Synthetic Respondents in Pharma: Opportunity to fill Gaps, within Guardrails

Written by -Aritra Das,Nishant Agarwal,Rajan Dua

The Problem

“Without this foundation, synthetic respondents are little more than general-purpose LLMs speaking in character.”

Generative AI has made it possible to simulate HCPs, patients, caregivers, and payers for surveys and interviews, on demand. The promise is faster insight at lower cost, without the constraints of traditional recruitment. But the offerings on the market range widely, and many come with bold claims that don't hold up.

Ungrounded models mislead: without curated, domain-specific data, synthetic respondents produce generic answers that look convincing but aren't reliable.
Bold claims outpace reality: many off-the-shelf panels promise instant results that haven't been validated against real research.
Not every question fits: synthetic respondents cannot replace real people on radical innovations or emotionally complex topics.

A Disciplined Way to Use Synthetic Respondents

ProcDNA doesn't promote instant AI panels. Synthetic respondents get built from a client's own research data and grounded in a deep understanding of the relevant therapy area, so outputs reflect real market dynamics rather than generic pattern-matching.

Relevance: trained on therapeutic-area-specific evidence that reflects current market realities.
Rigor: validated against past research and hold-out samples before use.
Fidelity of variation: designed to mirror real-world diversity of opinion, not just an average response.
Governance: human-in-the-loop processes decide where synthetic insight is enough, and where real respondents remain essential.

Built for Insights and Marketing Teams

If you run market research or brand strategy, this paper speaks to the tradeoffs you weigh on every study.

Pilot faster, spend less. Use synthetic respondents to refine discussion guides and test research instruments before expensive fieldwork begins.
Shortlist with confidence. Narrow down concepts or messages quickly, then validate the finalists with real respondents.
Reach markets you can't easily survey. Simulate perspectives in rare disease or other hard-to-reach populations where recruitment is scarce.
Know where the line is. A clear framework tells you when synthetic respondents are appropriate, and when only real people will do.

What Changes

Cycle time reduction: weeks or months saved in insights generation
Cost efficiency through lower recruitment and fieldwork spend
Breadth of perspectives, including under-represented geographies and rare subpopulations
Validation accuracy, measured against human-derived insights

These are the same measures ProcDNA uses internally to judge whether a deployment is working.

What You'll Learn

What synthetic respondents are, and why grounding in real data determines whether they help or mislead.
Where synthetic respondents create the most value today, mapped across four therapy areas.
ProcDNA's playbook for piloting synthetic respondents, including what to use them for and what to avoid.
A phased approach for scaling synthetic respondents responsibly, from pilot to governance.
Where this capability is headed next, and how to prepare for it.

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