GENOMICS
ACTIVITY 2
UPSTREAM QUALITY INTELLIGENCE WITH SCIENTIFIC DATA THEMES
HYBRID AI – PRACTITIONER SAFE
ACTIVITY 3
WORKSPACE BIOLOGICAL MODELS WITH QUANTUM FEDERATED LEARNING
END TO END WORKFLOW SECURE
ACTIVITY 4
WORKSTREAM APPROVED CLASSIFICATIONS FOR END USERS
ISO IEC JTC1 SC42
AI SAFETY INSTITUTE, RESEARCH RESOURCE AND DIGITAL REGULATION SERVICES ALIGNMENT FOR THE ASSESSMENT OF HUMAN PHENOTYPE ONTOLOGY PREDICTIVE HEALTH EXAM & EXAM INTERCEPTS
POPULATION HEALTH
MANAGEMENT PROGRAMME
AGILE GROUPS DEVELOP CLASSIFIERS
INFINITE IMPROVEMENT DEV-OPS
LIFE CYCLE HPO SAFE SPACE GEN AI
GENOME PREDICTIVE HEALTH AND
PECISION CARE
ACTIVITY 1
AIDRS-CQC HIGHER MEDICAL SCIENCE SAFETY [HEMSS] STEWARDSHIP IN SECTION THREE DNA PASSPORTS and PERSONAL ID ACTIVITY 6
INFRASTRUCTURE ACCESS FOR GMS/AIRR/AISI/AIDRS, AGILE GROUP DEVELOPMENT AND PHM STEWARDSHIP
EQUITABLE
EXPERT PUBLIC HEALTH AND PATIENT SAFETY PRACTICE TO INCLUDE CITIZEN RESPONSIBILITIES
Fig 5. AISI/AIRR/AIDRS cycle of continuous BM improvement in HPO classifications.
the CQC (and US equivalents), to include the public and engage the stakeholders in PHM.
n Author’s proposition on HEMSS: • Establish HEMSS stewardship through UK GMS/The Royal College of Pathologists (all relevant colleges) in collaboration with the AISI/AIRR for genomic hubs, biobanks, and life science (biopharma) integration into HPO primary care within the UK.
• Establish HEMSS as a national AIDRS stewardship that provides the necessary support to ICS assessments and CQC oversight in the PHM of HPO within the UK.
• Implement national or regional feedback cycles within the UK for the continual PHM of BM to predict and intercept HPO (Fig 5).
Solutions for the
implementation challenges Here is a summary of the challenges to implementing the ‘HPO Policy for HEMSS Stewardship of Classifications’ with the solutions proposed for the UK while adaptable to the US. n Complexity of implementation: Coordinating numerous stakeholders, technologies, and regulatory bodies. Solution: Stewarding a pre- existing national infrastructure and understanding of digital health integration through widespread HIMSS adoption, making HEMSS implementation an adaptation rather than a ground-up build.
n Funding and resources: Securing the necessary financial and other resources, HEMSS is a cross
discipline asset. Solution: The DHSC, DSIT and Offices of AI and Life Sciences align budget priorities for an Integrated Care Ecosystem. An AIDRS, ICS, CQC and HSSIB initiative, would provide dedicated financial and strategic support.
n Data governance and interoperability: Achieving seamless data sharing and ensuring robust data governance, security, privacy and ethical use. Solution: Employing advanced techniques such as federated learning, quantum computing and GPT 5 facilitate developments, authorisation and adoption of classifications.
n Regulatory landscape: Navigating potentially evolving and differing regulatory environments for AI in healthcare. Solution: Advocating for an appropriateness to regulation as detailed in the manuscripts in a balanced ecosystem that ensures safety and ethics that progress the PHM with HPO consistently.
n Stakeholder adoption: Gaining buy-in, trust and active participation from various stakeholders. Solution: Driving buy-in by demonstrating adverse event reduction associated with cross discipline AI practitioners, thereby highlighting the benefits of the PHM ecosystem in mitigating negative outcomes.
n Measuring impact on outcomes: Establishing metrics and mechanisms to evaluate the effectiveness of the ecosystem through digital twin classifications.
WWW.PATHOLOGYINPRACTICE.COM MAY 2025
Solution one: The ‘X’ in the classification provides transparency for practitioners and citizens. Solution two: The DHSC- AIDRS/ ICS/CQC ecosystem benefits from HEMSS stewardship in a cycle of digital continual improvement (Fig 5).
Conclusions
This HPO policy brief underscores the need for clear and accessible communication of its technical underpinnings to a broad audience of policymakers and stakeholders. The author issues a direct call to action for the United Nations to champion the global adoption of pre-eXam and eXam stewardship, and for the UK government to prioritise AIDRS implementation of HEMSS. The ‘X’ classification, drawing its strength from the detailed measurements and metrics within my six research papers, provides a transparent and user-friendly mechanism to evaluate the criteria for this ecosystem across nations. A recent US court case in March
ruled against authority to regulate laboratory-developed tests as medical devices, which underscores the need for robust ecosystem of HIMSS maturity and HEMSS stewardship for value-based care at the point of need. These align with science and technology standards, addressing gaps and reinforcing the ‘HPO Policy for HEMSS Stewardship of Classifications,’ ensuring development, authorisation and adoption of scientific data and advanced AI. It is through federated learning and transformation that the stewardship of classifications evolves BM and integrates HPO, with
2025,27 43 FAIR
POINT OF NEED
ADOPTION OF VALUE BASED CARE
ACTIVITY 5
ONTOLOGY SYSTEM ENGINEERING INITIATIVES FOR PUBLIC SAFETY
GENERATIVE AI
MULTIMODAL DATA
© copyright James Henry
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