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name: healthsim-patientsim description: "Generate realistic clinical patient data including demographics, encounters, diagnoses, medications, labs, and vitals. Use when user requests: (1) patient records or clinical data, (2) EMR test data, (3) specific clinical cohorts like diabetes or heart failure, (4) HL7v2 or FHIR patient resources."

PatientSim - Clinical Patient Data Generation

For Claude

Use this skill when the user requests clinical patient data, EMR/EHR test data, or medical records. This is the primary skill for generating realistic synthetic patients with complete clinical histories.

When to apply this skill:

• User mentions patients, clinical data, or medical records
• User requests EMR or EHR test data
• User specifies clinical cohorts (diabetes, heart failure, oncology, etc.)
• User asks for HL7v2 messages, FHIR resources, or C-CDA documents
• User needs encounters, diagnoses, medications, labs, or vitals

Key capabilities:

• Generate patients with realistic demographics and identifiers
• Create encounters across care settings (inpatient, outpatient, ED, observation)
• Apply clinical cohorts from specialized skills (diabetes, oncology, etc.)
• Produce appropriately coded data (ICD-10, CPT, HCPCS, LOINC, RxNorm, SNOMED)
• Transform output to FHIR R4 (Bundle, Patient, Condition, Encounter), HL7v2, C-CDA

For specific clinical cohorts, load the appropriate cohort skill from the table below.

Overview

PatientSim generates realistic synthetic clinical data for EMR/EHR testing, including:

• Patient demographics
• Encounters (inpatient, outpatient, emergency, observation)
• Diagnoses (ICD-10-CM)
• Procedures (CPT, ICD-10-PCS)
• Medications (with RxNorm codes)
• Lab results (with LOINC codes)
• Vital signs

Quick Start

Simple Patient

Request: "Generate a patient"

{
  "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"
  }
}

Clinical Cohort

Request: "Generate a diabetic patient with complications"

Claude loads diabetes-management.md and produces a complete clinical picture.

Cohort Skills

Load the appropriate cohort based on user request:

Cohort	Trigger Phrases	File
ADT Workflow	admission, discharge, transfer, ADT, patient movement	adt-workflow.md
Behavioral Health	depression, anxiety, bipolar, PTSD, mental health, psychiatric, substance use, PHQ-9, GAD-7	behavioral-health.md
Diabetes Management	diabetes, A1C, glucose, metformin, insulin	diabetes-management.md
Heart Failure	CHF, HFrEF, HFpEF, BNP, ejection fraction, I50	heart-failure.md
Chronic Kidney Disease	CKD, eGFR, dialysis, nephropathy	chronic-kidney-disease.md
Sepsis/Acute Care	sepsis, infection, ICU, critical care	sepsis-acute-care.md
Orders & Results	lab order, radiology, ORM, ORU, results	orders-results.md
Maternal Health	pregnancy, prenatal, obstetric, labor, delivery, postpartum, GDM, preeclampsia	maternal-health.md
Pediatrics
↳ Childhood Asthma	asthma, pediatric, inhaler, albuterol, nebulizer, wheeze	pediatrics/childhood-asthma.md
↳ Acute Otitis Media	ear infection, otitis media, AOM, ear pain, amoxicillin pediatric	pediatrics/acute-otitis-media.md
Oncology
↳ Breast Cancer	breast cancer, mastectomy, ER positive, HER2, tamoxifen	oncology/breast-cancer.md
↳ Lung Cancer	lung cancer, NSCLC, EGFR, ALK, immunotherapy	oncology/lung-cancer.md
↳ Colorectal Cancer	colon cancer, rectal cancer, FOLFOX, colonoscopy	oncology/colorectal-cancer.md

Generation Parameters

Parameter	Type	Default	Description
age	int or range	18-90	Patient age or range
gender	M/F/O/U	weighted	M=49%, F=51%
conditions	list	none	Specific diagnoses to include
severity	string	moderate	mild, moderate, severe
encounters	int	1	Number of encounters to generate
timeline	string	1 year	How far back to generate history

Output Entities

Patient

Demographics extending the Person model with MRN.

Encounter

Clinical visit with class (I/O/E/U/OBS), timing, location, providers.

Diagnosis

ICD-10-CM code with type (admitting, working, final), dates.

Medication

Drug with RxNorm code, dose, route, frequency, status.

LabResult

Test with LOINC code, value, units, reference range, abnormal flag.

VitalSign

Observation with temperature, HR, RR, BP, SpO2, height, weight.

See data-models.md for complete schemas.

