Synthetic Data for Pharma and Healthcare Market Research

Guides · April 5, 2026 · Myles Friedman · 7 min read

Synthetic survey data enables pharmaceutical companies and healthcare organizations to run physician, HCP, and patient research studies in minutes instead of weeks — at roughly one-tenth the cost of traditional panels. Simsurveys offers two domain-specific models for healthcare research: a Healthcare (HCP) model trained on a database of all licensed U.S. physicians and their prescription history, and a Patient model trained on 500,000+ de-identified federal health records. Both have been validated against published benchmark surveys.

This guide covers what each model does, how it has been validated, and where synthetic data fits — and where it does not — in pharma and healthcare market research.

The Problem with Traditional Healthcare Research

Healthcare market research is among the most expensive and logistically demanding forms of survey research. The barriers are structural, not incidental, and they affect every stage of a study.

Physician panels typically cost $75–$150 per complete, take 4–8 weeks to field, and are notoriously difficult to recruit for specialist audiences. A 500-physician study can run $64,500–$123,500 before analysis. Specialists like oncologists and surgeons command even higher incentives and longer timelines.

Patient panels are harder still. Condition verification, IRB approvals, and recruitment logistics push timelines to 8–12 weeks. Rare disease populations are nearly impossible to recruit at statistical significance. Even common chronic conditions require careful screening to ensure respondents match the target population.

The result is predictable: most healthcare research questions go unanswered because the logistics are prohibitive. Teams default to smaller sample sizes, fewer concept tests, or no primary research at all. Decisions that should be informed by physician and patient data are instead made on instinct or secondary sources.

The cost gap is enormous. A traditional 500-physician panel study costs $64,500–$123,500 and takes 4–8 weeks. The same study run synthetically costs a fraction of that and delivers results in minutes. That difference does not just save budget — it changes which questions get asked in the first place.

Synthetic HCP and Physician Data

The Simsurveys Healthcare (HCP) model is trained on a database of all licensed U.S. physicians and their prescription history. It covers 15+ specialties including primary care, oncology, cardiology, neurology, dermatology, endocrinology, rheumatology, and surgery.

The model generates synthetic physician responses that reflect real-world prescribing patterns, clinical attitudes, and practice characteristics. Researchers can target by specialty, practice setting, years in practice, and geographic region.

We have validated the Healthcare model against three published benchmark surveys, each testing different aspects of physician attitudes and clinical practice:

AMA Prior Authorization Survey: 17 questions on care delays, administrative burden, and prior authorization impact. The model achieved a KL divergence of 0.039 on care delay questions — well within accepted thresholds for distribution similarity.
Physician Sarcopenia Study: Familiarity and screening behavior among physicians. The model achieved a KL divergence of 0.044 on familiarity questions and a rank-biased overlap (RBO) of 0.981 on screening triggers — near-perfect rank agreement.
Commonwealth Fund/KFF Primary Care Survey: 45+ questions covering practice satisfaction, burnout, care delivery, and payment models. The model achieved a KL divergence of 0.006 on practice satisfaction — indicating distributions that are nearly indistinguishable from the live survey.

Common use cases for synthetic HCP data include advisory board simulation, prescribing pattern research, market access studies, competitive intelligence, and message testing with physician audiences.

Synthetic Patient Data

The Simsurveys Patient model is trained on 500,000+ de-identified records drawn from six publicly available federal health datasets: NHIS, BRFSS, MEPS, NHANES, CAHPS, and PROMIS. It covers general adult patients, pediatric populations, and chronic disease cohorts by condition.

The model generates synthetic patient responses that reflect real-world health status, treatment experiences, care access, and patient-reported outcomes. Researchers can target by condition, demographics, insurance status, and geographic region.

