": "available",
  "subject": {
    "reference": "Patient/rajesh-kumar-abdm",
    "display": "Rajesh Kumar (ABDM ID: rajesh.kumar@abdm)"
  },
  "started": "2026-07-18T10:14:00+05:30",
  "numberOfSeries": 3,
  "numberOfInstances": 142,
  "procedureCode": [
    {
      "coding": [
        {
          "system": "http://loinc.org",
          "code": "24627-2",
          "display": "CT Chest with contrast"
        }
      ]
    }
  ]
}

Enterprise Security & ABDM Compliance

Addressing the #1 adoption barrier for hospital IT leaders with native data residency and consent controls.

🇮🇳

India Data Residency

100% of data processing and encrypted storage resides on MeitY-empanelled India cloud servers (AWS Mumbai / Azure India Central).

🛡️

ABDM Consent First-Class

Built from day one around ABDM Health Information Provider (HIP) and User (HIU) consent artifact validation before touching any PHI.

🔒

HIPAA-Eligible Architecture

End-to-end encryption at rest (AES-256) and in transit (TLS 1.3), complete audit logging, and zero-retention RAM cache options on roadmap.

SaaS Pricing for Every Stage

Start with a pilot sandbox or scale across your multi-hospital network.

Starter

Small Clinics & Individual Oncologists

Free / Trial
  • Up to 15 patient summaries / mo
  • ABDM M1 Consent Verification
  • Standard PDF OCR Extraction

Enterprise

Hospital Networks & Large CROs

Custom
  • On-Prem / India VPC Deployment
  • Custom BAA & SLA Guarantees
  • Dedicated Clinical Support Engineer
View Full Feature Comparison & ROI Calculator →
Ready for Onboarding

Bring Unified Clinical Context to Your Next Tumor Board

Join forward-thinking Indian oncology departments eliminating record gathering friction and focusing on patient care.

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Built by health-tech operators & medical AI engineers in India • Read Founder's Story & Advisory Board →