Healthcare AI Learning
All visual guides

Guide 04The Healthcare Data Landscape

Where healthcare AI gets its data — systems, formats, standards and meaning.

A map of source systems, data types, exchange standards and terminologies — plus the readiness lens that decides whether data is usable for AI.

A4 sheet — scroll sideways or pinch to zoom

Visual Guide 04 · Foundation / Technical / Data

The Healthcare Data Landscape

Where healthcare AI gets its data — systems, formats, standards and meaning.

Healthcare AI Learning

Band 1

Source systems

  • EHR / EPD / HIS
  • LIS / laboratory
  • RIS / PACS / imaging
  • Pharmacy & medication systems
  • Monitoring, devices, wearables
  • Patient-reported data / PROMs
  • Registries, claims, administrative data
  • Guidelines, literature, protocols

Band 2

Data types

  • Structured coded fields
  • Clinical notes / free text
  • Documents
  • Images
  • Waveforms / signals
  • Measurements / time series
  • Genomics / omics (selected settings)

Band 3

Exchange & interoperability

  • FHIRHealthcare data standard: resource model plus exchange framework
  • HL7 v2Event/message-based exchange, widely used in existing hospital environments
  • DICOM / DICOMwebMedical imaging information standard and web services
  • APIsSoftware interfaces exposing data or actions
  • SMART on FHIRApp authorisation and launch patterns around FHIR systems

Band 4

Semantics / terminologies

  • SNOMED CTClinical concepts
  • LOINCObservations, measurements, tests and documents
  • UCUMUnits of measure
  • ICDClassification for reporting, statistics and reimbursement contexts — not interchangeable with SNOMED CT

End-to-end

Source

Representation & exchange

Semantics

Quality & context

AI use

AI-readiness lens

RelevanceTimingProvenanceCompletenessCoding consistencyMissingnessPermissionsTask fit

Standardised ≠ complete, current, consistent or AI-ready.

Common misconceptions

  • “FHIR replaces HL7 v2 everywhere.” No. Many environments run both, often for years.
  • “SNOMED CT and LOINC do the same job.” No. They cover different semantic roles.
  • “Structured data is automatically high quality.” No. Coding practice, timing and completeness still vary.
  • “DICOM is just image pixels.” No. DICOM covers imaging-related information, metadata and services.
  • “Standardised means AI-ready.” No. Task fit, permissions and context still decide.
Healthcare AI Learning · Visual Guide 04Updated Sep 2026

Summarises the sources cited in the related Healthcare AI Learning courses: Healthcare Data & FHIR for AI, AI in Healthcare. Updated Sep 2026; regulatory content checked between 25 August and 10 September 2026. Educational summary only — not legal or clinical advice.

Related learning

Healthcare Data & FHIR for AIOpen
AI in HealthcareOpen

Guides summarise the same primary sources cited in the related courses — official EU legal texts, standards bodies and peer-reviewed literature. Educational summaries only, not legal or clinical advice; regulatory dates were checked between 25 August and 10 September 2026.

Updated Sep 2026