Synthetic Health Data Generation System for Medicare Analytics

A healthcare analytics company, required a sophisticated synthetic data generation system to support code development and testing within their Medicare claims analytics platform. The technical challenge involved generating approximately 6 million synthetic patients whilst maintaining statistical alignment with real-world distributions, implementing appropriate billing patterns across diverse encounter settings, and creating clinically plausible patient journeys with realistic progression patterns.

• Implement patient identity consistency maintaining demographic and clinical characteristics across monthly data generations

• Create epidemiologically reasonable population distributions 

• Develop realistic patient journeys for multiple disease areas with appropriate clinical progression patterns

• Implement setting-specific encounters reflecting outpatient, inpatient, clinic, and other care environments

• Develop comprehensive validation framework measuring alignment between synthetic and real-world data

1. Data Architecture Analysis

• Evaluated existing Databricks environment including current synthetic data generation scripts and integration points

• Reviewed data structures, statistical summaries, and reference pricing tables for procedures and diagnoses

• Identified requirements for SNOMED to ICD-10 and CPT code mapping system ensuring Medicare claims compatibility

• Designed patient consistency logic maintaining persistent identities with stable demographic and clinical characteristics across time periods

2. Technical Implementation

• Configured Databricks environment optimising for existing infrastructure compatibility

• Implemented deterministic algorithms for persistent patient ID generation maintaining consistency across monthly datasets

• Created comprehensive code mapping system translating SNOMED clinical terminologies to ICD-10 diagnostic codes and CPT procedure codes

• Configured setting-specific encounter generation differentiating outpatient, inpatient, clinic, and other care environments

• Developed statistical validation framework incorporating automated testing against population norms with quantitative alignment metrics

3. Solution Delivery

• Built detailed disease models for therapeutic areas incorporating appropriate prevalence rates, comorbidity patterns, and treatment pathways

• Implemented realistic treatment sequences reflecting temporal care patterns and clinical decision-making

• Developed comprehensive technical documentation including architecture diagrams, module specifications, and integration guides

• Established knowledge transfer programme with hands-on training sessions for CareSet engineering team

Quantitative Outcomes

• Designed system generating 6 million synthetic patients

• Implemented persistent patient identity system maintaining demographic consistency across unlimited monthly generations

• Created validation framework measuring alignment across prevalence rates, comorbidity patterns, treatment sequences, and billing distributions

Qualitative Benefits

• Enabled realistic code testing environment reflecting true population complexity beyond single-disease scenarios

• Provided epidemiologically plausible synthetic data supporting development of sophisticated analytics algorithms

• Created foundation for testing correlation analyses between National Provider Identifiers, hospitals, and patient diagnoses

"The Synthetic data based approach proposed by Umbizo addresses our fundamental challenge of creating realistic test data that actually reflects the complexity of Medicare populations. Moving beyond our current system where every synthetic patient has diabetes to one with epidemiologically reasonable distributions across multiple disease areas will transform our ability to develop and test analytics".

Client EngineeringTeam

• Assessment and Planning: Weeks 1-2

• Core Framework Implementation: Weeks 3-5

• Initial Disease Implementation (2 areas): Weeks 6-9

• Additional Disease Areas (2 areas): Weeks 10-13

• Knowledge Transfer and Deployment: Week 14

• Total Initial Implementation Duration: 14 weeks

Future enhancements will explore advanced comorbidity interaction models, sophisticated temporal progression patterns for chronic disease management, integration of social determinants of health affecting care patterns, and machine learning approaches for generating increasingly realistic clinical decision patterns. The validation framework will continuously evolve, incorporating new metrics as disease coverage expands and analytical requirements become more sophisticated.