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Detecting undiagnosed patients

inTrigue – Finding patients

People with rare and difficult-to-diagnose diseases can wait years for a correct diagnosis, and many carry no accurate code in any health system. That gap falls hardest on those with rarer conditions, atypical presentations, and access to fewer specialists.

inTrigue finds them. Applying Volv Global’s proprietary machine learning to population-scale real-world data, it surfaces people who have a disease but have never been correctly coded or diagnosed, so they can be recognised and referred for clinical assessment.

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Why finding patients matters

Every decision about developing treatments and patient care rests on understanding the true patient count.

Patient data captures only a fraction of the real disease population in rare and difficult-to-diagnose conditions because coding systems were not built to capture the full complexity of rare phenotypes. Those patients not captured remain absent from the clinical programmes and commercial strategies built for them.

The wrong
patient count

When the diagnosed population understates the full one, every model built on it carries the error forward: trial assumptions, market access dossiers, and commercial forecasts. inTrigue replaces that uncertain count with a real, characterised population, grounding every decision in evidence that holds up to scrutiny.

The risk of
missing patients

One of he most common reasons clinical trials in rare disease fail recruitment is that the patient population was smaller than assumed. Patients who exist are missed simply because they sit outside what standard coding captures. inTrigue finds them before recruitment stalls, widening the recruitable pool.

The value of
starting early

Knowing the real patient population before protocol lock, indication selection, or launch planning removes one of the most common sources of late-stage programme failure. Engaging inTrigue earlier carries more credible evidence into every stage that follows, from regulatory review to commercial launch.

Where inTrigue creates value

Impact across the product lifecycle​

Clinical
Development

Mapping the biomarkers and phenotypes that define the eligible cohort, de-risking trial design and building evidence regulators and HTA bodies will scrutinise.

Value, Access
& HEOR

Grounding patient counts on real numbers, not diagnostic proxies, to strengthen regulatory, HTA and market access arguments.

Medical
Affairs

Mapping biomarkers and trajectories across the patient population to surface the phenotypic complexity that shapes medical communication.

Commercial
Excellence

Finding where the patients actually are, letting launch targeting and field deployment begin on real numbers.

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How inTrigue works

Finding patients hidden in the data

inTrigue applies machine learning to population-scale real-world data, including electronic health records, claims data, primary care records, and ambulatory data, to surface patients whose data patterns are consistent with the clinical signatures of rare and difficult-to-diagnose diseases in patients who have never been correctly coded.

Every finding is validated with clinical experts before being delivered. The methodology is fully explainable: the clinical reasoning behind each flag can be examined, challenged, and published.

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What inTrigue makes possible

Finding difficult-to-diagnose patients, with AI

In HCM, inTrigue flagged between 2.5 and 4.6 times more likely patients than current diagnosed counts, including a subgroup of fast progressors who needed earlier intervention. In NETs, it surfaced 3.2 times the number the sponsor had predicted in UK primary care. In acute hepatic porphyria, working from a database with no confirmed diagnoses at all, a blinded clinician assessed 90% of the top candidates as plausible.

In each case, the true patient population was larger than the coded data suggested, and many of the patients found had been waiting years for a diagnosis they had not received.

Pharmaceutical teams use inTrigue to de-risk clinical programmes, strengthen value cases, and find patients that their own feasibility studies missed. The intelligence generated in each engagement is shared with our partners and is publishable, so the findings compound beyond the project itself.

  • Accelerate rare disease detection– identify subtle patterns in patient records years before conventional methods.
  • Support precision medicine– enable personalised treatment pathways based on biomarker and phenotype insights.
  • Improve clinical trial efficiency– expand the patient pool with correctly identified candidates, reducing time-to-recruitment.
  • Enhance healthcare outcomes– shorten the diagnostic odyssey for patients while reducing costs for healthcare systems.

Read our case studies .

What people ask

Questions about finding patients

inTrigue applies machine learning to population-scale real-world data to detect the clinical signatures of rare and difficult-to-diagnose diseases in patients who carry no relevant diagnostic code. It does not search for codes; it searches for patterns. Those patterns are validated with clinical experts, producing a probability score and evidence trail for each candidate, ready for clinical and trial workflows.

When the coded population understates the real one, every decision built on it carries the error. Trial assumptions, market access submissions, and commercial forecasts all rest on a patient count. If that count is wrong, the consequences compound across development, regulatory review, and launch.

The patients left out of the count also pay a personal cost: years without a correct diagnosis, without access to the right treatment, and without the option to participate in a trial designed for their condition. That cost falls on a real person, whether or not they appear in the coded data.

Clinical trials in rare disease frequently stall because the recruitable population was smaller than assumed. inTrigue finds the patients that conventional feasibility misses, widening the recruitable pool and de-risking the programme. In HCM, it found between 2.5 and 4.6 times more patients than the diagnosed count. In NETs, 3.2 times the sponsor's prediction. Beyond recruitment, the wider population also produces stronger real-world evidence, richer natural history data, and a more credible comparator for regulatory and HTA review.

inTrigue works with real-world data accessed through Volv Global's trusted data partners, covering more than 400 million patient records across the United States, United Kingdom, Germany, and beyond. No identifiable patient data is obtained at any point. All work is conducted under applicable privacy and regulatory frameworks, and Volv Global does not hold patient data.

Standard approaches search for patients who already carry the relevant diagnostic code. inTrigue detects clinical signatures in patients with no code at all, applying a proprietary machine learning methodology validated with clinical experts across population-scale data and multiple geographies. Many machine learning approaches to patient-finding are built and validated on a single population and lose accuracy when applied elsewhere; inTrigue's methodology is KOL-validated across data environments and geographies.

Every model is fully explainable: the clinical reasoning behind each flag is transparent and publishable. The knowledge generated compounds; each engagement adds to a methodology that grows more precise as it is applied across diseases, data environments, and geographies. Where inTrigue finds patients with an undiagnosed disease, Volv Global's inCare finds patients who are already diagnosed but untreated or lost from follow-up.

Shaping the future today

Real-world impact​​

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