HealthSim - Synthetic Healthcare Data Generation

Overview

HealthSim generates realistic synthetic healthcare data through natural conversation. Rather than writing code or configuration files, describe what you need and Claude generates appropriate data.

Products:

Product	Domain	What It Generates	Status
PatientSim	Clinical/EMR	Patients, encounters, diagnoses, procedures, labs, vitals, medications	Active
MemberSim	Payer/Claims	Members, professional claims, facility claims, payments, accumulators	Active
RxMemberSim	Pharmacy/PBM	Prescriptions, pharmacy claims, formularies, DUR alerts, prior auths	Active
TrialSim	Clinical Trials	Studies, sites, subjects, visits, adverse events, efficacy, CDISC output	Active
PopulationSim	Demographics/SDOH	Population profiles, cohort specifications, health disparities, SVI/ADI analysis	Active
NetworkSim	Provider Networks	Providers, facilities, pharmacies, networks, benefit structures	Active

Quick Start

Generate Clinical Data

Request: "Generate a 65-year-old diabetic patient with hypertension"

Claude will produce a patient with:

• Demographics (age 65, realistic name/address)
• Diagnoses (E11.9 Type 2 diabetes, I10 hypertension)
• Medications (metformin, lisinopril)
• Labs (A1C, BMP with values in expected ranges)
• Comorbidities (likely hyperlipidemia, possible obesity)

Generate Claims

Request: "Create a professional claim for an office visit"

Claude will produce:

• Claim header (provider NPI, member ID, service date)
• Service lines (CPT 99213/99214, charges)
• Diagnoses (ICD-10 codes)
• Adjudication (allowed, paid, patient responsibility)

Generate Pharmacy Data

Request: "Generate a pharmacy claim that triggers a drug interaction alert"

Claude will produce:

• Prescription details (NDC, quantity, days supply)
• Pharmacy claim (BIN, PCN, cardholder ID)
• DUR alert (DD code, clinical significance, recommendation)
• Claim response (approved with warning or rejected)

Scenario Skills

PatientSim Scenarios

Load these for clinical data generation:

Scenario	Use When	Key Elements
ADT Workflow	admission, discharge, transfer, patient movement	A01/A02/A03 events, bed management, census
Diabetes Management	diabetic, A1C, glucose, metformin, insulin	Disease progression, medication escalation, complications
Heart Failure	CHF, HFrEF, BNP, ejection fraction	NYHA classification, GDMT therapy, decompensation
Chronic Kidney Disease	CKD, eGFR, dialysis, nephrology	CKD staging, progression, comorbidities
Sepsis/Acute Care	sepsis, infection, ICU, critical	Sepsis criteria, antibiotic protocols, ICU stay
Orders & Results	lab order, radiology, ORM, ORU, results	Orders, specimens, lab panels, radiology reports
ED Chest Pain	chest pain, emergency, ACS, troponin	Risk stratification, HEART score, workup
Elective Joint	hip replacement, knee replacement, arthroplasty	Pre-op, surgery, recovery, PT
Maternal Health	pregnancy, prenatal, L&D, postpartum	Prenatal visits, GDM, preeclampsia, delivery
Oncology	cancer, tumor, chemotherapy, breast/lung/colorectal	Staging, treatment protocols, tumor markers

See: skills/patientsim/ for detailed skills

MemberSim Scenarios

Load these for claims and payer data:

Scenario	Use When	Key Elements
Plan & Benefits	plan, benefit plan, HMO, PPO, HDHP	Plan types, cost sharing, pharmacy tiers
Enrollment & Eligibility	enrollment, eligibility, 834, 270/271	Member add/term, coverage verification, QLE
Professional Claims	office visit, 837P, physician claim	E&M coding, place of service, adjudication
Facility Claims	hospital, inpatient, 837I, DRG	Revenue codes, DRG assignment, LOS
Prior Authorization	prior auth, pre-cert, authorization	Request/response workflow, approval criteria
Accumulator Tracking	deductible, OOP, accumulator	Year-to-date tracking, family vs individual
Value-Based Care	quality measures, VBC, HEDIS	Attribution, measure compliance, incentives
Behavioral Health	mental health, psychiatry, SUD, therapy	Psychotherapy, medication management, PHP/IOP

See: skills/membersim/ for detailed skills

RxMemberSim Scenarios

Load these for pharmacy and PBM data:

