POLICY AND PRACTICE REVIEWS article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1496937
Population Health Management Human Phenotype Ontology Policy for Ecosystem Improvement
Provisionally accepted
- Institute of biomedical sciences, London, United Kingdom
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Aims Population Health Management (PHM), through strategic integration of the Human Phenotype Ontology (HPO), emphasises the responsible use of digital infrastructure and comprehensive genomic data to promote good health and well-being. The UK seeks to steward medical science and phenotype practices in primary care settings with technical approaches for developing a national Biological Modelling (BM) ecosystem. By recognising diverse global healthcare systems, this manuscript offers a means for nations to adapt their HPO operational deployment for global PHM harmony. Methods The methodological approach incorporates primary care services and funding assessments to address digital infrastructure needs, ensuring secure national data access. Evaluations include ISO standards, systems thinking, alignment of UK infrastructure with informatics requirements, and AI norms within the ecosystem. Specific use cases for genomic predictive health pre-eXams and precise care eXams are assessed, alongside strategies for bias mitigation to ensure fairness in AI-driven classifications. Recommendations The manuscript advocates for establishing local agile ecosystem groups for PHM, regional Higher Expert Medical Science Safety (HEMSS) stewardship, national HPO value-based care models, and integrating global PHM general intelligence. Real-world AI and clinical practice comparisons are emphasised for validating digital twin personalised BM via Gen AI in the HPO transformation framework. Discussion Federated Learning and GPT-5 technologies advance international PHM by supporting HPO transformations. Standard personalised BM learning addresses intranational HPO variances, requiring individual classifications. National HPO roadmaps prioritise inclusiveness and stakeholder engagement, supported by informed consent and quantum intelligence. Ethical and equitable HPO deployment demands proactive stewardship and national cooperation to address limitations and ensure robust classifications. Conclusion Unified, data-driven HPO transformation utilising advanced AI and genomics is essential for personalised healthcare delivery. Rigorous assessments, ethical considerations, and global collaboration enable impactful implementation. National PHM ecosystems guided by HPO transformation in classifications sustain healthcare, advancing patient outcomes through responsible innovation and informed policy development.
Keywords: Population health management, Safety, security, Predictive health, Precision Care, Classifications, Human Phenotype Ontology Policy
Received: 15 Sep 2024; Accepted: 16 May 2025.
Copyright: © 2025 Henry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: James Andrew Henry, Institute of biomedical sciences, London, United Kingdom
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