Executive Summary
Health systems have long modeled patients, encounters, orders, results, medications, and claims inside EHRs, LIS/RIS, and payer cores. That foundation remains essential, but care now spans wearables, telehealth, precision medicine, and AI assistants at the bedside and in the back office. This article reframes classic healthcare models into an AI-first, safety-first blueprint where facts feed features, features power models and agents, and every recommendation is governed, explainable, and auditable.
Principles for an AI-Native Healthcare Model
1. Productize canonical domains as data products
Publish governed, versioned data products with clear owners and SLAs instead of point-to-point feeds.
Patient/Person, Provider/Organization, Encounter/Episode
Orders (labs, imaging, procedures), Results/Observations
Medications (orders, administrations, reconciliations)
Diagnoses/Problem List, Allergies/Intolerances
Care Plans/Pathways, Referrals, Transitions of Care
Imaging Studies, Waveforms, Genomics/Omics
Revenue cycle (claims, remits), Authorizations
Devices & Remote Monitoring, Social Determinants of Health (SDOH)
Quality measures, Registries, Public health reporting
Each product exposes a contract (schema, semantics, quality bars, and refresh cadence) that is consumable by analytics, ML features, and LLM retrieval.
2. Separate facts, metrics, features, and narratives
Facts: immutable clinical and administrative records (e.g., resulted lab, signed note).
Metrics: curated business/quality logic (e.g., LOS, readmit rate, HEDIS).
Features: ML-ready signals (e.g., vitals trends, medication burden, SDOH risk index).
Narratives: LLM outputs with citations and redactions (e.g., discharge summary draft, denial appeal letter).
Track lineage across all layers to enable trust and traceability.
3. Event-centric by default
Model high-value moments— EncounterOpened , OrderPlaced , CriticalResultPosted , MedicationAdministered , Transfer , AlertAcknowledged , PriorAuthApproved . Events unlock streaming features (sepsis risk now, not tomorrow) and accurate replay.
4. Safety, privacy, and compliance built in
First-class fields for HIPAA/PHI classification, consent, purpose of use, retention, break-glass, jurisdiction, and audit trails. Guardrails apply to both predictive models and LLM tools.
Target Architecture: Lakehouse + Streams + Time-Series + Graph + Vectors
Lakehouse for durable facts from EHR, LIS/RIS, PACS, payer/clearinghouse; schema-evolved and versioned.
Streaming/time-series for vitals, device telemetry, remote monitoring, and alerts.
Knowledge graph connecting patients, encounters, providers, orders/results, medications, devices, payers, and community resources for RCA and care navigation.
Vector indexes for retrieval-augmented generation (RAG) over guidelines, policies, patient education, notes, imaging reports, payer rules.
Feature store (offline/online parity), Model/Prompt registry (versions, approvals, guardrails, tests).
Interoperability layer aligned to FHIR resources to publish/ingest safely.
Modernized Canonical Domains (Healthcare-Specific)
Patient, Identifiers, Consent
Unify person demographics, identifiers (MRN, MPI, payer IDs), proxies, and consent directives (research, sharing limits, revocations): track jurisdiction and masking policies.
Encounters, Episodes, and Locations
Represent ED visits, inpatient stays, ambulatory visits, and telehealth sessions; connect to rooms/units and care teams. Support episode-of-care rollups for condition pathways.
Orders, Results, Observations
Normalize labs, imaging, procedures, and vitals. Preserve reference ranges, method, device, and critical flags link to ordering intent and clinical indication for explainability.
Diagnoses, Problems, Allergies
Distinguish coded diagnoses (billing) vs. active problem list . Track allergy reactions and severities, connecting them to medication decision support.
Medications
Unify orders, administrations (MAR), reconciliations, formulary/coverage. Distinguish between dispensing and administering; record infusion rates and PRN logic.
Care Plans and Pathways
Model goals, interventions, tasks, and outcome measures. Emit events for adherence, variance, and escalations.
Imaging, Waveforms, and Omics
Reference DICOM studies/series/instances; waveform strips; variant calls and panels: store derivatives and AI results with provenance.
Revenue Cycle & Utilization
Authorizations, eligibility, claims (UB-04/837), denials/appeals, remits, DRGs, and case-mix indexes; map utilization to clinical context for value-based care.
Devices & Remote Monitoring
Track device identity, calibration, channels, and patient pairing; stream measurements with quality flags and lot/UDI provenance.
SDOH & Community Resources
Represent housing, food, transportation, caregiver status, and screening; connect referrals to community-based organizations and track outcomes.