We have validated the Patient model against three published benchmark surveys spanning drug attitudes, hospital experience, and chronic pain:

KFF GLP-1 Health Tracking Poll: 20 questions on GLP-1 drug awareness, usage, and attitudes. The model achieved an average KL divergence of 0.039 (median 0.033) across all questions.
HCAHPS Hospital Experience: 21 questions on hospital care quality, validated against 631,000 live survey responses from CMS. The model achieved an average KL divergence of 0.091 — strong performance against one of the largest healthcare survey programs in the United States.
US Pain Foundation Chronic Pain Survey: 17 questions on pain management, treatment satisfaction, and quality-of-life impact. The model achieved an average KL divergence of 0.029, with all 17 questions rated “Good” on our validation scale.

No PHI. No HIPAA concerns. All Patient model training data comes from publicly available, de-identified federal datasets. There is no protected health information in the pipeline. Researchers can generate synthetic patient data without engaging a panel company, navigating IRB timelines, or managing patient consent workflows.

Common use cases for synthetic patient data include treatment satisfaction studies, patient-reported outcomes research, drug attitude surveys, health equity analysis, and patient journey mapping across conditions. Full technical details are available in our Patient Digital Twin white paper.

Pharma Research Applications

Synthetic survey data fits naturally into pharmaceutical research workflows where speed, cost, or access constraints limit what traditional panels can deliver. Here are the most common applications:

Drug launch planning: Test positioning, messaging, and value propositions with synthetic physician and patient audiences before committing to full-scale panel studies. Run 10 concept variations in the time it takes to field one traditional survey.
Advisory board preparation: Simulate physician responses to refine discussion guides, identify key objections, and prioritize topics before convening a live advisory board.
Patient journey mapping: Understand care experiences, treatment decision points, and unmet needs across conditions — including rare diseases where live patient recruitment is impractical.
Competitive intelligence: Get rapid reads on prescribing preferences, brand perceptions, and market dynamics without the lead time of a traditional tracker.
Payer research: Model healthcare professional attitudes toward coverage, formulary placement, and access barriers using synthetic HCP respondents.

In each case, the value is not just cost savings — it is the ability to ask questions that would otherwise go unasked because traditional research is too slow or too expensive. See our pricing page for current rates.

When Synthetic Beats Traditional in Healthcare

Synthetic data is not a blanket replacement for traditional healthcare panels. It is strongest in specific scenarios where the traditional approach hits structural limitations:

Early-stage screening: Test 10 concepts, messages, or positioning strategies before investing in a single live panel study. Use synthetic data to narrow the field, then validate the top candidates with real respondents.
Hard-to-reach specialists: Oncologists, surgeons, rare disease experts, and other high-value physician segments are expensive and slow to recruit. Synthetic HCP data provides directional reads without the recruitment bottleneck.
Speed-sensitive decisions: Launch timelines, competitive responses, and regulatory milestones do not wait for 8-week field periods. Synthetic data delivers results in minutes when the decision window is narrow.
Budget optimization: Use synthetic data for exploration and hypothesis generation, then allocate traditional panel budget to the studies that matter most. This approach stretches research budgets further without sacrificing rigor on high-stakes questions.

The best healthcare research programs use synthetic and traditional data together — synthetic for breadth and speed, traditional for depth and confirmation.

Limitations in Healthcare Research

Synthetic data has real limitations in healthcare contexts, and researchers should understand them before designing a study.

Highly specialized factual knowledge: Exact prevalence rates for niche conditions, specific clinical protocol details, and emerging treatment data may be weaker in synthetic responses. The models reflect patterns in their training data, not real-time clinical knowledge.
Rare conditions with very small populations: Conditions affecting fewer than a few thousand patients nationally require careful interpretation. The models have less training signal for ultra-rare diseases.
Clinical trials and regulatory submissions: Synthetic survey data is not a replacement for clinical trial data and should not be used for regulatory filings. It is a market research tool, not a clinical evidence tool.
High-stakes decisions in isolation: For decisions with significant financial or clinical consequences, synthetic data works best alongside traditional research rather than as the sole evidence base.

We publish full validation reports for every benchmark study, including question-level metrics, distribution comparisons, and subgroup analyses. All reports are available on our publications page, and our validation methodology is fully documented.

Getting Started

Both the Healthcare (HCP) model and Patient model are available now. You can create a free account and run your first synthetic healthcare study in minutes — no panel partner, no IRB, no recruitment wait.