Clinical Coherence Rules

PatientSim ensures generated data is clinically realistic:

1. Age-appropriate conditions: No pediatric conditions in adults, geriatric conditions require appropriate age
2. Gender-appropriate conditions: Prostate conditions for males only, pregnancy for females only
3. Medication indications: Drugs match diagnoses (metformin requires diabetes)
4. Lab coherence: Values align with conditions (elevated A1C with diabetes, BMP reflects renal status)
5. Temporal consistency: Diagnoses before treatments, labs after orders
6. Comorbidity patterns: Realistic comorbid conditions cluster together (e.g., diabetes + hypertension + CKD)

See validation-rules.md for complete rules.

Safety Guardrails

All PatientSim data is 100% synthetic (fictional and simulated). Enforce these rules at all times:

1. No real patient data. Never copy real medical records, real MRNs, or real SSNs into output. All identifiers must be generated.
2. No clinical advice. Output is test data, not medical guidance. Never phrase output as a recommendation for actual patient care.
3. No real provider NPIs in patient context. Use synthetic NPIs (prefix with 9999) unless explicitly pulling from NetworkSim reference data.
4. Validate code systems. Only emit ICD-10-CM codes from the current valid set (e.g., E11.65 not E11.999). Same for CPT, LOINC, and RxNorm -- use real codes, not invented ones.
5. PHI boundary. If a user supplies real patient details, refuse and explain that PatientSim generates synthetic data only.

Negative Examples (What NOT to Generate)

Mistake	Why It Fails	Correct Approach
Assigning pregnancy to a male patient	Gender-inappropriate	Check gender before obstetric conditions
Metformin without a diabetes diagnosis	Medication without indication	Always pair drugs with supporting Dx
A1C of 14.2% on a healthy patient	Lab contradicts condition list	Abnormal values require matching diagnosis
ICD-10 code E11.999	Invalid code -- does not exist	Use valid codes like E11.65 (with complications)
Discharge date before admission date	Temporal inversion	Ensure chronological ordering of all events
Using a real SSN (e.g., 078-05-1120)	PHI leak risk	Generate synthetic SSNs in 900-xx-xxxx range

Edge Case Handling

Scenario	Behavior
Partial data request ("just demographics")	Omit clinical entities (encounters, labs, meds); return only requested subset
Age-cohort conflict ("5-year-old with COPD")	Flag the clinical implausibility, suggest an age-appropriate alternative, and ask before proceeding
Invalid ICD-10 code from user (e.g., E11.999)	Reject the code, suggest the nearest valid code, explain why
Missing required fields (no age or gender given)	Apply defaults from Generation Parameters table; note assumptions in output
Contradictory instructions ("healthy patient with A1C of 12%")	Prioritize clinical coherence; ask user to clarify intent
Unsupported output format ("as X12 837")	Redirect to MemberSim which owns claims formats; explain the boundary

Output Formats

Format	Request	Use Case
JSON	default	API testing
FHIR R4	"as FHIR", "FHIR bundle"	Interoperability
HL7v2 ADT	"as HL7", "ADT message"	Legacy EMR
CSV	"as CSV"	Analytics

Data Integration (PopulationSim)

Add geography (5-digit county FIPS or 11-digit tract FIPS) to ground generation in real CDC PLACES, SVI, and ADI data. See data-integration.md for full patterns, data sources, and provenance tracking.

Examples

Example 1: Basic Patient with Encounter

Request: "Generate a 45-year-old male with an office visit for hypertension"

Output:

{
  "patient": {
    "mrn": "MRN00000001",
    "name": { "given_name": "Michael", "family_name": "Johnson" },
    "birth_date": "1980-06-22",
    "gender": "M"
  },
  "encounter": {
    "encounter_id": "ENC0000000001",
    "patient_mrn": "MRN00000001",
    "class_code": "O",
    "status": "finished",
    "admission_time": "2025-01-15T09:30:00",
    "discharge_time": "2025-01-15T10:00:00",
    "chief_complaint": "Blood pressure follow-up"
  },
  "diagnoses": [
    {
      "code": "I10",
      "description": "Essential hypertension",
      "type": "final",
      "diagnosed_date": "2024-06-15"
    }
  ],
  "medications": [
    {
      "name": "Lisinopril",
      "code": "104376",
      "dose": "10 mg",
      "route": "PO",
      "frequency": "QD",
      "status": "active"
    }
  ],
  "vitals": {
    "observation_time": "2025-01-15T09:35:00",
    "systolic_bp": 138,
    "diastolic_bp": 88,
    "heart_rate": 72,
    "temperature": 98.4,
    "spo2": 98
  }
}

