International openEHR Archetype Suite for Prostate Cancer Care

A set of internationally validated openEHR archetypes covering the full prostate cancer diagnostic and monitoring pathway. The suite includes:

• Patient‑reported outcome measures (ePROMs): IPSS, IIEF‑5, SE
  • CKM prostate cancer patient‑reported data
• Pathology workflows: prostate biopsy order, macro/micro assessment
  • Order core biopsy (prostate)
• Radiology assessment: MRI‑based prostate evaluation
  • CKM prostate cancer radiology

All artefacts are openly available and reusable via the GitHub links above.

Contact: Per Vincent, Karolinska University Hospital (SWE)

Unique Selling Points

1. Internationally available and reusable
   Developed and reviewed across multiple countries, ensuring global applicability beyond Europe or Scandinavia.
2. End‑to‑end clinical coverage
   From patient‑reported symptoms to biopsy pathology to MRI assessment — a complete, interoperable model chain for prostate cancer care.
3. Based on openEHR standards
   Fully aligned with the openEHR Reference Model and CKM conventions, enabling semantic interoperability, long‑term maintainability, and cross‑vendor reuse.
4. Ready for integration in clinical systems
   Production‑grade models suitable for EHRs, registries, research platforms, and decision‑support pipelines.
5. Openly accessible and extensible
   Published under open licences on GitHub, enabling rapid adoption, adaptation, and contribution by the global community.
6. Supports data quality and structured research
   Standardised data capture improves comparability, multi‑site research, and AI/ML readiness.

Technical Features

• openEHR compliant archetypes aligned with RM, AM, and CKM conventions
• Structured ePROM models for IPSS, IIEF 5, and Sexual Enjoyment (SE)
• Standardised pathology workflow models for prostate biopsy ordering, macro/micro assessment, and reporting
• MRI prostate assessment archetypes supporting PI RADS aligned structured reporting
• International semantic validation across multiple countries and clinical domains
• Consistent terminology binding (SNOMED CT, LOINC where applicable)
• Interoperable data structures enabling cross system and cross vendor reuse
• Machine readable JSON templates ready for integration into EHRs, registries, and research platforms
• Version controlled GitHub repositories ensuring transparency, traceability, and collaborative evolution
• Extensible modelling approach allowing adaptation for local workflows without breaking semantic integrity
• AI/ML ready structured data supporting downstream analytics and decision support pipelines

Integration Constraints

• openEHR compliant backend required (supports RM, AQL, OPT/JSON templates)
• Terminology services dependency for SNOMED CT / LOINC bindings
• Consistent versioning of archetypes and templates across environments
• Strict adherence to CKM aligned modelling conventions to avoid semantic drift
• FHIR mapping layer needed if integrating with non openEHR systems
• Structured data capture UI must support coded fields, ordinal scales, and nested clusters
• Vendor systems must support composition level validation for safe data ingestion
• MRI and pathology templates require image/report metadata alignment with local PACS/LIS systems
• ePROM workflows depend on patient facing apps capable of structured questionnaire delivery
• Interoperability requires stable identifiers for archetypes, templates, and terminology bindings
• Deployment requires Git based lifecycle management for template updates and governance
• AI/analytics pipelines must consume structured JSON compositions without schema deviations

Targeted Customers

• Hospitals and cancer centres
  For structured prostate‑cancer diagnostics, reporting, and patient‑reported outcomes.
• EHR and clinical software vendors
  Integrating openEHR‑based templates into commercial clinical systems.
• National cancer registries and screening programmes
  Standardized data capture for population‑level monitoring and quality assurance.
• Research institutions and academic medical centers
  Harmonized datasets for multi‑site studies, AI/ML pipelines, and clinical trials.
• Digital health and ePROM platform providers
  Structured questionnaires and interoperable patient‑reported data capture.
• Pathology and radiology information system vendors
  Structured biopsy workflows and MRI reporting aligned with international standards.
• Health data platforms and interoperability hubs
  Organisations building cross‑border, multi‑vendor data ecosystems.
• Public health agencies and policy bodies
  Supporting standardization, bench-marking, and international data comparability.
• openEHR community projects and national programmes
  Countries adopting openEHR as a national or regional standard.

Conditions for reuse

• openEHR compliant environment required, supporting archetypes, templates, and AQL queries
• Access to licensed terminologies (e.g., SNOMED CT, LOINC) where bindings are included
• Local clinical validation recommended to align with national guidelines and workflow specifics
• Governance process needed for version control, template updates, and semantic consistency
• Implementing systems must support structured data capture, coded fields, and nested clusters
• Interoperability mappings (e.g., FHIR, HL7 v2) must be maintained by the adopting organization
• Clinical safety checks required before deployment in production environments
• Patient facing ePROM delivery platforms must support structured questionnaires and secure authentication
• Integration with PACS/LIS needed for radiology and pathology metadata alignment
• Compliance with local data protection regulations (e.g., GDPR) for patient reported and clinical data
• Open license terms of the GitHub repositories must be respected for reuse and redistribution