Precision Public Health: From Individual Data to Population Intelligence
Author: Peehu Sachdeva, Market Strategy Lead, Axia Medicine
Published by Axia Medicine | November 2025
This is the third article of a series. Previous entries:
Healthcare produces more data than ever before. Genomes are sequenced in days, wearables capture signals by the second, and electronic records document millions of encounters. Yet, as introduced in The Genomic Paradox, technological progress alone does not guarantee better patient outcomes outcomes. And as The Multimodal Shift demonstrated, obsolete systems prevent data being combined in effective ways for care.
Analyses of EHR-related malpractice claims highlight how gaps in information exchange can contribute to harm. Cases include medication errors when discharge instructions never reached the next provider and neurological deterioration missed because paper and digital systems could not integrate.
The challenge is structural rather than individual. Health systems were designed to prioritise patient safety, regulatory compliance, and local accountability. As care becomes more distributed and data sources more diverse, those same safeguards must now be complemented by infrastructure that supports secure, governed data sharing across settings.
COVID’s Integration Failure
The pandemic revealed this problem at population scale. A National Academies assessment concluded that U.S. genomic epidemiology suffered from insufficient funding, poor coordination, and limited integration capacity, blocking correlation of viral sequences with clinical and epidemiological data.
Each separate discipline worked within its own institutional boundary, replicating at national scale the same ownership logic that fragments clinical care.
Genomicists sequenced variants. Epidemiologists mapped the spread. Social scientists showed how housing and occupation shaped risk. Yet these insights rarely connected. Genetic variants linked to severe disease were identified, but existing systems could not map them onto the communities most exposed. Vaccine acceptance data remained siloed from hospitalisation patterns. Environmental exposures were seldom integrated.
To echo the The Genomic Paradox, the failure was not data scarcity. It was infrastructure built for institutional control rather than integration.
Clinical Research at a Standstill
The same fragmentation undermines research. Over half of clinical trials are discontinued due to recruitment failure. More than 80% miss enrollment timelines, and in rare diseases, nearly a third of Phase III trials collapse for the same reason.
The FOR-DMD trial for Duchenne Muscular Dystrophy shows the cost. Planned to open 40 sites by 2011, it began recruitment only in 2013, an 18-month delay caused by disconnected registries and databases. Each day of delay translates to lost opportunity and millions of dollars in costs; meanwhile, 30 million Americans live with rare diseases, yet most remain unaware of trials that could benefit them.
Improving visibility does not mean bypassing clinicians or institutions. It means enabling governed pathways through which patients, care providers, and researchers can connect more effectively over time.
What Population Scale Demands
As explored in our previous article, precision medicine requires data beyond just health records: genomics, proteomics, imaging, behavioural, environmental and socioeconomic data, all contribute to delivering personalized Care in the most efficient way.
Projects like UK Biobank demonstrate the impact of population-scale, multimodal datasets: when data is integrated across genetics, imaging, and clinical records, discoveries in cardiovascular disease and dementia follow at unprecedented pace.
However, such national initiatives each took more than a decade and billions of investment. If every therapeutic area and every country required comparable efforts, the industry’s progress would be measured in decades.
The cost of inaction is vast. U.S. racial and ethnic health inequities cost $451 billion in 2018, with education-related disparities adding $978 billion. Without change, combined costs could surpass $1 trillion annually by 2040. Precision public health therefore reframes equity as not only a moral imperative but an economic necessity.
From Fragmentation to Collaboration
The path forward is not to replace institutions, but to connect them more effectively. Clinics and health systems play a central role as long-term stewards of patient data, trusted intermediaries of consent, and anchors of clinical context. Patients contribute essential information and agency. Researchers translate data into insight. Public health agencies turn insight into action.
When these roles are aligned, integration becomes possible by design. Consent can be purpose-specific and transparent. Data can move securely while preserving provenance and accountability. Population-level insight can emerge without sacrificing individual trust.
This is the foundation of precision public health.
From Constraint to Capability
What has really been commoditized is data capture. What remains scarce is integration at scale with context and agency preserved. That distinction determines whether health systems prevent harm, whether trials recruit efficiently, and whether public health learns from crises rather than repeats them.
Precision public health will not be achieved by more apps. As demonstrated in the following article, precision medicine requires infrastructure that makes collaboration routine and fragmentation the exception. The core question is not how to collect more data, but how to build systems where health data disconnection is impossible by design.
At AXIA Medicine, we aim to help translate individual-level insight into population intelligence that benefits patients, providers and society as a whole.
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