Scenario	Use When	Key Elements
Retail Pharmacy	prescription fill, retail, copay	New/refill, pricing, patient pay
Specialty Pharmacy	specialty drug, biologics, hub	Limited distribution, PA, patient support
DUR Alerts	drug interaction, DUR, therapeutic dup	Alert types, severity, override
Formulary Management	formulary, tier, coverage	Tier structure, PA requirements, alternatives
Rx Enrollment	rx enrollment, pharmacy member, BIN PCN	Pharmacy benefit eligibility, plan assignment
Rx Prior Auth	pharmacy PA, step therapy	Clinical criteria, approval workflow
Rx Accumulators	rx deductible, rx OOP, Part D phases	Pharmacy cost sharing tracking, TrOOP
Manufacturer Programs	copay card, patient assistance	Copay cards, PAPs, hub services

See: skills/rxmembersim/ for detailed skills

TrialSim Scenarios

Load these for clinical trial data generation:

Scenario	Use When	Key Elements
Clinical Trials Domain	trial concepts, phases, CDISC	Phase definitions, regulatory, standards
Recruitment & Enrollment	screening, enrollment, consent	Screening funnel, I/E criteria, randomization
Phase 1 Dose Escalation	Phase 1, FIH, MTD, 3+3, BOIN, CRM	Dose escalation, DLT, PK sampling
Phase 2 Proof-of-Concept	Phase 2, Simon's, MCP-Mod	Futility stopping, dose-response
Phase 3 Pivotal	Phase 3, pivotal, registration trial	Multi-site, endpoints, safety monitoring
Oncology Trials	oncology trial, tumor endpoints	RECIST, survival endpoints, biomarkers
Cardiovascular Trials	CV outcomes, MACE	Cardiac events, biomarkers
CNS Trials	CNS, Alzheimer's, MS	Cognitive scales, imaging
Cell & Gene Therapy	CAR-T, gene therapy, CGT	Long-term follow-up, CRS, ICANS
Dimensional Analytics	trial analytics, star schema, dashboard	fact/dim tables, DuckDB, Databricks

See: skills/trialsim/ for detailed skills

PopulationSim Scenarios

Load these for population intelligence and cohort definition:

Scenario	Use When	Key Elements
Geographic Profile	county profile, demographics for, MSA	County/tract/metro demographics, health indicators
Health Patterns	diabetes rate, prevalence, disparities	CDC PLACES measures, age-adjusted rates
SDOH Analysis	SVI, ADI, social vulnerability, deprivation	SVI themes, ADI rankings, barriers
Cohort Definition	define cohort, population segment	CohortSpecification for generation products
Trial Support	diversity planning, site selection, feasibility	FDA diversity, site ranking, enrollment projections

Key Differentiator: PopulationSim analyzes real population data (Census, CDC) and outputs specifications, not synthetic records. These specs drive realistic generation in PatientSim, MemberSim, and TrialSim.

See: skills/populationsim/ for detailed skills

NetworkSim Scenarios

Load these for provider network knowledge and entity generation:

Scenario	Use When	Key Elements
Network Types	HMO, PPO, EPO, POS, HDHP	Network definitions, cost/flexibility tradeoffs
Plan Structures	deductible, copay, coinsurance, OOP	Benefit design, cost sharing, accumulators
Pharmacy Benefits	tier structure, formulary, PBM	Tier design, formulary types, pharmacy networks
PBM Operations	BIN, PCN, claims processing, rebates	Claim flow, adjudication, manufacturer rebates
Utilization Management	prior auth, step therapy, QL	PA process, step requirements, quantity limits
Specialty Pharmacy	specialty drugs, hub model, REMS	Limited distribution, specialty services
Network Adequacy	access standards, time distance	Time/distance, provider ratios, ECPs
Provider Generation	generate provider, NPI, physician	Synthetic providers with taxonomy, credentials
Facility Generation	generate hospital, facility, CCN	Synthetic facilities with beds, services
Pharmacy Generation	generate pharmacy, NCPDP	Synthetic pharmacies with type, chain

See: skills/networksim/ for detailed skills

Generative Framework

Load these for batch generation and specification-driven data creation:

Scenario	Use When	Key Elements
Profile Builder	batch generation, cohort profile, population spec	Demographics, conditions, coverage definitions
Journey Builder	temporal patterns, event sequences, care pathways	Timelines, triggers, branching
Quick Generate	simple single-entity generation	Fast single patient/member/claim
Distributions	customize statistical patterns	Age, cost, utilization distributions
Templates	start from common patterns	Pre-built profiles and journeys

Key Differentiator: The Generative Framework enables specification-driven generation at scale. Build a profile, define a journey, then execute to generate hundreds or thousands of correlated entities across all products.

