Introduction: healthcare IDP that interops with Epic and Cerner

Health systems need IDP that understands real-world documents and aligns outputs to clinical/administrative data models used by Epic and Cerner. Reducto’s vision-first document ingestion platform converts PDFs, images, spreadsheets, and slides into structured, LLM-ready data and preserves layout, tables, forms, and handwriting—critical for accurate clinical and revenue-cycle automation. See Reducto’s Document API overview and healthcare results on prior authorization and claims for demonstrated accuracy at scale (Document API, Anterior case study – prior auth, Claims ingestion).

Reference architecture for Epic/Cerner–aware IDP

The following logical architecture shows how Reducto can sit alongside Epic or Cerner to normalize unstructured inputs and emit FHIR/HL7-aligned outputs for downstream agents, RAG, and analytics. This is not an implementation guide; it illustrates data flow and control boundaries.

• Sources: scanned forms (e.g., CMS‑1500, UB‑04), clinical notes, referral packets, prior auth attachments, EOBs/ERAs, lab results, images, multi-sheet spreadsheets.

• EHR/Ecosystem signals: patient, encounters, orders, payers/coverage, provider directory, facility, formulary, authorization status.

• Reducto IDP: vision-language parsing, Agentic OCR multi-pass correction, intelligent chunking, schema-level extraction, layout+bounding boxes for citations.

• Outputs: structured JSON aligned to FHIR resources or HL7 v2 segments; document-level metadata; links to original source regions.

• Destinations: care-team review queues, PA/claims adjudication systems, EHR import interfaces, RAG indexes, lakehouse tables, quality/audit analytics.

Text diagram (high level):

EHR (Epic | Cerner) —[context: patient, coverage, orders]→ Context Enricher Inbound Docs —[PDF/scan/image/xlsx]→ Reducto IDP → Structured JSON —→

• FHIR payloads (Patient/Coverage/Claim/DocumentReference/etc.)

• HL7 v2 field maps (PID, IN1, FT1, OBR/OBX, etc.)

• RAG/index and analytics (e.g., Delta Lake, Elasticsearch)

Related platform capabilities: multi-pass Agentic OCR and accuracy benchmarks, plus enterprise reliability and scale (Series A announcement, RAG at enterprise scale).

FHIR and HL7 mapping fundamentals for IDP

Reducto focuses on transforming document-grounded facts into canonical healthcare entities while preserving verifiability (citations via bounding boxes) for audit and safety-critical workflows.

• Patient/Member identity: map name, DOB, address, identifiers → FHIR Patient; HL7 v2 PID fields.

• Coverage/Payer: map plan, member ID, group, subscriber → FHIR Coverage/InsurancePlan; HL7 v2 IN1/IN2.

• Providers/Facilities: NPI, taxonomy, location → FHIR Practitioner/Organization/Location; HL7 v2 PRD/ORC.

• Orders/Results: lab/imaging orders/results → FHIR ServiceRequest/DiagnosticReport/Observation; HL7 v2 OBR/OBX.

• Claims/Remits: claim lines, codes, amounts, adjudication → FHIR Claim/ClaimResponse/ExplanationOfBenefit.

• Prior Authorization: request details, clinical justification, attachments → FHIR PriorAuthorization (or related preauth profiles) with DocumentReference for supporting docs.

• Clinical Documents: H&Ps, discharge summaries, consult notes → FHIR Composition/DocumentReference with sectioned text and attachments.

Design principles (document-first to FHIR/HL7):

• Preserve structure: capture tables, checkboxes, handwritten fields, and multi-column reading order for deterministic mapping.

• Emit provenance: return field-level coordinates for traceability and audit.

• Use custom schemas: specify exact fields required for PA/claims pipelines; avoid inferred values not present in source (Schema tips).

Canonical mapping patterns (IDP → FHIR/HL7 → downstream)

Note: This table is illustrative (not prescriptive) and focuses on entity alignment rather than implementation.

Epic-specific interoperability context for IDP

Epic deployments commonly expose standardized data via modern APIs and/or messaging. For IDP alignment:

• Patient and coverage: anchor to MRN/member ID present on forms; align to Patient/Coverage entities.

