The healthcare industry faces unique challenges when it comes to testing claims processing systems, benefits configuration, or provider contracts. Real patient data is highly sensitive and protected by strict privacy regulations, making it difficult to use for thorough testing and development. Enter GenAI-powered synthetic data generation.
Benefits of Synthetic Claims Data
Synthetic data is artificially generated information that mimics real-world data without containing actual real-world observations. It is created using computer algorithms and simulations, typically based on AI technologies, rather than being produced by real events or collected from real sources. Synthetic data maintains the same statistical properties and relationships as the original data it’s modeled on, so it can be used for research, testing, and training machine learning models, while protecting the privacy and confidentiality of authentic data.
Synthetic healthcare claims data generated by advanced AI models offers several key advantages:
- Privacy Protection: Synthetic data eliminates the risk of exposing real patient information. Synthetic data generated by AI does not contain any real personal information, significantly reducing privacy risks and compliance concerns. This allows organizations to work with data that closely resembles real-world information without compromising individual privacy or violating data protection regulations like GDPR or HIPAA.
- Unlimited Scale: Generative AI can produce large volumes of diverse synthetic data on demand, overcoming limitations in real-world data availability. This is particularly valuable for scenarios where real data is scarce, such as rare diseases or underrepresented populations.
- Edge Case Simulation: Developers can create synthetic data representing rare or complex claims situations that may be underrepresented in real datasets. Synthetic data can be used to augment real datasets, improving the performance and robustness of machine learning models. It helps address issues like class imbalance, overfitting, and limited data diversity, leading to more accurate and generalizable AI systems.
- Faster Development Cycles: Generating synthetic data using AI is often faster and more cost-effective than collecting and processing real-world data. This accelerates research and development cycles, particularly in time-sensitive areas like drug discovery or pandemic response. With on-demand synthetic data generation, teams can accelerate testing and iteration without waiting for data access approvals.
- Cost-Efficiency: Synthetic data reduces the need for expensive data masking and governance processes associated with real patient data.
Ensuring Quality and Realism
Generative AI allows for greater control over the quality, format, and characteristics of the generated data. Organizations can tailor synthetic datasets to meet specific requirements, ensuring suitability for particular use cases or scenarios.
Modern GenAI tools employ advanced techniques to maintain the statistical properties, relationships and complexities found in real healthcare claims data. This includes accurately representing:
- Diagnosis and procedure codes
- Provider and patient demographics
- Claim amounts and adjudication logic
- Temporal patterns and trends
By leveraging these advanced AI-generated synthetic datasets, healthcare organizations can confidently test and improve their new AI models, model contracts and benefits configurations, process claims without compromising patient privacy or data security.
For more information please contact Anup Panthaloor, EVP – Health Plans and Health Services or Deepan Vashi, EVP – Healthcare Payer Practice and Solutions Lead, Solutions Architecture, Firstsource. Visit Firstsource relAI to read about our suite of AI led platforms, solutions and offerings. Learn how we’re empowering businesses of all sizes to seamlessly integrate digital technology and AI into their operations to enable new levels of efficiency, innovation, and competitive advantage.