See: skills/generation/ for detailed skills

Common Skills

Cross-product infrastructure skills:

Skill	Use When	Key Elements
State Management	save scenario, load scenario, list scenarios	DuckDB persistence, scenario naming
Identity Correlation	link patient, find member, cross-product	SSN correlation, entity linking
DuckDB Skill	query database, SQL, schema	Direct database operations

See: skills/common/ for detailed skills

Output Formats

Default: JSON

By default, Claude outputs data as JSON objects that match the canonical data model.

Healthcare Standards

Request specific formats:

Format	Request Phrases	Use Case
FHIR R4	"as FHIR", "FHIR bundle", "FHIR resources"	Interoperability, modern APIs
C-CDA	"as C-CDA", "as CCD", "discharge summary", "referral note"	Clinical documents, HIE
HL7v2	"as HL7", "ADT message", "HL7v2"	Legacy EMR integration
X12 834	"as 834", "X12 enrollment", "enrollment file"	Benefit enrollment
X12 270/271	"as 270", "eligibility inquiry", "eligibility check"	Eligibility verification
X12 837	"as 837", "X12 claim", "EDI format"	Claims submission
X12 835	"as 835", "remittance", "ERA"	Payment posting
NCPDP D.0	"as NCPDP", "pharmacy claim format"	Pharmacy transactions
CDISC SDTM	"as SDTM", "SDTM domains"	Clinical trial regulatory submission
CDISC ADaM	"as ADaM", "analysis datasets"	Clinical trial statistical analysis

See: formats/ for transformation skills

Analytics Formats

Format	Request Phrases	Use Case
Dimensional (DuckDB)	"star schema for DuckDB", "dimensional model", "for analytics"	Local BI development
Dimensional (Databricks)	"star schema for Databricks", "load to Databricks", "Unity Catalog"	Enterprise analytics

See: formats/dimensional-analytics.md for star schema details

Export Formats

Format	Request Phrases
CSV	"as CSV", "save to CSV", "spreadsheet"
Parquet	"as Parquet", "for analytics"
SQL INSERT	"as SQL", "INSERT statements"

Generation Parameters

Demographics

Parameter	Default	Options
age_range	18-90	Any range, e.g., "pediatric (0-17)", "senior (65+)"
gender	weighted (49% M, 51% F)	"male", "female", specific distribution
count	1	Any number, batches for large counts

Clinical (PatientSim)

Parameter	Options
conditions	diabetes, heart failure, CKD, hypertension, COPD, etc.
severity	mild, moderate, severe, well-controlled, poorly-controlled
complications	with/without specific complications

Claims (MemberSim)

Parameter	Options
claim_type	professional, institutional, dental
claim_status	paid, denied, pending, partial
network_status	in-network, out-of-network

Pharmacy (RxMemberSim)

Parameter	Options
fill_type	new, refill
drug_type	generic, brand, specialty
dur_alerts	none, warning, reject

Reproducibility

For consistent results across sessions:

Request: "Generate 10 patients using seed 42"

Claude will:

1. Use seed 42 for all random selections
2. Generate identical output if same parameters used
3. Note the seed in output for reference

Validation

Claude automatically validates generated data for:

• Structural: Required fields, data types, formats
• Temporal: Date ordering (discharge after admission, etc.)
• Referential: Foreign key relationships
• Clinical: Age-appropriate conditions, gender-appropriate conditions
• Business: Valid code combinations, realistic pricing

Request explicit validation: "Validate this patient data"

Reference Data

For code lookups and documentation:

Reference	Description
Code Systems	ICD-10, CPT, HCPCS, LOINC, NDC, RxNorm
Terminology	Healthcare terminology and abbreviations
Clinical Rules	Clinical business rules and guidelines
Validation Rules	All validation rules and constraints
HL7v2 Segments	HL7v2 segment definitions (MSH, PID, OBR, OBX, etc.)

