GENOMICS


is the ‘how-to’ guide for ecosystem- wide adoption, ensuring a systematic approach to transformation. The foundational context for this PHM programme was established in Table 1.


n Technological empowerment: The work identifies and integrates cutting- edge generative AI,6 computing,5


quantum and federated learning,4


specifically tailored to overcome the complexities of HPO data of primarily genomic origin management and analysis. This directly enables the advanced diagnostic, preventive, and therapeutic capabilities envisioned by the plan, making technological aspirations a reality, particularly through the tools and applications presented in Table 5 (Metrics for Precise Care eXams from Pre-eXams).


n Standardised foundation for national integration: By advocating strict adherence to international ISO standards21-25


and HIMSS levels,26


the essential groundwork for a truly interoperable, scalable, and secure national health data ecosystem is laid. This foundational standardisation is critical for the plan’s success in unifying health information across diverse providers, as guided by Table 9 (International Key Expert Areas Guidance on HPO Transformation). The clarification of key terms, provided in Table 2 (HIMSS development of HPO transformation and HEMSS PHM adoption), also underpins this standardisation.


n Ethical and trust-based governance: A robust ethical and safety governance model with HIMSS26


and HEMSS


proposed is critical for cultivating public trust11


and ensuring responsible


digital health innovation. This directly supports the plan’s patient-centric and ethical imperatives, guaranteeing safe and equitable deployment of technological advancements, with specific mitigation strategies for bias detailed in Table 6 (AI-HPO Bias Mitigation Metrics).


n Ecosystem for measurable progress and accountability: Through specific metrics and comparatives, tangible tools are offered for monitoring progress and demonstrating the effectiveness and return on investment of the plan’s initiatives. This enables data-driven accountability and continuous improvement through the NICE,20


AIDRS,14 AIRR,15


The detailed metrics to measure AI- driven HPO intervention effectiveness are provided in Table 4 (AI-HPO Intercept Metrics), and the value and impact of the HPO ecosystem, demonstrating the plan’s benefits, are


To actualise this transformation, it is imperative to address and overcome resistance to essential digital infrastructure, such as the Federated Data Platform (FDP) provided by companies like Palantir.27


The accumulation and


secure integration of health data, as facilitated by such platforms, are not merely technological advancements but fundamental requirements for achieving the comprehensive insights necessary for advanced public health management, with proactive genomic ‘pre-eXams’ and personalised ‘eXam’ intercepts. As articulated by the UK head of Palantir, opposition from some medical professionals against these shared software systems risks prioritising “ideology over patient interest”, thereby critically delaying the progression of public health initiatives and compromising efforts to enhance patient safety through data-driven care.27


Embracing secure,


distributed data is crucial for unlocking the full potential of HPO and AI in delivering the future of UK healthcare, wherein X is transparent, explainable, fair, trustworthy and metrically viable through national development and authorisation to adopt personalised predictive and precise wellbeing, as the primary care norm.


References n A full list of references for this article is available at www.bit.ly/PiP-Henry3


James A Henry, MSc Mol Path Research, MSc Biomedical Sciences, PhD Candidate by Publication


and the AISI.16


James has spent a decade presenting research nationally and internationally and was invited by the United Nations to submit a Scientific Policy Brief. Population Health Management, Human Phenotype Ontology Policy for Biological Modelling Classifications is a foundation for citizen- centric primary care. His six publications advance pathology and policy in practice for AI applications in medicine and public health. James actively seeks employment and funding to support his doctorate by publication, ensuring the successful dissemination of his accepted research papers. Please get in touch via email to discuss opportunities for sponsorship or engagement.


James.henry19@outlook.com WWW.PATHOLOGYINPRACTICE.COM AUGUST 2025 59


outlined in Table 3 (System thinking: Ecosystem Value and Impact for HPO Transformation). The real-world application examples are further supported by Table 7 (Real-world AI in Clinical Practice Comparatives for Pre- eXams) and Table 8 (Real-world AI for Clinical Practice Digital Twin eXams).


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