Verana Health and the patients hiding in plain sight

A clinical trial can have a promising treatment, a careful protocol, and a surprisingly awkward problem: the people it needs are somewhere else. They are getting injections, changing prescriptions, returning for checkups. Their doctors know them. A trial sponsor may have no idea which waiting room to look in.

Verana Health works on that gap. It takes records generated during ordinary medical care and turns them into datasets and software that researchers can use. The unglamorous work is deciding whether a diagnosis, a treatment, and a clinical measurement mean the same thing across different systems. The payoff is a better-informed place to start looking.

A very small computer. A very large request.

Long before Verana, the American Academy of Ophthalmology tried a registry built around handheld data entry. Doctors were supposed to record information after seeing patients, then synchronize it. David Parke, the Academy’s former CEO and a Verana co-founder, recalled the problem: “the process was a substantial burden to the physicians and was not successful.”

The important change was retrieving information from electronic health records. IRIS, the Academy’s registry, launched in 2014. Later, the Academy sought a commercial partner and began conversations with DigiSight, a tele-ophthalmology company. Verana took shape in 2018 as that business shifted toward data analytics. The change in direction followed a practical discovery: the record of care could become useful research material.

There is a lesson here for anyone building software around busy professionals. Data collection becomes more plausible when it fits work already being done. Asking a doctor to become a second data-entry clerk was a rather expensive way to learn that.

The work before the question

Verana’s position rests partly on relationships. It is the exclusive data partner of the American Academy of Ophthalmology and the American Urological Association. Its expertise combines clinical informatics, engineering, epidemiology, and statistical research. The raw ingredients arrive in incompatible formats, with important details sometimes buried in notes.

VeraQ is its data-curation platform. Qdata packages disease-specific research datasets. The distinction matters: a pile of records and a cohort suitable for a particular research question are different things. In ophthalmology, useful variables can include visual acuity and retinal thickness, details a billing code alone cannot describe.

Qdata Explorer lets life sciences teams build and share ophthalmology cohorts without writing code. Site Explorer applies eligibility criteria to help sponsors and contract research organizations identify practices with potentially suitable patients. Verana Trial Connect brings the process back into participating practices, helping staff review their own patients for a study.

These products sit inside a business selling data access, software, and research services. Buyers include pharmaceutical, biotechnology, and medical-device companies, plus CROs. Participating clinicians also receive services such as quality reporting and trial opportunities. That creates a practical reason for the people supplying information to stay involved.

A promising record does not automatically become a willing participant.

Five weeks, with the numbers left in

A Verana case study about Syneos Health and EyePoint Pharmaceuticals makes the workflow concrete. Their wet age-related macular degeneration trial faced competing studies and sites short of staff and resources. Verana used IRIS data and selected protocol criteria to identify practices, then gave interested sites access to Trial Connect.

The company reported identifying 31 active sites with potentially eligible patients in five weeks. Fifteen sites activated Trial Connect; 27 patients were screened. Those are distinct steps, and keeping them separate makes the example useful. A promising record does not automatically become a willing participant. Staff still have to review eligibility and work with patients.

The copyable method is straightforward: test a protocol against the population before committing recruitment resources, then give practices a workable shortlist. It depends on the relevant clinical details being recorded and on sites having the capacity to act. Software cannot supply an absent measurement or an extra coordinator.

The price of making records useful

Building this infrastructure has required outside capital. Verana announced a $100 million investment led by GV in February 2020, alongside acquiring PYA Analytics’ technology and team. In January 2022, Johnson & Johnson Innovation - JJDC and Novo Growth led its $150 million Series E to advance products, services, and partnerships.

Its January 2026 merger with COTA added oncology depth and brought another $52 million in equity commitments. That figure is financing, not a purchase price. At the announcement, the combined company said it would serve 17 of the top 20 global biopharma companies and have access to more than 95 million patients.

Flatiron Health offers a direct alternative in oncology data. For buyers, the useful comparison concerns the disease, the variables, the follow-up, and whether the records support the proposed analysis. A larger database can still be the wrong database for a small, specific question.

A conversation with a paper trail

In September 2026, Verana introduced Agent Claire for Life Sciences, allowing conversational questions against disease-specific data. The revealing detail is its SQL output: researchers can inspect the underlying query. Easier access makes that review more consequential. An answer needs to reflect the clinical question someone intended to ask.

October brought a Bitfount collaboration combining EHR criteria with imaging eligibility flags generated locally from eye scans. It addresses another gap between a recorded diagnosis and actual trial eligibility. Verana’s research platform uses de-identified data; its patient-facing privacy explanation also describes third-party statistical verification and an opt-out route through providers.

The useful promise is modest enough to test: give research teams a better view of patients already receiving care. Start there, check the records, and follow the trail back to the practice. The waiting room has been there all along.