GENOMICS


ISO 15189:2022 ANNEX A.2


GOVERN ONTOLOGY DEVELOP


HEALTHCARE INFORMATION MANAGEMENT SYSTEM SOCIETY


UPSTREAM


PANGENOME WITH HYBRID CLOUD


A.3 AI ASSURANCES A.4 TRAINING


NATIONAL AI OPS FOR IMAGE AND SOCIAL FACTORS


WORKSPACE


HYBRID STANDARD SERVICE LEVEL AGREEMENTS


7.6 CONTROL OF DATA BACK PROPAGATION


7.2 GENOME PREDICTIVE HEALTH [CHANNEL-PRE-EXAM]


7.3 ONTOLOGY PRECISION CARE [CHANNEL EXAMS]


UKAS


HUMAN PHENOTYPE ONTOLOGY DATA


BIOBANK BIOLOGICAL REFERENCES


API AND DATA STORAGE


7.1.3.2 Pre ontology data collection activities


7.4 POST EXAM ONTOLOGY OUTOME


MODEL ONTOLOGY HEALTH CODING SYSTEM


7.1.2.1 Each request accepted in agreements for data sharing


CLAUSE 6.7 SERVICE AGREEMENTS


FEDERATED DATA PLATFORM AND LEARNING


ONTOLOGY VALUE IN DATA SHARES


DATA ALLIANCES IN FUTURE HEALTH


POPULATION HEALTH MANAGEMENT AT POINTS OF NEED


ADOPT


DATA USE AND


ACCESS BILL


PRACTITIONER MANAGEMENT REVIEW


7.5 NON-CONFORMING WORK


PUBLIC HEALTH SOCIAL SERVICES


HEALTHCARE AUTHORISED USER WORKSTREAM


HIGHER EXPERT MEDICAL SCIENCE SAFETY


[AIRR, AISI, AIDRS]


Fig 3. Population health management, the HPO transformations agreements: Point of Care [grey] and Point of Need [blue in safe space]. Institute16 in a genomic ecosystem. The


operationalisation of these pre-eXams and eXams, with public perspectives and tool resourcing, is further supported by the framework depicted in Figure 1.


Data-driven decision making and AI adoption at scale A core UK plan directive is harnessing genomic data and AI for superior health outcomes, enabling efficient services, aiming to make the NHS the most AI-enabled health system in the world.1,4-6,8-16


The manuscript outlines


the essential infrastructure for national- scale implementation by emphasising structured knowledge, equitable tools, and robust data-driven engagement.2 This directly aligns with creating a new Health Data Research Service with the Wellcome Trust and significant joint investment, as HPO provides the standardised language for comprehensive data integration.1-2,7


of advanced AI like federated learning,4 quantum computing,5 (GPT-5)6


Practical deployment and generative AI


is detailed as a critical means to


securely analyse vast, sensitive genomic datasets without compromising privacy. Figure 2 explicitly details confidentiality measures within the federated learning approach, demonstrating large-scale AI deployment, building public confidence for adoption. Figure 2 illustrates the ecosystem encompassing genomic data, digital socio-environment health factors, digital medical/pathology images, and clinical trials. It focuses on the ‘Federated Learning Classification Human Phenotype


Ontology Domain,’ detailing privacy and security measures (encryption, differential privacy, secure aggregation and byzantine robustness) and various ‘Federated Learning’ technologies. It links to national and international HPO transformation stewardship for ethical, secure, and collaborative data use to fulfil the plan’s ambitions. Data integrity maintained during large-scale PHM of HPO, as illustrated by Figure 2, is critical for public confidence in genomics AI.11,14-16


It details seamless integration of multi-omics data and AI-driven analysis for personalised treatment as a PHM operational strategy to move beyond generic one-size-fits-all approaches.3 This empowers the ecosystem for individualised care based on a patient’s unique genomic and phenotypic profile, materialising predictive and precision medicine across the population.17-18 Figure 3 visually represents how genomic predictive health (pre-eXams) and precise care (eXams) functionally connect to all laboratories and all national PHM points of need, providing digital pathways for HPO interventions. Figure 3 demonstrates how federated data platforms and learning enable data sharing in multifaceted service


WWW.PATHOLOGYINPRACTICE.COM AUGUST 2025


Delivering personalised medicine and precise care with HPO integration Patient-centric healthcare is the foundation of the 10-Year Plan, aiming for precise, effective individual treatments.1-2 The manuscript provides a concrete methodology via precise care eXam intercepts.2


agreements, facilitating PHM at points of need. The emphasis is on data governance, outlining the operational model for achieving predictive health [pre-eXam] and precise care [eXam]. The multifaceted service agreements and data governance steward PHM at points of need which are clarified through Figure 3.


Driving operational efficiency and value-based care into practice


The PHM plan enhances value and sustainability by optimising spending, leveraging HPO value-based care, and enabling multi-year budgets and transformation funds to translate HPO innovations into practice.1-3


HPO’s


standardised data (primarily founded in genomics) significantly speeds up clinical trial recruitment, aiming for 150-day setup times for PHM clinical trials. An HPO roadmap is an actionable plan with phased activities for data integration, predictive analytics (pre-eXams), and sustained optimisation (eXams) that drive efficiencies and enhance outcomes, contributing to fiscal health. HPO streamlines technology procurement and a single national formulary for medicines, supporting life sciences in service delivery.1-3


Work focuses on the practical


application of biological modelling research to develop and adopt pre-eXam and eXam classifications and explains how AI tools and data learning can be used for cost-effective care delivery.2,19


HPO’s data-


driven insights also support the National Institute of Health and Clinical Excellence


55


© copyright James Henry


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