A diagnosis is not the same as treatment.
Some patients who are diagnosed have never started the therapy they are eligible for. Others started and stopped, ended up on the wrong therapy for their condition, or fell out of follow-up before anyone noticed. These patients already exist inside a health system’s own coded data, but stay invisible to the teams who could reach them.
inCare brings that treatment- and trial-eligible population into view within existing coded care pathways, so the right patients can be reached at the right moment.
Why finding eligible patients matters
Health systems already hold the data that shows where this gap sits. A patient’s record carries the diagnosis, the treatment history, the gaps in follow-up, yet that information rarely gets pulled together into a single, actionable view. The result is a population that is neither invisible, like an undiagnosed patient, nor accounted for, like someone actively on therapy: diagnosed, eligible, and unreached.
A trial's slow recruitment or a new therapy's low uptake often comes down to how many diagnosed patients are actually being treated, not how many people have the condition.
inCare surfaces those diagnosed, eligible patients directly from existing coded pathways whose records indicate possible eligibility, for clinical review, cutting screen failures and showing sites where they actually are.
A budget-impact model is only as strong as the population behind it, and an undercount of untreated or miscoded patients weakens every figure built on it.
inCare adds those undiscovered, eligible patients back into that count, so pricing and budget-impact work starts from a grounded number, not an assumption.
Behind every under-counted population sits a person who stopped a therapy, was placed on the wrong one, or lost contact with the clinic that diagnosed them.
inCare brings that person back into view, grouped by the reason they went untreated, so a clinician can act on a real name, not an estimate.
Where inCare creates value
Surfacing diagnosed, trial-eligible patients from coded data while a protocol is still being designed, so eligibility criteria are built around a real population.
Adding diagnosed-but-hidden, treatment-eligible patients into the eligible-population count, so budget-impact and value models are built on evidence, not assumption.
Giving a real-world view of the diagnosed population, including those who stopped treatment, so scientific exchange reflects who is actually being treated.
Bringing attention to a flagged, priority group, so field and account teams focus where eligible patients are.
How inCare works
inCare applies Volv Global’s proprietary machine learning to the coded clinical data a health system already holds, including diagnosis codes, treatment records and follow-up history, to find patients who are diagnosed but untreated, treated then stopped, placed on the wrong therapy, or lost from follow-up. It stratifies that population, working within a health system’s existing coded pathways, so outreach, screening and referral reach the right group while the decision to act stays with the clinician and the care team.
What people ask
A: A health system's own coded data already contains the answer, spread across diagnosis codes, treatment records and follow-up history that were never pulled together into one view. inCare brings that existing data together to show which diagnosed patients have profiles consistent with trial or therapy eligibility for clinician confirmation, as well as which of them have never started treatment, stopped early, been placed on the wrong therapy, or fallen out of follow-up.
A: Both work with patients a pharma team cannot yet reach, but the patients sit in different places. inCare works within a population that is already diagnosed and coded, finding people who are eligible for a therapy or trial but have gone untreated or lost from view. inTrigue reaches beyond the coded population altogether, finding patients who have never received the right diagnosis or code at all.
A: The two sit on either side of a clinical threshold. inAdvance works before that threshold is crossed, finding patients on a trajectory towards a diagnosis so intervention can happen earlier. inCare works after it, among patients already diagnosed, finding those eligible for a therapy who have gone untreated or lost from view.
A: No. inCare answers a question of access: is this diagnosed patient receiving the treatment they are eligible for, or are they untreated, on the wrong therapy, or lost from follow-up. Once that treatment gap is closed, inFlow answers a different question, forecasting how the patient is likely to respond to treatment, progress in their disease, or relapse.
A: Volv Global does not receive directly identifiable patient data. 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 methodology accurate and designed to reduce bias. inCare surfaces and groups eligible patients for clinical attention; it does not diagnose, prescribe, or replace clinical judgement.