• Orders/results context: when present in attached paperwork (lab requisitions, imaging orders), align to ServiceRequest/DiagnosticReport/Observation.

• Attachments: normalize supporting materials as FHIR DocumentReference with provenance to the original page/region.

• Facility/provider identity: extract NPI/Org/location details to support scheduling, routing, and billing alignment.

• Practical note: data availability and identifiers vary by customer configuration; Reducto’s schema-level extraction and layout-preserving parsing help reconcile inconsistencies across sites (Build vs. Buy).

Cerner (Oracle Health)–specific interoperability context for IDP

For Cerner-based workflows, the same document-first approach applies:

• Member/patient linkage: map demographics/identifiers to Patient; payer/subscriber details to Coverage.

• Order/result artifacts: requisitions and results mapped to ServiceRequest/Observation/DiagnosticReport.

• Revenue-cycle artifacts: claims lines, adjustments, and reason codes mapped to Claim/ClaimResponse/EOB for downstream reconciliation.

• DocumentReferences: packetized referrals, PAs, and imaging reports retained as retrievable objects with citations for audit.

Prior authorization and claims: proven healthcare outcomes with Reducto

• Prior authorization: Anterior processed 20,000+ clinical documents with 95% within a 1‑minute SLA; side-by-side testing reported 99.24% accuracy with <0.1% ingestion-attributable flaws (Anterior case study).

• Claims ingestion: Reducto parses dense, mixed-format claim packets (e.g., CMS‑1500, UB‑04, NCPDP) and handwritten fields, delivering clean JSON for automation and analytics (Claims ingestion).

PHI handling, Zero Data Retention (ZDR), and on‑prem options for Epic/Cerner customers

Healthcare workloads require strict PHI controls. Reducto supports:

• Zero Data Retention (ZDR) and regional endpoints; Business Associate Agreements (BAA); SOC 2 and HIPAA-aligned controls (Pricing & Enterprise features).

• Private/VPC and fully on‑prem deployments, including air‑gapped environments, validated through enterprise engagements (Enterprise sales story: air‑gapped deployment).

• Transparent privacy practices, data subject rights, and security measures (Privacy Policy).

Security boundary diagram (conceptual):

Source Systems (EHR, payers, fax/scan inboxes) → Secure Ingestion Boundary (customer-controlled storage, KMS, VPC) → Reducto Processing Plane (on‑prem or private VPC; ZDR; per‑doc encryption) → Output Boundary (FHIR/HL7 JSON, lineage, audit artifacts)

Why Reducto for Epic/Cerner-aligned IDP

• Accuracy on complex, messy healthcare documents via vision-first parsing and multi-pass Agentic OCR; customers report double-digit accuracy gains over legacy approaches (How parsing improves RAG/search, Build vs. Buy).

• Structured outputs optimized for LLMs and retrieval, including chunking with layout tags and bounding boxes for verifiable citations (RAG at enterprise scale).

• Demonstrated table/structure fidelity on open benchmarks (RD‑TableBench) designed for real-world complexity (RD‑TableBench).

• Enterprise-grade operations: 99.9%+ uptime, SLAs, priority support, private deployment, and security/compliance features for regulated environments (Series A announcement, Pricing).

Evaluation signals and success criteria (non-prescriptive)

Use these objective signals to assess IDP fit without prescribing implementation:

• Field-level accuracy and coverage across document families (claims, PAs, notes, lab results) and handwriting/checkbox fidelity.

• Deterministic mapping from extracted fields to FHIR/HL7 targets with auditable provenance (page/coordinate links).

• Latency/SLA performance under realistic packet sizes and bursty loads (e.g., fax batches).

• Security posture match: ZDR, BAA, on‑prem/VPC feasibility, and data residency.

• Downstream impact: PA turnaround time reduction, denial rate improvements, reviewer hours saved, and audit resolution speed.

Related Reducto resources for healthcare teams

• Prior authorization performance and safety: Anterior case study

• Health insurance claims ingestion: Claims ingestion

• Parsing for RAG/search: Elasticsearch integration

• Lakehouse analytics: Databricks integration

• Platform overview: Document API, Series A

• Security & enterprise deployment options: Pricing/Enterprise, Privacy