A case for robust expert qualification in healthcare research
Editor’s note: Michelle Higginson is co-founder and EVP of Inlightened, where she helps life sciences and healthcare organizations make better commercial, marketing and innovation decisions by connecting them with specialized industry expertise. A healthcare technology marketing executive with nearly 15 years of experience, Higginson has built and led marketing functions. She earned both her B.S. in marketing and an accelerated MBA from Bentley University. She also serves on the board of Easterseals Massachusetts. Find Higginson on LinkedIn.
A few years ago, if someone wanted to impersonate a physician in a paid healthcare research study, it took effort. Today, all it takes is a convincing LinkedIn profile, an AI-generated headshot and a few minutes of research.
The healthcare industry has spent the last several years racing to integrate AI into research workflows. Faster synthesis. Smarter survey design. Real-time analysis of qualitative data. For many applications, the tools are genuinely good.
But while the industry is investing heavily in AI, it is overlooking something more fundamental: verifying that the experts generating insights are who they say they are. AI innovations are making it easier for fraudsters to misrepresent themselves, and harder to trust that insights are being sourced from the qualified professionals they’re meant to.
As the co-founder of a healthcare expert network, I spend my days connecting life sciences companies, startups, consultancies and market research organizations with the healthcare professionals they need to inform decisions and build solutions. From formulary committee physicians to nurses making bedside technology choices to specialists shaping treatment protocols, the quality of those connections is everything.
Increasingly, I believe healthcare market research has a participant authentication problem, and industry standards have not kept pace with the incentives for fraud. The process of qualifying credentials and confirming identity is far more fragile and fraught than most want to admit.
The honor system is not a security model.
When a pharmaceutical company commissions expert interviews to inform a drug launch strategy, or when a medical device company surveys specialists to pressure test a market access plan, they are making high-stakes decisions based on the assumption that respondents are credentialed professionals with real clinical experience. That assumption is usually correct.
But the mechanisms most platforms and recruiters use to verify it are surprisingly thin: a name, an NPI number, sometimes a LinkedIn profile and the assumption that the barrier to impersonation is high enough to deter bad actors. Unfortunately, it isn’t anymore.
The financial incentives for fraudulent participation in healthcare research have grown substantially as market research budgets have scaled, provider availability has become increasingly scarce and per-interview compensation has risen. Synthetic identities have become easier to construct. The shift toward remote, asynchronous research formats, accelerated and normalized during the pandemic, removed much of the ambient verification that in-person settings provided.
In an in-person setting, you could physically see a participant. You could verify an ID. You could ask questions that quickly exposed inconsistencies. Today, many of those signals are absent.
Not only is this problem solvable, it is deeply human. It requires investment and process discipline, but it is not technically complex.
Closing the gap: Expert verification standards
Real verification means cross-referencing against primary sources: licensing boards, hospital credentialing systems, professional society memberships. It means using identity verification infrastructure – or investing in biometric solutions – that already exists in adjacent healthcare applications, not building a parallel system from scratch. It means treating expert sourcing with the same rigor that clinical trial enrollment receives. Quality control.
It also requires asking different questions during expert qualification. Not just the usual screeners – even highly detailed ones – but additional validation through channels that can be independently confirmed. It requires a commitment to surfacing inconsistencies that simple attestation does not.
Most insights providers do not do this. Thorough verification is slower and more expensive than asking someone to check a box. In a competitive market where speed and cost drive procurement decisions, quality can feel like a luxury.
The client companies commissioning this research have a role here too. Expert verification standards are rarely outlined in vendor RFPs. Red flags and audits are not automatic when research feels unusually clean, fast or uniform. The assumption of integrity is baked into the procurement process in a way that does not reflect the current environment.
Healthcare insights inform real decisions. The medical director survey that shapes a formulary strategy affects what drugs patients can access. The clinician interviews that pressure test a care delivery model influence how care gets organized. The supply chain leader input that informs a device pricing model shapes what systems can afford to buy. When the expert layer is contaminated, those decisions are downstream of noise at best and deliberate manipulation at worst.
The industry has developed sophisticated methods for minimizing bias, controlling for survey fatigue and triangulating across data sources. It has not developed equally sophisticated methods to continuously validate identities, credentials and qualifications to ensure research panels consist exclusively of vetted experts. That gap needs to be closed.
The good news is that the companies building and running expert networks are capable of doing so. Some are already investing in this infrastructure. But the market will not demand it at scale until buyers start asking harder questions. The industry must acknowledge that expert integrity and compliance is a foundational research quality issue.
AI tools will continue to improve and research workflows will continue to accelerate. But none of that matters if the industry cannot trust the identities of the experts informing its decisions.
Healthcare research organizations spend enormous energy validating data. It’s time to apply the same rigor to validating the people at its source.