GENOMICS 1 PRIMARY CARE SERVICES
• Value-Based Care • Mitigation of Litigation • Improving Access to Care • Enhancing Quality and Safety • Expand Mental Health Services • Integrating Care Services • Harnessing Tech Data • Preventive Care • Patient Education • Health Equity
2 SPECIALISED HOSPITALS HIMSS
• Accurate Primary Care Service. • Advanced Medical Treatments • Patient-Centered Care. • Research and Innovation • Quality and Safety: • Interdisciplinary Collaboration • Infrastructure and Technology • Precision Medicine • Real-Time Monitoring • Patient Outcomes
NATIONAL BIOBANKS A UK-US Government B
HEALTH PARTNER
BIOLOGICAL MODEL QUANTUM
HEMSS
STEWARDSHIP TABLE 1
PHM MISSION [DHSC DIST] HPO POLICY [AISI, AIRR, AIDRS]
C
CLASSIFICATIONS Predictive Health
Pre-eXam with eXam Intercepts
D MONITOR
ENGINEERING INITIATIVES FOR PUBLIC SAFETY
UK NEIGHBOURHOOD CLINICS: US FEDERAL CENTERS
PHM are fair, transparent and trustworthy services with security, privacy and informed consent accessible across platforms
Fig 3. Overview of Findings [1-4] and Recommendations [A-D] Table 1 illustrates a fair and transparent
approach to the progressive PHM development of HPO through BM. The ‘X’ column underscores the principles of fairness and transparency, building trust in a safe and effective healthcare ecosystem. An ‘X’ designation signifies adherence to appropriate HPO regulations and ethical data governance, ensuring explainability for the end-user. Clear and understandable explanations are vital for demonstrating a commitment to responsible data handling and ethical HPO practices, which are integral to both predictors and intercepts when authorised and assigned ‘X’ for adoption [X=Gen AI]. In the US, the adoption of value-based
care within PHM has been slow, with a limited impact on overall healthcare delivery, despite an Executive Order promoting value-based care derived from scientific data themes.17
within the UK exhibit variability and require accurate predictors for precise interventions, although proactive prevention remains a significant challenge.22
Scientific data themes and
accessible BM technology, utilising bioinformatic translation into spatial multi- omics, can potentially reduce medical variability in an era of pangenome specificity.23,24
Science and technology
driven stewardship of PCN variability, using BM for disease prediction and a standardised HPO vocabulary within a user-friendly portal, can enhance the efficiency of accurate interventions.25
By
aligning genomics, biobanks, and life sciences as standard data themes within an AI-driven ecosystem, PHM effectively stewards HPO implementation at the point of need.26
Healthcare
costs can be improved by using BM to compute a standard HPO vocabulary for predictors and diagnosis within primary care practice.18
The Simultaneously,
precise medicine targeting biopharma in the biological sciences is increasingly expected to be the digital norm.19
slow uptake of HPO necessitates effective stewardship to enhance data utilisation and improve healthcare delivery through robust classifications.20
The HIMSS GROUP MATURITY Voluntary: Classifier Development
AIDRS HEMSS PRINCIPLES Authority: Classifier Adoption
HIMSS – Health Informatics and Management HIGHER – Hybrid Cloud Partners, integrate System Society Maturity Levels 0-7
HIMSS with Tech and data standards The strategic choice
of pre-eXam and eXam for predictive and precision BM serves as a crucial step towards proactive healthcare management rather than reactive treatment.21
reader is directed to paper 1 to culture intelligent workflow, commencing with newborn digital twins in paper 4. Primary Care Networks (PCNs)
40
IFRAM – Infrastructure Readiness and Adoption Model
EXPERT – Digital AI partner ISO/IEC JTC1/ SC42 CCN, LLM, GAN, ViT, Hybrid AI and Gen AI (AGI)
AMRAM – Adoption Model for Application of MEDICAL – Practitioner & Biopharma Responsible AI in Medical and Radiological Management
EMRAM – Electronic Medical Record Adoption Model
CCOM – Continuity of Care Record Adoption Model
Table 2. Safe HPO Space Capacity and Capability. MAY 2025
WWW.PATHOLOGYINPRACTICE.COM
Owners of Tool for Development of Classifications SCIENCE – Multi-Omics, Images & Health Factor
Data Themes in an Alliance to Access and Use
SAFETY – Public Health & Patient Safety. Safe & Secure DNA Access to Predictors and Intercepts*
papers 2 and 6 for proposals on Global HPO transformation.
Overview of findings Table 2 outlines the Safe HPO Space
The reader is directed to
supported by HIMSS maturity, which reinforces figure 3, Sections 1-4, in building a robust Population Health Management (PHM) infrastructure with AISI, AIRR, and AIDRS. Despite these advancements, HIMSS vulnerabilities are highlighted below. Table 2 also outlines the alignment of AIDRS/HEMSS stewardship with figure 3, Sections A-D recommendations, emphasises the critical role of national government authorities in advancing the PHM mission. This includes upholding the Human Phenotype Ontology policy while ensuring HEMSS stewardship of classifications, as detailed below. Table 2 outlines HIMSS maturity in planning predictors, developing intercepts, building and testing BM, and deploying HPO classifications for point-of-need operations observed for value-based care. Additionally, Table 2 highlights HEMSS’s role as a steward of these transformations, promoting good health and economic growth.
A HEMSS Stewardship
3 GENOMICS – MULTIOMICS
HEALTH PROVIDER
HYBRID AI GEN AI [CNN ViT LLM]
HIMSS
MATURITY TABLE 1
• Drug Development • Clinical Trials
• Regulatory Compliance • Market Access • Collaborative Research • Centered Biobank Approach • Precision Medicine • Biomarker Identification • Sustainability • Health Equity
BIO LIFE SCIENCES
• Genome Research • Personalised Medicine • Innovative Technologies • Collaborative Partnerships • Regulatory Compliance • Patient Engagement • Biobank Access • Multiomics Analysis • Predictive Health • Ethical Considerations
HIMSS 4 BIOPHARMA – NUTRIENTS
© copyright James Henry
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