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healthsim

Generate synthetic healthcare data across EMR, claims, pharmacy, and trials domains.

9|2|Updated Dec 9, 2025
Search Tags:#testing#data generation#synthetic data#healthcare#claims#FHIR#clinical#X12#HL7#EMR#pharmacy
One-click install
npx skills add https://github.com/mark64oswald/healthsim-skills --skill healthsim
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: healthsim
Source: https://github.com/mark64oswald/healthsim-skills/tree/main/.
Command: npx skills add https://github.com/mark64oswald/healthsim-skills --skill healthsim

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Healthcare data generation is complex, time-consuming, and often requires specialized coding or manual effort. This Skill automates the creation of realistic, clinically coherent synthetic healthcare data, eliminating manual data entry, reducing compliance risks associated with real PHI, and accelerating testing and development cycles for EMR systems, claims processing, and pharmacy benefits.

Core Features & Use Cases

  • PatientSim: Generate detailed clinical/EMR data including patients, encounters, diagnoses, procedures, labs, vitals, and medications.
  • MemberSim: Create realistic claims and payer data such as members, professional/facility claims, payments, and accumulator tracking.
  • RxMemberSim: Produce comprehensive pharmacy benefits data, including prescriptions, formularies, drug utilization review (DUR) alerts, and prior authorizations.
  • Multi-format Output: Export generated data in industry-standard formats like FHIR R4, HL7v2, X12 (837/835), and NCPDP D.0, as well as common formats like CSV and JSON.
  • Use Case: Imagine you need to test a new claims adjudication system. Use HealthSim to generate 50 diverse professional claims, including some with prior authorization requirements and others with specific adjudication outcomes (e.g., denied for medical necessity), and export them as X12 837 transactions. This saves weeks of manual test data creation and ensures comprehensive test coverage.

Quick Start

Generate a 65-year-old diabetic patient with hypertension and recent labs.

Frequently Asked Questions about healthsim

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate synthetic healthcare data for testing claims and EMR systems?▼

Synthetic healthcare data generation creates realistic patient records, claims, and pharmacy data without using real PHI. This Skill automates the process across EMR, claims, and pharmacy domains, generating complete care journeys in industry formats like FHIR R4, HL7v2, and X12, eliminating manual test data creation and compliance risks.

Can I export generated healthcare data in FHIR, HL7v2, X12, and NCPDP formats?▼

Yes. The Skill produces multi-format output including FHIR R4, HL7v2, X12 (837/835), NCPDP D.0, CSV, and JSON, allowing you to match your system's input requirements and test end-to-end workflows across EMR, claims adjudication, and pharmacy benefits without format conversion overhead.

What types of healthcare data can I generate—clinical records, claims, pharmacy, or trial data?▼

The Skill generates five data types: PatientSim (clinical encounters, diagnoses, medications), MemberSim (claims, payments, accumulator tracking), RxMemberSim (prescriptions, formularies, DUR alerts, prior authorizations), TrialSim (trial enrollments and outcomes), and PopulationSim (networked patient populations across care settings).

Do I need coding expertise to generate realistic synthetic healthcare data?▼

No. The Skill is designed for effortless data generation without specialized coding. It automates clinically coherent synthetic data creation, reducing manual effort and allowing non-technical users to accelerate testing cycles for healthcare systems.

Can I generate claims data with specific adjudication outcomes like denials or prior authorization requirements?▼

Yes. You can generate diverse professional and facility claims with configurable outcomes including prior authorization requirements, medical necessity denials, and other adjudication scenarios, enabling comprehensive test coverage for claims processing systems.

What validation and data quality features are included in the generated synthetic data?▼

Generated data includes embedded validation rules, provenance tracking, and canonical JSON data models to ensure clinical coherence and correctness across all domains, supporting end-to-end care journey validation without manual quality assurance.

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