Healthcare technology is advancing at a dizzying pace. A new generation of systems for electronic health records management, insurance claims processing, and medical billing are progressing down a path toward full automation and integration. IoMT is enabling new levels of patient monitoring while telemedicine is improving the delivery of patient care. And AI is positioned to impact every aspect of the next generation of healthcare systems.
“In 2023, more than 540 organizations and 112 million individuals were implicated in healthcare data breaches reported to the HHS Office for Civil Rights (OCR)” according to HealthITSecurity.
Achieving Healthcare Data Security
That’s where synthetic data comes in. Synthetic data is 100% secure because its not real data, eliminating the possibility of putting sensitive data at risk. But there is a perception that synthetic data is not as good as production data for testing advanced healthcare systems. With so much complexity in the many interconnected systems, how can synthetic data accurately simulate the complex data flows and business rules that must be tested?
Additionally, many of the data structures used in healthcare systems are governed by data interchange standards to ensure interoperability. For example, FHIR, X12 EDI and HL7 are all complex nested data structures with precise specifications for the diverse data elements exchanged between trading partners in the healthcare ecosystem. The challenge of generating controlled, conditioned, and accurate synthetic data for these healthcare data structures has only been accomplished by one company, and that company is GenRocket.
As you learn about GenRocket, you will see we have taken an approach to synthetic data generation that is different than any other platform. We have proven that synthetic data, properly modeled, designed, and deployed by an intelligent and adaptable data generation platform, is far superior to production data.
Industry-Leading Synthetic Data Generation
GenRocket has developed the industry’s most advanced synthetic Test Data Automation platform for healthcare. We’ve established a reputation as the leading solution for testing enterprise-class insurance claims processing systems with comprehensive synthetic test data at scale. Collectively, our health insurance customers manage 21% of policy holders in the US. GenRocket has been deployed by the largest healthcare organizations in the world operating the most complex insurance claims processing systems.
GenRocket’s synthetic data solution is enabling leading healthcare organizations to increase test coverage and accelerate test cycles without the use of sensitive production data. And with Test Data Automation, the need to manually produce test data to simulate complex insurance claims transactions is eliminated. Beyond the healthcare data interchange standards, GenRocket can simulate all data formats used in healthcare for testing the next generation of applications.
GenRocket’s Synthetic Test Data Automation platform brings a unique set of capabilities. Our platform was designed from the ground up to solve any test data challenge with synthetic data. The platform generates synthetic data that is realistic and maintains referential integrity just like production data, but with the ability to control the volume, variety, and output formatting of the data. Only GenRocket allows you to design the exact synthetic data needed for each individual test case scenario.
Establish Your Synthetic Data Requirements
Following is a summary of capabilities that are critical for comprehensive testing, all of which are delivered by GenRocket’s Synthetic Test Data Automation Platform:
Data Privacy – Because synthetic data is not real production data, it is 100% compliant with HIPAA and all other data privacy laws. Additionally, GenRocket can replace sensitive PHI and PII data with synthetic data by ingesting only metadata (i.e., database schema, JSON, DDL, etc.) and without access to any production data.
Data Conditioning – To achieve full test coverage, GenRocket has intelligent data generators that can be configured to produce positive data, negative data, edge case values, patterns, and permutations. A small number of manually constructed transactions sets can be replaced with thousands of controlled variations. Additionally, rules can be defined to control the nature of the data generated when testing business rules and program logic.
Data Validity – GenRocket can blend queried production data with generated synthetic data. This is important for synchronizing reference data fields like member ID’s and ICD diagnostic codes with associated procedures and payments to ensure test data is aligned with test objectives.
Standards Compliance – GenRocket is the only synthetic data vendor to partner with the ANSI X12 EDI standards body to simplify the process of leveraging XML Schema Definition (XSD) files from X12 to quickly model and generate EDI test data that is fully X12 compatible.
Complex Workflows – Synthetic test data can be controlled and conditioned to maintain proper state throughout complex transaction workflows for complete end-to-end testing of all X12 EDI transactions and acknowledgments. This allows testers to map data values, such as a member ID, for a complete “round trip” transaction. For example, a given member ID can be associated with a submitted 837 claim, generate the proper acknowledgements, and generate the 835 payment for the same claim associated with that member ID.
Application Compatibility – GenRocket’s Test Data Automation platform is fully compatible and able to generate synthetic data for testing X12 EDI implementations of core administrative processing solutions such as FACETS, Edifecs and NASCO.
Data Accuracy – When using a shared pool of production test data, it must be reserved and refreshed to maintain accuracy. Because GenRocket generates a fresh copy of synthetic data for each test run, data accuracy is preserved and the need for data reservation and refresh is eliminated.
Data Orchestration – Enterprise-class claims processing systems manage data from multiple sources and in multiple formats. With GenRocket, synthetic data can be generated in any volume, variety, or format to simulate multiple data feeds and transaction flows to enable systems integration and performance testing. And GenRocket is easily integrated with test tools like Tosca or Selenium and orchestrated by CI/CD pipeline tools like Jenkins or Azure DevOps.
Scalable Self-Service Platform – The GenRocket platform enables self-service through a Center of Excellence (CoE) or Community of Practice (CoP) model for supporting global and distributed teams of developers and testers. Additionally, a feature called G-Questionnaire allows dev and test teams to quickly modify and repurpose preconfigured Test Data Cases to quickly design their own permutations and combinations of test data, and without the need to be a GenRocket expert.
One Platform for Any Test Data Challenge
GenRocket is the only synthetic data platform that delivers all of these advanced capabilities. Collectively, they provide synthetic data generation solutions for virtually any healthcare application environment not only for claims processing, but also for Electronic Health Records (EHR) systems, patient management and scheduling, telemedicine and virtual health, pharmacy management, laboratory Information Systems (LIS), and more.
And GenRocket is uniquely positioned to enable healthcare advancements in AI/ML and IoMT with its ability to generate highly accurate training datasets and large-scale data volumes in the billions of records.
Learn more about the innovative ways synthetic data is being used in healthcare and the power of GenRocket’s industry leading Synthetic Test Data Automation platform by reading the articles and case studies featured below.
Healthcare Case Studies
