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Domain Models

Patient AI Model

Generate validated patient responses for healthcare experience, treatment satisfaction, and pharmaceutical research with AI trained on 500,000+ de-identified federal patient records.

Model Overview

The patient model specializes in research from the patient perspective — healthcare experiences, treatment satisfaction, medication attitudes, and condition-specific outcomes. Built on the consumer model as a foundation and fine-tuned on over 500,000 de-identified patient records from federally mandated public health surveys, the model generates responses that reflect how real patients experience the healthcare system.

Unlike the Healthcare (HCP) model, which focuses on physician decision-making and prescribing behavior, the patient model captures the patient side: how people navigate conditions, evaluate treatments, interact with providers, and make healthcare decisions.


Key Capabilities

Patient Experience

Hospital satisfaction, provider communication, care quality, and care transitions.

Treatment Satisfaction

Medication attitudes, side effect concerns, adherence patterns, and willingness to try new treatments.

Chronic Disease

Condition management, functional limitations, mental health comorbidities, and quality of life.

Drug & Pharma Attitudes

Trust in pharmaceutical companies, drug pricing perceptions, insurance coverage, and cost-related non-adherence.

Patient-Provider Relationships

Trust in providers, communication quality, shared decision-making, and care coordination.

Health Behaviors

Preventive care, health literacy, lifestyle factors, and social determinants of health.


Research Applications

  • Pharmaceutical patient experience and treatment satisfaction studies
  • Hospital and health system quality measurement
  • Condition-specific patient research (diabetes, heart disease, COPD, pain, cancer, and more)
  • Drug attitude, awareness, and willingness-to-try studies
  • Patient-reported outcome research
  • Medical device patient experience studies
  • Health equity and underserved population research
  • Mental health and stigma research

Validated Populations

  • General adult patients (U.S. population)
  • Pediatric patients (child health data from NHIS)
  • Chronic disease patients by condition
  • Hospital patients (inpatient and outpatient)
  • Patients by demographic, insurance, and geographic segments

Pediatric research: The patient model includes child health data from the National Health Interview Survey, covering conditions, healthcare access, functional limitations, and developmental health for pediatric populations — one of the hardest segments to recruit for in traditional patient research.


Model Training & Data Sources

Built on the consumer model as a foundation and fine-tuned on publicly available, de-identified federal health datasets:

  • NHIS (National Health Interview Survey) — ~30,000 households/year covering chronic conditions, healthcare access, functional status, and mental health. Includes both adult and child components.
  • BRFSS (Behavioral Risk Factor Surveillance System) — 457,000+ respondents covering health behaviors, chronic conditions, and preventive care
  • MEPS (Medical Expenditure Panel Survey) — 18,000+ respondents covering healthcare utilization, patient experience, and insurance
  • NHANES (National Health and Nutrition Examination Survey) — ~15,000 respondents with clinical and self-reported health data
  • CAHPS (Consumer Assessment of Healthcare Providers and Systems) — national patient experience benchmarks across hospitals and care settings
  • PROMIS (Patient-Reported Outcomes Measurement Information System) — 2,078 validated items across 109 patient health domains

Data integrity: All training data is publicly available and has been de-identified at the source through federal disclosure avoidance protocols. No protected health information (PHI) is present. No HIPAA regulations have been violated.


Validation

The patient model has been validated against published patient survey data across multiple research domains:

KFF GLP-1 Drug Poll

20 questions on drug awareness, pharma trust, affordability, and insurance. Average KL divergence: 0.039. Validated against the Kaiser Family Foundation Health Tracking Poll (n=1,327).

Download report →

HCAHPS Hospital Experience

21 questions across 7 domains of hospital patient experience. Average KL divergence: 0.091. Validated against CMS national data (~631,000 surveys from 4,304 hospitals).

Download report →

US Pain Foundation Survey

17 questions on chronic pain, mental health, patient-provider relationships, and treatment experience. Average KL divergence: 0.029. Validated against published data (n=2,275 chronic pain patients).

Download report →

Further Reading

For a deeper look at the architecture and methodology behind patient survey simulation, see our white paper:

The digital twin approach to survey research has also been explored in peer-reviewed academic literature, including Toubia et al. (2025) in Marketing Science.


Compliance & Ethics

All patient training data is sourced from publicly available federal health surveys that have been de-identified at the source through rigorous federal disclosure avoidance protocols. No protected health information (PHI) is present in any training data. Simsurveys maintains encryption, access control, monitoring, confidentiality agreements, and secure data retention/deletion consistent with SOC 2 and HIPAA principles.

View detailed security and compliance practices →

Ready to conduct patient research?

Generate validated patient responses with our specialized patient AI model.

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