ABDM Ecosystem Engine FHIR R4 Standard

Technical Product Architecture & Data Pipeline

EntheoryAI transforms siloed, multi-modal oncology records into clean, coded, conflict-checked clinical context. Here is the deep technical specification of how our 5-phase pipeline operates.

Deep Engine Architecture

The 5-Phase EntheoryAI 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 (PHR address) or hospital MRN. Initiates automated consent artifact generation with NHA ABDM Consent Manager (HIP).

Input: ABDM ID (`patient@abdm`), Mobile OTP, Consent Artifact

Output: Authorized Data Pipe with active cryptographic token

Method: ABDM M1/M2 API handshake & RSA-256 signed payload

Taxonomy: Demographic & Consent Artifacts

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: Multi-hospital APIs, DICOM-Web, PDF documents

Output: Raw fragmented data bundle with source attribution

Method: Asynchronous streaming fetchers with retry logic

Taxonomy: Clinical Notes, DICOM Metadata, Lab Values

Phase 3 (Core Differentiator)

Process, Normalize & 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 DICOM/LOINC feeds

Output: Coded FHIR Bundle with highlighted conflict alerts

Method: LayoutLMv3 OCR + SNOMED concept 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 Summary Card

Method: Fine-tuned Medical LLM with confidence scoring

Taxonomy: Clinical Summaries & Genomic Panels

Phase 5

Export & Hospital System Integration

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 report

Method: Encrypted HTTPS REST endpoints & S3 signed URL export

Taxonomy: Full Export Bundle

Trait Layer Engine

Beyond Naive Data Aggregation

Every data point in EntheoryAI carries 5 core metadata traits — crucial for serious technical buyers.

1. Data Source & Provenance

Tracks exactly which hospital, imaging center, or diagnostic lab generated the record, including ABDM HIP ID and digital signature.

2. Confidence & Verification Status

Distinguishes auto-extracted OCR entities from physician-verified entries with explicit 0–100% confidence scores.

3. Temporal Context & Alignment

Establishes exact procedure timestamps, flagging historical vs current active results across multi-year care episodes.

4. Automated Staging Conflict Detection

Detects direct contradictions between clinical notes, pathology findings, and radiology DICOM reports (e.g. Stage III vs Stage IV).

Developer & Integration Playground

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"
        }
      ]
    }
  ]
}

Security Commitments

Enterprise Security & ABDM Compliance

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

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India Data Residency

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

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ABDM Consent First-Class

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

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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.