GENOMICS


Classification Metrics and Algorithms Prevalence: P / (P + N) Accuracy: (TP + TN) / (P + N) True Positive Rate (Recall, Sensitivity): TP / P False Negative Rate (Miss Rate, Type II Error): FN / P = 1 - TPR False Positive Rate (Probability of False Alarm): FP / N = 1 - TNR True Negative Rate (Specificity): TN / N = 1 - FPR Positive Predictive Value (Precision): TP / PP = 1 - FDR False Omission Rate: FN / PN = 1 - NPV Negative Predictive Value: TN / PN = 1 - FOR False Discovery Rate: FP / PP = 1 - PPV


Informedness (Bookmaker Informedness): TPR + TNR - 1 Prevalence Threshold: (√TPR * FPR - FPR) / (TPR - FPR) Positive Likelihood Ratio: TPR / FPR Negative Likelihood Ratio: FNR / TNR Markedness (DeltaP): PPV + NPV - 1 Diagnostic Odds Ratio: LR+ / LR- Balanced Accuracy: (TPR + TNR) / 2 F1


Score: (2 * PPV * TPR) / (PPV + TPR) = (2 * TP) / (2 * TP + FP + FN) Fowlkes-Mallows Index: √(PPV * TPR) Matthews Correlation Coefficient: (TPR * TNR * PPV * NPV) - √(FNR * FPR * FOR * FDR) Threat Score (Critical Success Index, Jaccard Index): TP / (TP + FN + FP) Bayesian Inference: P(θ|D) = (P(D|θ) * P(θ)) / P(D)


Q U A N T U M


C O M P U T E


Abbreviations Glossary BA: Balanced Accuracy BI: Bayesian Inference BM: Bookmaker Informedness CSI: Critical Success Index DOR: Diagnostic Odds Ratio F1


: F1 Score


FDR: False Discovery Rate FN: False Negative, FNR: Rate FOR: False Omission Rate FP: False Positive FPR: False Positive Rate FM: Fowlkes-Mallows Index LR+: Positive Likelihood Ratio LR-: Negative Likelihood Ratio MCC: Matthews Correlation Coef MK: Markedness NPV: Negative Predictive Value N: Negative, TN: True Negative P: Positive, TP: True Positive PPV: Positive Predictive Value PT: Prevalence Threshold TNR: True Negative Rate TPR: True Positive Rate TS: Threat Score


Transformation in Classification: GPT-5 with GANs complements genome pre-eXams by advancing predictive Biological Models through real-time data analysis, summarisation, and anomaly detection. Public inclusivity translates complex genomic data into accessible insights as stakeholder engage classifications. For eXams, GPT-5 enhances precise intercepts and refines personal care. Classifiers strengthen trust, equity, and transparency, advancing HPO transformation while aligning with ethical practices and promoting impactful ecosystem strategies.


Fig 5. Population health management, the HPO classification transformation through evidence based metrics.


extensive training for healthcare professionals to effectively use new genomic data, AI tools, and classifications, requiring upskilling for public health and patient safety. The activities and outcomes described in Table 10 (HPO Transformation Roadmap) would logically include genomics phases for network training initiatives.


n Public engagement and trust: While addressed through HEMSS, maintaining sustained public engagement and trust regarding data privacy and ethical AI use in sensitive genomic areas requires continuous, transparent communication.11


The


bias mitigation strategies detailed in Table 6 (AI-HPO Bias Mitigation Metrics) directly support building this trust by ensuring equitable and fair implementation of digital health.


n Regulatory evolution: The rapid pace of AI and genomic innovation necessitates the evolution of regulatory frameworks (eg for AI safety)16 data use,14,19 commission.20


and


with assessments for These needs keep


pace, posing an ongoing challenge for


governance, which requires ongoing stewardship in HEMSS. The specific AI-HPO tools for digital twin simulation and genomics mentioned in Table 5 (Metrics for Precise Care eXams from Pre-eXams) would be subject to these evolving regulations.


n Scalability and maintenance: Ensuring the ecosystem can scale nationally while adapting to future scientific discoveries and technological shifts requires robust long-term planning and dedicated resources. This long- term planning is precisely the purpose of Table 10 (HPO Transformation Roadmap), providing a phased map for sustained development and adoption. Pertinent is the X in the pre-eXam and eXam as an authorisation process proposed by the author in an HPO policy for NICE.2


These challenges aren’t insurmountable but demand strategic planning, sustained commitment, and collaborative efforts with ecosystem stewardship of all stakeholders which prioritise the public for successful navigation, commencing with collective thought on Pre-eXams


Maintaining sustained public engagement and trust regarding data privacy and ethical AI use in sensitive genomic areas requires continuous, transparent communication


58


and eXams [X=Gen AI], as supported by the real-world comparatives in Table 7 (Real-world AI in Clinical Practice Comparatives for Pre-eXams) and Table 8 (Real-world AI for Clinical Practice Digital Twin eXams).


Conclusions – the future of UK healthcare


This manuscript serves as a definitive blueprint for how the UK can strategically achieve the ambitious goals outlined in its Fit for the Future: 10 Year Health Plan as they pertain to national and international PHM,1,3


By proposing a


robust HPO infrastructure, concrete, actionable pathways for global healthcare transformation are offered.2


Ultimately,


this work provides not just a vision but a deployable, value-driven infrastructure that will enable the UK to proactively build a health system that is not only ‘Fit for the future’ but also a world leader in preventive, personalised, and equitable healthcare delivery. The time for this transformation, anchored by genomics, is now.


Key strategic implications for achieving the plan may be visualised as follows: n Actionable roadmap for implementation: A clear, phased roadmap is provided, explicitly referring to Table 10 (HPO Transformation Roadmap). This table translates the plan’s high- level objectives into practical, implementable steps for comprehensive HPO integration across the healthcare continuum. This


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