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One methodology, for any difficult-to-diagnose disease

Volv Global's machine learning methodology

One proprietary methodology powers all of Volv Global’s solutions and adapts to any difficult-to-diagnose disease. Each new disease is modelled from first principles, not adapted from an existing one.

This methodology detects patterns in real-world data that show a disease is present, developing, or likely to respond to a specific treatment, depending on what a clinical or commercial team most needs to know.

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What is Volv Global's methodology?

A more complete, personalised picture of each patient

Volv Global’s methodology looks beyond a single diagnosis code. It builds a more complete, personalised picture of each patient’s health journey from the real-world data available, rather than treating everyone who shares a label as the same.

That more complete picture gives clinicians and researchers a stronger basis for diagnosis and treatment decisions, and gives patients a shorter path to the right care.

Unlocking accurate
diagnosis

Volv Global's methodology learns the biomarkers of a disease directly from real-world data, building prediction models for difficult-to-diagnose conditions where unmet need is greatest.

Generating
real-world evidence

Our methodology continuously turns real-world data into fresh insight along the whole patient journey, helping to establish how a disease progresses and what its natural history looks like.

Personalising
healthcare

The methodology gives precise, patient-level insight into disease management and treatment, so care can be targeted to each individual.

Reducing healthcare
cost and burden

Volv Global's insights help reduce the cost of late or incorrect diagnosis: the ineffective treatment, the long-term care, and the decline in health that follows.

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Insights at your fingertips

inVolv platform

inVolv is Volv Global’s secure platform for delivering the insights generated by its methodology directly to partners and clients. It supports data-driven decisions with tailored reporting and analytics for each stakeholder, including interactive filters and visualisations such as charts and heatmaps.

inTrigue supports the development of new treatments

Impact across the product lifecycle

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Volv Global started in rare and difficult-to-diagnose diseases

The challenge of rare diseases

Rare diseases affect fewer than one person in several thousand. Clinicians see them less often, and developers of therapies and payers have fewer patients to draw understanding from, which can leave people without a clear path to diagnosis or care.

Depending on how ‘rare’ is defined, more than 300 million people, between 3.5 and 5.9% of the world’s population, live with one of an estimated 6,000 to 10,000 rare diseases. Volv Global’s methodology began here, and now reaches far beyond it.

EURORDIS – Rare Diseases Europe

NORD – the National Organization for Rare Disorder

Patient resources

Navigating complexities

Beyond rare and difficult-to-diagnose diseases

That same challenge is not unique to rare disease. Many other difficult-to-diagnose conditions share the same small, heterogeneous patient populations, and building a true understanding of their prevalence, progression, patient journey and outcomes is just as complex.

So Volv Global builds machine learning algorithms tailored to each disease, able to learn for themselves how to characterise it and recognise the patients who have it.

Limited treatment options

Of the thousands of rare diseases, only around 5% have an approved treatment, leaving the remaining 95% unaddressed.

Limited knowledge

For many of these diseases, the underlying disease biology is still poorly understood.

Everyone is unique

Small, heterogeneous patient populations present challenges for clinical development.

Pipeline herding

'Pipeline herding' means development efforts are unevenly distributed, leaving some diseases with a persistent gap in unmet need.

Societal burden

The cost of not treating rare diseases, in healthcare spend and in quality of life, is higher than most people realise.

Understanding every patient, across every disease.

One proprietary methodology, powering four solutions

inTrigue
finds
patients

Finding the patients a health system cannot yet see, recognising candidate patients who may have a disease never been correctly coded or diagnosed for HCP-led confirmation.

inAdvance
detects
disease earlier

Recognising patients on a trajectory towards a condition before conventional signs appear.

inFlow
predicts what
happens next

Drawing on real-world evidence to anticipate progression, response and outcomes for patients already diagnosed.

inCare
finds eligible
patients

Flagging patients not yet receiving a treatment to evaluate their eligibility, closing the gap between diagnosis and care.

Volv Global powers solutions across stakeholders

Impact across the product lifecycle​

Clinical
Development

De-risking clinical trial planning by learning the biomarkers of a disease directly from real-world data, supporting more accurate patient search and recruitment.

Value, Access
& HEOR

Building natural histories of disease and generating the real-world evidence regulators and payers need for approval and reimbursement.

Medical
Affairs

Turning real-world evidence into insight on disease progression, outcomes and endpoints, supporting clinical conversations with treating physicians.

Commercial
Excellence

Finding and profiling the patients most likely to benefit from a specific treatment, sharpening launch and go-to-market decisions.

Bridging technology and treatment

All about Volv Global's methodology

Volv Global’s proprietary methodology extracts meaningful insight from sparse, unstructured or inconsistent data in electronic health records and claims data, surfacing the patients most at risk of a complex disease.

This methodology builds proprietary computational models that detect biomarkers, phenotypes and other clinical features directly from that population-scale data. For diseases with especially heterogeneous populations, it also clusters patients into meaningful sub-groups, reflecting the different ways a disease actually presents.

Customised algorithms

Volv Global's methodology is customised for each project. It learns from the data available rather than from existing knowledge or guidelines, building a mathematical representation of the disease from first principles as its starting point.

High precision methodology

Volv Global's models combine computational rigour with expert human judgement: independently validated by disease experts and continuously refined. In independent validation, results have held up in real clinical settings and transferred across different data environments. This includes the largest machine learning study of Pompe disease to date, covering 3,549 patients.

Patient privacy by design

Volv Global does not hold patient data. No identifiable patient data is obtained at any point; all work is conducted through trusted data partners operating under the privacy and regulatory frameworks that apply in each market.

Learn once, use everywhere

Volv Global's methodology works across the healthcare ecosystem, from primary care to academic centres to data partners and EMR providers. It is designed to learn once and then be adapted across different coding systems and ontologies. Most machine learning models need retraining for each new setting; this one does not.

Integrating with existing systems

Volv Global's methodology integrates with the healthcare systems already in place. Because coding systems and clinical practice differ by country, it learns its own model for each one, adapting to local language, data structure and clinical thinking.

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Proprietary Machine Learning Methodology