Example 2: Acute Inpatient Encounter

Request: "Create an inpatient admission for pneumonia"

Generates a hospital encounter with:

• Encounter class I (inpatient), admission and discharge dates
• Diagnosis: J18.9 (Pneumonia, unspecified organism) plus respiratory symptoms
• Procedures: chest X-ray (CPT 71046), blood cultures, CBC
• Labs: WBC, procalcitonin, BMP, blood gas
• Medications: antibiotics (ceftriaxone + azithromycin)
• Imaging results documenting chest infiltrates

Example 3: Complex Multi-Condition Patient

Request: "Generate a 68-year-old female with diabetes, hypertension, and CKD stage 3"

Claude combines patterns from multiple cohort skills to generate a coherent patient with:

• Multiple chronic diagnoses with appropriate onset dates
• Medications for each condition (metformin, lisinopril, etc.)
• Quarterly encounters over 2 years
• Labs showing disease progression (A1C, eGFR trends)
• Comorbidity interactions (CKD affecting medication choices)

Related Skills

All cohort sub-skills are listed in the Cohort Skills table above. Additional references:

• oncology-domain.md - Foundational oncology knowledge

Cross-Product: MemberSim (Claims)

PatientSim clinical encounters generate corresponding claims in MemberSim:

PatientSim Cohort	MemberSim Skill	Typical Timing
Office visits	professional-claims.md	Same day
Inpatient stays	facility-claims.md	+2-14 days
Surgeries	prior-authorization.md, facility-claims.md	PA before, claim after
Behavioral health	behavioral-health.md	Same day

Integration Pattern: Generate clinical encounter in PatientSim first, then use MemberSim to create corresponding claims with matching dates, diagnoses, and procedures.

Cross-Product: RxMemberSim (Pharmacy)

PatientSim medication orders generate prescription fills in RxMemberSim:

PatientSim Cohort	RxMemberSim Skill	Typical Timing
Chronic disease meds	retail-pharmacy.md	Same day or +1-3 days
Discharge meds	retail-pharmacy.md	+0-3 days post-discharge
Specialty drugs	specialty-pharmacy.md	+1-7 days
High-cost drugs	rx-prior-auth.md	PA required first

Integration Pattern: Generate medication orders in PatientSim, then use RxMemberSim to model pharmacy fills with matching NDCs and appropriate fill timing.

Cross-Product: PopulationSim (Demographics & SDOH)

When geography is specified, PatientSim grounds generation in real CDC PLACES, SVI, and ADI data via PopulationSim. See data-integration.md for the full data-driven generation pattern, data files, and provenance tracking.

Cross-Product: NetworkSim (Provider Networks)

NetworkSim provides realistic provider and facility entities for clinical encounters:

PatientSim Need	NetworkSim Skill	Generated Entity
Attending physician	provider-for-encounter.md	Provider with NPI, credentials
Hospital/facility	synthetic-facility.md	Facility with CCN
Specialty referral	synthetic-provider.md	Specialist with taxonomy

Integration Pattern: Generate encounters in PatientSim first, then use NetworkSim to add realistic provider entities with proper NPIs, credentials, and hospital affiliations.

Cross-Product: TrialSim (Clinical Trials)

For patients enrolled in clinical trials:

• ../trialsim/therapeutic-areas/oncology.md - Oncology trial endpoints
• ../trialsim/therapeutic-areas/cardiovascular.md - CV outcomes trials
• ../trialsim/therapeutic-areas/cns.md - CNS trial assessments

Integration Pattern: Use PatientSim for clinical care journeys. When a patient enrolls in a trial, apply TrialSim skills for trial-specific data (RECIST, SDTM format, randomization).

Output Formats

• ../../formats/fhir-r4.md - FHIR transformation
• ../../formats/hl7v2-adt.md - HL7v2 ADT messages
• ../../formats/hl7v2-orm.md - HL7v2 Order messages
• ../../formats/hl7v2-oru.md - HL7v2 Results messages

Reference Data

• ../../references/oncology/ - Oncology codes, medications, regimens

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Generative Framework Integration

PatientSim integrates with the Generative Framework for specification-driven generation at scale.

• Profile-Driven: "Use the Medicare diabetic profile to generate 100 patients" — samples demographics, generates clinical attributes, links to NetworkSim providers.
• Journey-Driven: "Add the diabetic first-year journey to each patient" — generates encounters over time, labs, medication changes, and complication branching.
• Cross-Domain Sync: Patient → MemberSim Member (via SSN), Encounter → Claim, Prescription → RxMemberSim Fill, Trial Subject → TrialSim Subject. See cross-domain-sync.md.