Format Transformations

Transform generated data to healthcare standards:

Healthcare Standards

Format	Skill	Use Case
FHIR R4	formats/fhir-r4.md	Modern interoperability, REST APIs
C-CDA	formats/ccda-format.md	CCD, Discharge Summary, clinical documents
HL7v2 ADT	formats/hl7v2-adt.md	Admit/Discharge/Transfer messages
HL7v2 ORM	formats/hl7v2-orm.md	Order messages (lab, rad, meds)
HL7v2 ORU	formats/hl7v2-oru.md	Results/observation messages
X12 837	formats/x12-837.md	Claims submission (P/I)
X12 835	formats/x12-835.md	Remittance/ERA
NCPDP D.0	formats/ncpdp-d0.md	Pharmacy transactions

Export Formats

Format	Skill	Use Case
CSV	formats/csv.md	Spreadsheets, data analysis, bulk export
SQL	formats/sql.md	Database INSERT statements, data loading
Dimensional	formats/dimensional-analytics.md	Star schema for DuckDB, Databricks, BI tools

Integration & Testing

Guide	Description
Integration Guide	Cross-skill data flows and consistency
Testing Patterns	Validation and test patterns

Examples

Example 1: Simple Patient

Request: "Generate a patient"

Output:

{
  "mrn": "MRN00000001",
  "name": {
    "given_name": "John",
    "family_name": "Smith"
  },
  "birth_date": "1975-03-15",
  "gender": "M",
  "address": {
    "street_address": "123 Main Street",
    "city": "Springfield",
    "state": "IL",
    "postal_code": "62701"
  }
}

Example 2: Clinical Scenario

Request: "Generate a 58-year-old female with poorly controlled Type 2 diabetes"

Output:

{
  "patient": {
    "mrn": "MRN00000002",
    "name": { "given_name": "Maria", "family_name": "Garcia" },
    "birth_date": "1967-08-22",
    "gender": "F"
  },
  "diagnoses": [
    { "code": "E11.65", "description": "Type 2 diabetes with hyperglycemia" },
    { "code": "I10", "description": "Essential hypertension" },
    { "code": "E78.5", "description": "Hyperlipidemia" }
  ],
  "medications": [
    { "name": "Metformin", "dose": "1000 mg", "frequency": "BID" },
    { "name": "Glipizide", "dose": "10 mg", "frequency": "BID" },
    { "name": "Lisinopril", "dose": "20 mg", "frequency": "QD" }
  ],
  "labs": [
    { "test": "HbA1c", "value": "9.8", "unit": "%", "flag": "H" },
    { "test": "Glucose", "value": "245", "unit": "mg/dL", "flag": "H" },
    { "test": "Creatinine", "value": "1.2", "unit": "mg/dL" }
  ]
}

Example 3: Professional Claim

Request: "Generate a paid professional claim for an office visit"

Output:

{
  "claim": {
    "claim_id": "CLM20250115000001",
    "claim_type": "PROFESSIONAL",
    "member_id": "MEM001234",
    "provider_npi": "1234567890",
    "service_date": "2025-01-15",
    "place_of_service": "11",
    "principal_diagnosis": "E11.9",
    "claim_lines": [
      {
        "line_number": 1,
        "procedure_code": "99214",
        "charge_amount": 175.00,
        "units": 1
      }
    ]
  },
  "adjudication": {
    "status": "paid",
    "allowed_amount": 125.00,
    "paid_amount": 100.00,
    "deductible": 0.00,
    "copay": 25.00,
    "coinsurance": 0.00
  }
}

Example 4: Pharmacy Claim with DUR

Request: "Generate a pharmacy claim that gets rejected for early refill"

Output:

{
  "claim": {
    "claim_id": "RX20250115000001",
    "transaction_code": "B1",
    "ndc": "00071015523",
    "drug_name": "Atorvastatin 20mg",
    "quantity": 30,
    "days_supply": 30,
    "service_date": "2025-01-15"
  },
  "response": {
    "status": "rejected",
    "reject_code": "79",
    "reject_message": "Refill Too Soon",
    "dur_alert": {
      "type": "ER",
      "message": "Refill 12 days early (before 80% used)",
      "previous_fill_date": "2024-12-27",
      "days_early": 12
    }
  }
}

Tips

1. Be specific: "diabetic patient with A1C of 9.5" beats "sick patient"
2. Request format early: "Generate as FHIR..." rather than converting after
3. Use seeds: For reproducible test data across sessions
4. Batch large requests: "Generate 100 in batches of 20"
5. Validate sensitive data: Request validation for production-like scenarios

Disclaimer

HealthSim generates synthetic test data only. It is not a clinical decision support system and does not provide medical advice, diagnosis recommendations, or treatment guidance.

Intended uses:

• Software development and testing
• System integration validation
• Training and educational demonstrations
• Performance and load testing

Not intended for:

• Clinical decision support
• Medical advice or treatment recommendations
• Actual patient care
• Processing real PHI

The clinical patterns, medication regimens, and lab values reflect general healthcare conventions suitable for test data. They do not account for individual patient circumstances or the full complexity of clinical practice.