Product updates, research insights, and perspectives on the future of AI-powered survey research.
Synthetic survey data is AI-generated survey responses that statistically replicate how real people answer research questions. How it works, when to use it, and how to validate it.
How pharmaceutical companies and healthcare organizations use synthetic survey data for physician, HCP, and patient research. Validated against AMA, KFF, HCAHPS, and US Pain Foundation benchmarks.
Side-by-side comparison of synthetic survey data and traditional panel research. Cost, speed, accuracy, and when to use each approach.
Synthetic respondents are AI-generated survey participants that replicate real population response patterns. How they work, how they're validated, and when to use them.
Synthetic physician data validated against the AMA Prior Authorization Survey. 17 questions on care delays, treatment abandonment, and insurer burden. KL divergence 0.039 on care delay questions.
Validation comparing synthetic consumer responses against the IFIC 2025 Food & Health Survey—3,000 U.S. adults on dietary attitudes, nutrition awareness, and food processing perceptions.
Consumer model validated against Walmart's Retail Rewired Report on trust in AI recommendations, digital assistant attitudes, and agentic retail experiences.
Patient model validated against the KFF July 2023 Health Tracking Poll on GLP-1 drugs. 20 questions, average KL divergence 0.039, median 0.033. Untrained model—most conservative test.
Baseline validation against HCAHPS—the CMS-mandated patient experience benchmark. 21 questions, ~631,000 live surveys from 4,304 hospitals.
Healthcare model validated against the Commonwealth Fund/KFF 2015 National Survey of Primary Care Providers. 45+ questions on practice patterns, ACA impact, and payer mix.
Consumer model validated against the Happy Returns and NRF 2024 study—2,007 consumers on holiday returns, free return policies, and return behaviors.
Healthcare model validated against a live physician survey on sarcopenia awareness, screening practices, and diagnostic approaches. Live n=253, simulated n=1,000.
Patient model validated against the US Pain Foundation's 2022 national chronic pain survey. 17 questions, average KL 0.029, median 0.011. All questions rated “Good.”
A framework for evaluating synthetic survey data as model-based estimators. Covers Total Survey Error, question-type-aware metrics, and when synthetic estimates may outperform live samples.
How synthetic respondent systems serve as the preference layer for AI agents making purchasing decisions. The timing constraint that traditional research cannot solve.
How Simsurveys builds patient digital twins from 500,000+ de-identified federal health records. Architecture, data sources, and validation evidence.
AI agents are fast, efficient, and have no idea what people actually think. The Simsurveys Oracle gives them that missing piece—real human preference, in real time.
A new domain-specific AI model for patient research, trained on 500,000+ de-identified federal health records from NHIS, BRFSS, MEPS, NHANES, CAHPS, and PROMIS. No panel partner required.
I launched one of the first web survey tools in 1999. The industry said phone interviewing couldn’t be replaced. Twenty-five years later, here’s why the same argument against synthetic data is wrong.
Meet Oracle Voices — our qualitative research platform that brings AI-powered digital panelists to focus groups, in-depth interviews, and ethnographic studies. Run rich qualitative research at a fraction of the time and cost.