Post-marketing shouldn't be the part everyone dreads.
Every approval comes with obligations: safety signals to monitor, real-world effectiveness to demonstrate, questions regulators will ask years after launch. The industry treats this as a cost centre — slow, expensive, and structurally harder to recruit for than the trials that came before it. Vana Healthcare is built to make it the opposite: fast, queryable, and continuous, from a patient panel that already consented.
Sources: FDA Postmarketing Requirements and Commitments reports; published industry analyses on post-marketing spend and real-world evidence. Full citations at the bottom of this page.
Post-approval research is structurally harder than pre-approval research. That's not a management failure — it's the design.
Understanding why it's hard is the whole reason a different approach can work.
Once a drug is approved, the incentive to enrol disappears
Before approval, a trial is often the only route to a promising therapy — patients have a reason to enrol and accept randomisation. After approval, that same patient can simply get the drug. Recruitment for post-marketing studies is therefore harder than for the pivotal trials that preceded them, precisely because the drug succeeded.
Years and millions, for an obligation rather than an asset
Individual post-marketing studies routinely run for years and cost millions. Because they're framed as regulatory compliance rather than commercial advantage, they compete for budget against pipeline work — and lose. The predictable result is the delayed share of the FDA's backlog.
Retrospective data answers yesterday's question
Claims and EHR extracts are the default fallback, and they're months stale, encounter-shaped, and blind to everything that happens between visits. If the question is "how are patients on this therapy actually doing, right now," a retrospective extract structurally cannot answer it.
Nobody's ecosystem rewards doing this well
There's little upside for being excellent at post-marketing surveillance and meaningful downside for being visibly bad at it. So the rational strategy has been to do the minimum, slowly. That equilibrium holds only while the alternative is expensive — which is the assumption we're built to break.
A consented patient panel you can actually ask a question — and get an answer this week.
Not a dataset you license and hope covers your question. A live population that has already agreed to be asked.
Patients join and stay engaged
A daily companion app gives patients a genuine reason to show up — their own health, made legible, in one place instead of a dozen portals. Engagement isn't a growth-hack metric here; it's the mechanism that keeps the data continuous instead of episodic.
Consent is granular and captured at the source
Patients approve what's shared, by data type, and can revoke it. That means the consent trail exists before your question does — not reconstructed afterwards from a licensing agreement written years earlier for a different purpose.
You define a cohort and push a question to it
This is the part that doesn't exist elsewhere. If you need to know whether a specific subset — a therapy, a dose, a comorbidity profile, an age band — experienced a particular symptom today, that question can go out as a notification and come back as structured, timestamped, consented data. Not a chart review. A query.
The same panel compounds across questions
Every study on the panel makes the panel more valuable: longer longitudinal history, better-characterised patients, established trust. The second question is cheaper and faster to answer than the first — the opposite of commissioning a new study each time.
The goal is simple: when a question about a drug already on the market needs a real answer from real patients, Vana Healthcare is the first call — because it's the fastest, cleanest, most defensible way to get one.
We chose where to start by looking at where the funded work actually is.
Post-marketing demand is not evenly distributed across therapeutic areas — it concentrates around recently approved drug classes with live obligations, and it fades as those classes mature. We screened candidate indications against live trial registry data to find where the window is currently open. Oncology came out ahead on every axis that matters to this model, which is why the roadmap is cancer-first rather than opportunistic.
| Indication / class | Active Phase 4 | Approval wave | Window |
|---|---|---|---|
| Multiple myeloma bispecifics & CAR-T — Phase 1 beachhead |
19 | 2021–2024 | Open now |
| MGUS / smoldering myeloma precursor monitoring — same cohort |
35 | — | Long-horizon |
| CLL & lymphoma BTK inhibitors, bispecifics — Phase 2 |
25 | 2019–2024 | Open now |
| Breast cancer ADCs, CDK4/6 inhibitors — Phase 3 |
71 | 2019–2025 | Largest pool |
| Active-surveillance prostate cancer | 38 | — | Long-horizon |
| Melanoma checkpoint inhibitors |
2 | 2011–2014 | Largely closed |
| Early-onset colorectal cancer | 16 | — | Thin near-term |
| Cancer survivorship 18M+ US population |
5 | — | Large but unfunded |
Counts are active or upcoming studies from ClinicalTrials.gov, live queries run August 2026, screened across nine candidate indications. Trial landscapes shift — we re-run these rather than citing them from memory, and we'll share the full methodology and sponsor-level detail on request.
What the screen tells us
Aggressive, short-survival cancers can show far higher trial counts and still be the wrong starting point — a continuous-engagement panel needs a population that lives with a condition for years. Conversely, a huge population with no current funded work (survivorship) isn't a near-term commercial thesis. The intersection of funded now and long-horizon is narrow, and that's where we started.
Why the expansion path is clinical, not opportunistic
Myeloma to CLL and lymphoma isn't a jump — it's the same hematology teams, the same referral networks, and overlapping drug classes (BTK inhibitors, bispecifics, CAR-T). Breast cancer follows as the largest version of the identical problem: 71 active Phase 4 studies and a population living for years on ongoing therapy. Each phase inherits infrastructure and trust from the last rather than rebuilding both.
Why we publish our reasoning
Any partner senior enough to sign a data agreement will interrogate why we picked our beachhead. We'd rather show the screen, including the options we rejected and why, than present a conclusion and ask for trust. If our reasoning is wrong somewhere, we want the partner who can tell us.
You don't have to restructure anything to work with us.
The most common objection to a new data partner isn't the price — it's the integration and compliance overhead. We've designed around that deliberately.
We plug into your existing infrastructure, not around it. Vana Healthcare is a data and patient-relationship layer. Your discovery engines, analytics stack, and statistical workflows stay exactly as they are — we're an input to them, not a replacement for them.
Consent exists before the question does. The compliance-review burden that usually stalls a new data source is front-loaded on our side, at the point of patient onboarding, with a granular and revocable trail — rather than assembled retroactively to justify a use case.
Start with one defined study, not a platform commitment. A sponsored registry or a single natural-history study is a scoped, budgeted, familiar procurement line. If the data quality doesn't hold up, you've risked one study — not an ecosystem migration.
De-identified and aggregated by default for partner queries. Cohort-level answers, not raw identifiable records. The architecture is designed so the thing you receive is the thing you actually need for a regulatory or research question.
Our incentives point the same direction as yours. We only have a business if patients trust us enough to stay engaged, and partners trust the data enough to keep asking questions. Neither survives a shortcut on either side. If you grow with us, we grow — that's the whole structure.
Pre-launch, and we'd rather you hear that from us.
There is no live product yet and no signed pharma pilot. What exists is the thesis above, the research behind it, and a team building toward a first cohort. We're having conversations now with partners who want to shape what this becomes rather than evaluate it once it's finished.
If that's early for your organisation, we understand entirely — tell us what would need to be true, and we'll come back when it is. If it's interesting because it's early, we should talk.
Sources
FDA Postmarketing Requirements and Commitments reports and downloadable database (backlog size and status distribution, including the delayed share) — fda.gov. Projected oncology post-marketing spend and the real-world-evidence acceleration estimate — published industry analysis, Flatiron Health. Post-approval recruitment difficulty and study cost/duration — National Academies / NCBI Bookshelf and Postmarketing Research and Surveillance: Issues and Challenges. Trial counts — ClinicalTrials.gov live queries, August 2026.