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Anticipating patient progression and response

inFlow – Predicting patient outcomes

Once a patient is diagnosed, the questions do not stop.

Clinicians, trial teams and payers all need to know what happens next: who will respond, who will progress quickly, and what the real-world course of the disease looks like. A single population-level estimate can obscure the answer, treating a mixed population as one group when real subgroups respond differently.

inFlow answers that question, giving clinical, medical and access teams a forward view of what happens next, to strengthen trial design, evidence and decisions.

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Why predicting patient outcomes matters

A patient's future course is individual, not a population estimate.

Most clinical data describes a disease at the population level, not the person in front of a clinician or a trial participant. That single estimate smooths over the subgroups that matter and risks trials, evidence and clinical judgement.

The one-size
assumption

Clinical trials are often designed as though every participant will respond alike, hiding the subgroups most likely to benefit or most at risk. inFlow designs around real subgroups in any disease, an approach validated in a rare metabolic disease, in one of the largest studies of its kind, mapping the endpoints that mattered most.

The late-evidence
gap

The evidence to justify a treatment's reimbursement case usually arrives only after launch, once pricing decisions are already made. This earlier evidence gives market access and HEOR teams a defensible picture, drawn from a prognostic model that supports clinician-led assessment.

The trial-and-error
default

Clinicians default to trial-and-error without a forward view of disease progression, and the burden of an ineffective choice falls unevenly on those with fewer options. inFlow surfaces who is likely to respond before treatment begins, helping clinicians move directly to the right option.

Where inFlow creates value

Impact across the product lifecycle​

Clinical
Development

Showing how each patient is likely to progress and respond, replacing a one-size assumption with real subgroups and sharpening trial design.

Value, Access
& HEOR

Building an evidence base earlier, giving a defensible value picture before payer negotiations and strengthening the case for reimbursement.

Medical
Affairs

Grounding scientific exchange in a real-world view of disease progression and connecting diagnosis to outcomes.

Commercial
Excellence

Forecasting how patients progress and respond to treatment, sharpening market segmentation and go-to-market messaging.

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How inFlow works

Predicting each patient's path

inFlow applies Volv Global’s proprietary machine learning to population-scale real-world data, including clinical records, electronic health records and other real-world evidence, to forecast how a patient’s disease is likely to progress and how they are likely to respond to treatment.

It builds predictive models from historical and current data, surfaces the therapies most likely to help each patient, and refines its predictions continuously as new data becomes available, so insights stay accurate over time, surfacing the pattern while leaving the decision with the clinician.

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What people ask

Questions About Predicting Patient Outcomes

A: inFlow applies Volv Global's proprietary machine learning to clinical, biological and real-world data. By surfacing subtle signals in that data, it forecasts how a disease is likely to progress and how a patient may respond to a specific therapy, supporting proactive, personalised treatment decisions.

A: A forward view of how a disease is likely to progress, and how a patient is likely to respond, lets clinicians move directly to the treatment most likely to help. It also gives trial and access teams a stronger evidence base earlier, before pricing and reimbursement decisions are made. inFlow covers patients already diagnosed; for patients not yet on treatment, inCare identifies candidate patients for HCP-led eligibility assessment.

A: inFlow gives clinical development teams a forward view of patient subgroups before a trial begins, supporting stratification, endpoint selection and a stronger basis for probability of success.

A: inFlow works with the real-world data that healthcare organisations and data partners already hold, so its predictions can support decisions within existing clinical and research workflows.

A: No directly identifiable patient data is received by Volv Global. Volv Global works through trusted data partners operating under the privacy and regulatory frameworks that apply in each market, and follows rigorous validation processes to keep its predictive models accurate and free of bias. inFlow surfaces and prioritises patients for clinical attention; it does not diagnose, prescribe, or replace clinical judgement.

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