AI-Native Entities to Add
FeatureDefinition: (id, owner, inputs_contracts, transform_ref, clinical_validation, bias_notes, tests)
FeatureValue: (entity_key, feature_id, ts, value, online/offline_source)
EmbeddingIndex: (index_id, domain: guidelines/policies/notes/payer_rules, dim, partitions)
EmbeddedChunk: (index_id, chunk_id, vector, text_ref, source_uri, redaction_policy)
PromptTemplate: (id, purpose, inputs_schema, guardrails_ref, evaluation_suite)
ToolDefinition: (id, name, contract, scope, PHI access policy, rate_limits)
ModelCard: (model_id, version, intended_use, training_refs, performance, risks, approvals)
ModelRun: (run_id, model_id, dataset_ref, metrics, calibration, fairness_audit, lineage_hash)
SafetyControl: (id, rule, trigger, action, override_policy, audit_ref)
These make predictive and generative systems traceable, testable, and governable.
High-Value AI Use Cases
1. Early Deterioration & Sepsis Sensing
Stream vitals/labs; compute trend features and calibration; surface risk with reason codes (lactate, MAP trajectory) and actionable steps aligned to sepsis bundles.
2. Imaging & Diagnostic Triage
Prioritize worklists (CT for stroke, PE) using model scores; store outputs as derived observations with links to source studies and reader feedback for iterative improvement.
3. Medication Safety & Reconciliation
Detect duplication, interactions, and renal/hepatic dose mismatches; reconcile discrepancies across inpatient, outpatient, and pharmacy claims; generate pharmacist notes with supporting citations.
4. Care Coordination & Discharge
LLM drafts discharge summaries and patient education using RAG over orders, results, and care plans; clinician reviews and signs. Track comprehension and readmission risk.
5. Prior Authorization & Denial Prevention
RAG over payer policies to assemble evidence; auto-draft prior authorizations and appeals with citations to notes, images, and guidelines; measure overturn rates.
6. Population Health & SDOH Navigation
Identify rising-risk cohorts, stratify them by unmet needs, and route referrals accordingly; measure engagement and outcome deltas.
Example Minimal Schemas (Illustrative)
// Observation (fact)
{ "obs_id":"LAB-88912", "patient_id":"P-1023", "encounter_id":"E-5510",
"code":"LOINC:4548-4", "display":"Hematocrit", "value":34.2, "unit":"%",
"ref":{"low":36,"high":46}, "effective":"2025-09-01T10:42:00Z", "critical":false }
// MedicationAdministration (fact)
{ "admin_id":"MAR-22018", "med_code":"RxCUI:617314", "dose":"2mg",
"route":"IV", "start":"2025-09-01T11:05Z", "end":"2025-09-01T11:20Z",
"patient_id":"P-1023", "encounter_id":"E-5510" }
// FeatureValue (online)
{ "entity_key":"patient:P-1023",
"feature_id":"v2_sepsis_risk_10min", "ts":"2025-09-01T11:30Z", "value":0.82 }
// Narrative with citations (LLM output)
{ "narrative_id":"discharge_draft_E-5510",
"citations":[{"type":"note","id":"NOTE-7712#A&P"},
{"type":"obs","id":"LAB-88912"},
{"type":"order","id":"ORD-33051"}],
"redactions":["names","address"] }
Metrics That Matter
Clinical: mortality, LOS, readmissions, time-to-antibiotics, guideline adherence, diagnostic turnaround.
Operations: ED boarding time, bed assignment latency, OR utilization, denial rate, and auth cycle time.
Experience: CAHPS/CG-CAHPS, portal engagement, time-to-message response.
AI Quality: AUC/PPV/NPV and calibration; segment fairness; feature freshness; RAG citation coverage; override/accept rates; red-team findings cleared.
Operating Model: MLOps + LLMOps + ClinOps
Contracts & tests for feeds, features, prompts, tools; golden test sets per pathway.
Lineage from raw facts to bedside recommendations; hash artifacts for audits.
Monitoring for data/feature drift, concept drift, hallucination risk, and PHI leakage.
Safety gates for high-risk actions; human-in-the-loop and break-glass logging.
Privacy & jurisdiction routing for cross-border processing and research vs. treatment.
Implementation Roadmap
Phase 1 (0–90 Days): Foundation
Stand-up lakehouse domains (patients, encounters, orders/results, medications), event capture (orders, critical results), feature store MVP (deterioration signals), first vector index (guidelines and policies), and dashboards tied to a single clinical pathway (e.g., sepsis).
Phase 2 (90–180 Days): Intelligence
Add imaging triage, medication safety, care-plan adherence scoring; registry for models/prompts; evaluation suites; policy-aware tool execution logs; knowledge graph for care coordination.
Phase 3 (180+ Days): Agentic Operations
Automate prior auth drafting, discharge-summary generation, and population-health outreach with governed agents; enable continuous learning loops with clinician feedback and A/B rollouts.
Conclusion
An AI-first healthcare model elevates EHR tables into a safety-conscious learning system. Facts, features, vectors, graphs, and narratives interlock to deliver earlier detection, smoother coordination, safer medications, faster reimbursements, and clearer communication while preserving privacy, provenance, and clinician trust.