Mark64oswald Healthsim Agent Populationsim

PopulationSim - Population Intelligence & Cohort Generation

Overview

PopulationSim provides population-level intelligence using public data (Census ACS, CDC PLACES, SVI, ADI) for:

1. Standalone Analysis: Geographic profiling, health disparities, population comparisons
2. Cross-Product Integration: Cohort specs driving generation in PatientSim, MemberSim, RxMemberSim, TrialSim

Key Differentiator: PopulationSim analyzes real population characteristics and creates specifications — it does not generate synthetic records itself.

Quick Reference

Safety Guardrails

All Generated Data is Synthetic

PopulationSim outputs synthetic, fictional, simulated data — never real patient records. All profiles and cohort specs are derived from aggregated public statistics and must not be treated as real patient data.

Do NOT:

• Present synthetic data as actual patient records
• Make clinical recommendations (e.g., "you should prescribe," "the patient needs") based on generated data
• Pull from or reference real patient databases — use only public reference data

Do:

• Remind users that all generated data is synthetic test data
• Use real, valid medical code systems for standards: ICD-10 (diagnoses), CPT/HCPCS (procedures), LOINC (labs), SNOMED (clinical terms), RxNorm/NDC (medications), NPI (providers)
• Use real public reference data (Census ACS, CDC PLACES, SVI, ADI) for population characteristics

Negative Examples — What PopulationSim Must NOT Do

Edge Cases

• Missing FIPS: Validate inputs; return clear error if FIPS not found in crosswalk files
• Partial data: Some tracts lack PLACES or SVI coverage — flag gaps rather than imputing zeros
• Invalid codes: Only emit ICD-10, CPT, LOINC, RxNorm, NDC codes from recognized systems

Output Types

PopulationProfile

Geographic entity with demographics, health indicators, and SDOH indices:

{
  "geography": {
    "type": "county",
    "fips": "06073",
    "name": "San Diego County",
    "state": "CA",
    "region": "Pacific"
  },
  "demographics": {
    "total_population": 3286069,
    "median_age": 37.1,
    "age_distribution": {
      "0-17": 0.21,
      "18-64": 0.62,
      "65+": 0.17
    },
    "race_ethnicity": {
      "white_nh": 0.43,
      "hispanic": 0.34,
      "asian": 0.12,
      "black": 0.05,
      "other": 0.06
    },
    "median_household_income": 102285,
    "poverty_rate": 0.103
  },
  "health_indicators": {
    "source": "CDC_PLACES_2024",
    "diabetes_prevalence": 0.095,
    "obesity_prevalence": 0.280,
    "hypertension_prevalence": 0.285,
    "depression_prevalence": 0.195,
    "smoking_prevalence": 0.098
  },
  "sdoh_indices": {
    "svi_overall": 0.42,
    "svi_themes": {
      "socioeconomic": 0.38,
      "household_composition": 0.45,
      "minority_language": 0.52,
      "housing_transportation": 0.35
    },
    "adi_national_rank": 35
  },
  "healthcare_access": {
    "uninsured_rate": 0.071,
    "pcp_per_100k": 82.4,
    "insurance_mix": {
      "employer": 0.52,
      "medicare": 0.15,
      "medicaid": 0.18,
      "individual": 0.08,
      "uninsured": 0.07
    }
  }
}

CohortSpecification

Generation input for other HealthSim products:

{
  "cohort_id": "houston_diabetics_2024",
  "name": "Houston Metro Diabetic Adults",
  "target_size": 10000,
  "geography": {
    "type": "msa",
    "cbsa_code": "26420",
    "name": "Houston-The Woodlands-Sugar Land, TX"
  },
  "demographics": {
    "age": {
      "min": 18, "max": 85, "mean": 58.4,
      "brackets": { "18-44": 0.18, "45-64": 0.42, "65-74": 0.28, "75+": 0.12 }
    },
    "sex": { "male": 0.48, "female": 0.52 },
    "race_ethnicity": { "white_nh": 0.28, "black": 0.22, "hispanic": 0.38, "asian": 0.08 }
  },
  "clinical_profile": {
    "primary_condition": { "icd10": "E11", "name": "Type 2 Diabetes" },
    "comorbidities": {
      "I10": { "name": "Hypertension", "rate": 0.71 },
      "E78": { "name": "Hyperlipidemia", "rate": 0.68 },
      "E66": { "name": "Obesity", "rate": 0.62 }
    }
  },
  "sdoh_profile": {
    "poverty_rate": 0.18,
    "uninsured_rate": 0.16,
    "food_insecurity": 0.15,
    "svi_mean": 0.58
  },
  "z_code_rates": {
    "Z59.6": { "name": "Low income", "rate": 0.18 },
    "Z59.41": { "name": "Food insecurity", "rate": 0.15 }
  },
  "insurance_mix": {
    "medicare": 0.38, "medicaid": 0.22, "commercial": 0.32, "uninsured": 0.08
  }
}

Cross-Product Integration

Integration Flow

                    ┌─────────────────────┐
                    │   PopulationSim     │
                    │  CohortSpecification│
                    └──────────┬──────────┘
                               │
           ┌───────────────────┼───────────────────┐
           │                   │                   │
           ▼                   ▼                   ▼
    ┌─────────────┐     ┌─────────────┐     ┌─────────────┐
    │ PatientSim  │     │ MemberSim   │     │  TrialSim   │
    │ - patients  │     │ - members   │     │ - subjects  │
    │ - diagnoses │     │ - claims    │     │ - diversity │
    │ - SDOH codes│     │ - plans     │     │ - sites     │
    └──────┬──────┘     └──────┬──────┘     └─────────────┘
           │                   │
           └─────────┬─────────┘
                     ▼
              ┌─────────────┐
              │ RxMemberSim │
              │ - Rx claims │
              │ - adherence │
              └─────────────┘

Integration Patterns

Data Sources (Embedded v2.0)

Embedded data package (148 MB, 100% US coverage):

DuckDB Reference Tables

Reference data also available in DuckDB:

See Data Architecture for details.

