Real-world evidence: from buzzword to procurement requirement

The shift

Real-world evidence was, for a decade, mostly the province of pharma. It is now increasingly required in software and device procurement — buyers want to see how the product performs outside the trial environment.

What buyers ask for

Site-specific outcomes, subgroup analyses, and time-series performance data. Vendors that cannot produce these are being screened out.

Building the capability

RWE is a capability, not a document. It requires data infrastructure, biostatistics talent, and clinical partnerships. Start early.

Publishing

Peer-reviewed publication remains the gold standard. Preprints are useful but do not substitute. Plan for a publication cadence, not a one-off paper.

How the ask changed shape

A few years ago, real-world evidence requests in procurement conversations were vague gestures toward wanting to see the product work outside a controlled trial. Today the ask is far more specific: named comparator groups, defined outcome windows, and a stated analytic method, often modeled on the same frameworks payers use internally for their own coverage reviews.

This specificity is a double-edged development for vendors. It removes ambiguity about what will satisfy a buyer, but it also means a thin or opportunistically framed evidence package is now easy for a sophisticated reviewer to spot and reject, where previously it might have passed on the strength of a confident narrative.

The internal muscle most companies lack

Generating credible real-world evidence requires a combination of biostatistics, health-economics framing, and data engineering that most digital health startups simply do not staff for at seed or Series A. The instinct is often to outsource this entirely to a consultancy near the point of first sale, which produces evidence that is technically defensible but disconnected from the product roadmap.

The companies handling this well build a lightweight internal analytics function earlier than seems necessary, even if it is a single hire, so that evidence generation is continuous rather than a scramble triggered by a specific deal. Continuous measurement also means the company discovers its own weak outcomes before a buyer's analyst does.

When evidence generation becomes a liability

There is a genuine tradeoff in publishing real-world results before a company is confident in them. A single ambiguous or negative finding, even one with reasonable methodological explanations, can circulate among payer analysts far longer than it takes a company to generate a stronger follow-up study. Some founders reasonably choose to delay disclosure until results are robust.

The more defensible posture is not to avoid publication but to be deliberate about sequencing: run the internal study first, address the weaknesses it surfaces, and only then move toward a public or semi-public evidence package. Rushing to publish for competitive reasons before the data is solid tends to cost more credibility than it buys in speed.

The operator view

Build the evidence generation function as if a payer will ask for it in twelve months, not as a response to an active deal. That lead time is usually what separates a company that can answer procurement's methodology questions in the room from one that has to go quiet and return weeks later.

It is also worth treating internal evidence generation as a product feedback loop rather than purely a sales enablement exercise. The same analysis that convinces a payer often reveals where the product itself underperforms for a specific subgroup, which is valuable regardless of whether a deal is on the table.

Where the data actually comes from

Much of the real-world evidence now demanded by payers has to be assembled from claims data, EHR extracts, and patient-reported outcomes that were never designed to answer a specific coverage question, which means a meaningful share of the analytic work is data cleaning and linkage rather than the statistical analysis itself. Vendors who underestimate this step often miss committed timelines by months.

Companies that plan for this reality budget for a data engineering phase before any analysis begins, and treat the linkage and cleaning work as a recurring capability rather than a one-time project tied to a single payer ask.

The risk of overfitting evidence to one buyer

A study designed narrowly to satisfy one payer's specific methodology preferences can be far less persuasive to the next payer, who may weight a different outcome measure or comparator population entirely. Teams that build a single bespoke study per deal end up repeating expensive analytic work rather than compounding it.

The more durable approach designs a core evidence package flexible enough to be re-cut for different audiences, with a shared underlying dataset and methodology that can support multiple payer-specific summaries without rerunning the entire analysis from scratch each time.