Directory Structure

skills/populationsim/
├── SKILL.md                           # This file - master router
├── README.md                          # Product overview
├── population-intelligence-domain.md  # Core domain knowledge
│
├── data/                              # Embedded Data Package (v2.0)
│   ├── README.md                      # Data dictionary
│   ├── county/                        # County-level files
│   ├── tract/                         # Tract-level files
│   ├── block_group/                   # Block group files (ADI)
│   └── crosswalks/                    # FIPS and CBSA mappings
│
├── data-access/                       # Data Access Skills (v2.0)
│   ├── README.md                      # Category overview
│   ├── data-lookup.md                 # Direct value lookups
│   ├── geography-lookup.md            # FIPS code resolution
│   └── data-aggregation.md            # Geographic aggregation
│
├── geographic/                        # Geographic Intelligence
│   ├── README.md                      # Category overview
│   ├── county-profile.md              # County-level profiles
│   ├── census-tract-analysis.md       # Tract-level analysis
│   ├── metro-area-profile.md          # MSA/CBSA profiles
│   └── custom-region-builder.md       # Custom region aggregation
│
├── health-patterns/                   # Health Analysis
│   ├── README.md                      # Category overview
│   ├── chronic-disease-prevalence.md  # Disease burden analysis
│   ├── health-behavior-patterns.md    # Risk factor analysis
│   ├── healthcare-access-analysis.md  # Coverage and access
│   └── health-outcome-disparities.md  # Disparity analysis
│
├── sdoh/                              # Social Determinants
│   ├── README.md                      # SDOH framework overview
│   ├── svi-analysis.md                # Social Vulnerability Index
│   ├── adi-analysis.md                # Area Deprivation Index
│   ├── economic-indicators.md         # Income, poverty, employment
│   └── community-factors.md           # Housing, transportation, food
│
├── cohorts/                           # Cohort Definition
│   ├── README.md                      # CohortSpecification schema
│   ├── cohort-specification.md        # Complete cohort definition
│   ├── demographic-distribution.md    # Age, sex, race distributions
│   ├── clinical-prevalence-profile.md # Comorbidity patterns
│   └── sdoh-profile-builder.md        # SDOH and Z-code rates
│
└── trial-support/                     # Clinical Trial Support
    ├── README.md                      # Trial integration overview
    ├── feasibility-estimation.md      # Eligible population modeling
    ├── site-selection-support.md      # Site network optimization
    └── enrollment-projection.md       # Timeline projections

Quick Examples

Example 1: County Health Profile

Request: "Profile San Diego County health indicators"

Response:

San Diego County, CA (FIPS 06073) Population Profile:

Demographics (ACS 2022 5-year):

• Total Population: 3,286,069
• Median Age: 37.1 years
• Race/Ethnicity: 43% White NH, 34% Hispanic, 12% Asian, 5% Black

Health Indicators (CDC PLACES 2024):

• Diabetes: 9.5% (vs 10.1% national)
• Obesity: 28.0% (vs 32.1% national)
• Hypertension: 28.5% (vs 32.4% national)

SDOH Indices:

• SVI Overall: 0.42 (moderate vulnerability)
• ADI National Rank: 35th percentile

Example 2: Disparities by SVI Quartile

Request: "How do health outcomes vary across SVI quartiles?"

Response: Stratification by SVI quartile shows clear outcome variation:

Disparity: Q4 tracts show higher prevalence vs Q1 across all indicators; lower-vulnerability areas have better outcomes. See health-patterns/health-outcome-disparities.md.

Example 3: Cohort for PatientSim

Request: "Define a cohort of high-risk diabetics in underserved California"

Response: Creates CohortSpecification with:

• Geography: CA census tracts with SVI ≥ 0.70
• Demographics: Hispanic 58%, mean age 58.4
• Comorbidities: HTN 71%, obesity 62%
• SDOH: Rx cost barrier 31%, food insecurity 22%

Example 4: Trial Feasibility

Request: "Feasibility for T2DM trial: age 40-70, HbA1c 8-11%"

Response:

Top Metros: Houston (128K), Miami (115K), Los Angeles (108K)

Related Products

• PatientSim - Clinical patient data
• MemberSim - Health plan member data
• RxMemberSim - Pharmacy data
• TrialSim - Clinical trial data
• NetworkSim - Provider networks

Domain Knowledge

See Population Intelligence Domain for geographic hierarchy, census data, and SDOH frameworks.

Generative Framework Integration

PopulationSim feeds the Generative Framework via CohortSpecifications that drive synthetic generation in PatientSim, MemberSim, and TrialSim.

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