How Synthetic Data Can Support Real-World Evidence Generation

Privacy concerns, #fragmenteddatasets, and underrepresentation have long challenged evidence generation in healthcare. Synthetic data is changing that - offering secure, scalable, and representative datasets that accelerate #AI models, enable faster drug development, support personalised medicine, and drive regulatory-ready research.

By bridging data gaps while protecting patient privacy, #syntheticdata is paving the way for more inclusive, impactful, and innovative healthcare solutions.

1. In today’s rapidly evolving healthcare landscape, the ability to
generate and analyze real-world data is critical for advancing
clinical research, optimizing treatment strategies, and ensuring
better patient outcomes. However, the challenges of data
privacy, access, and scalability often hinder the efficient
use of real-world data.
How Synthetic Data Can Support
Real-World Evidence
Generation
Enter Synthetic Data: A Game
Changer for RWE Generation

2. Synthetic Data?
Synthetic data is artificially generated information that mirrors the
statistical properties of real-world data without containing any
actual personal details. It is produced using advanced algorithms
like Generative Adversarial Networks (GANs) or Variational
Autoencoders (VAEs). This approach ensures data privacy while
retaining the analytical value of real datasets.
Privacy-Preserving: Eliminates risks associated with sharing
real patient data.
Key Attributes
Statistically Representative: Captures the underlying
patterns and correlations of real data.
What is

3. in Healthcare
Relevance of Synthetic Data
Enhancing Data Accessibility - Synthetic data facilitates the
sharing of datasets without compromising patient confidentiality,
thereby accelerating research and innovation
Addressing Data Scarcity - In scenarios where real-world data
is limited or hard to obtain, synthetic data can simulate diverse
patient populations, enriching research studies
Supporting Regulatory Compliance - By generating data that
mimics real-world scenarios, synthetic datasets assist in meeting
regulatory requirements without exposing sensitive information

4. Synthetic Data
Use Cases of
Algorithm Training & Validation
Synthetic datasets provide a diverse, bias-free
foundation for training machine learning models.
By simulating varied patient cohorts, they ensure
algorithms are robust, generalizable, & inclusive
of underrepresented populations.
Drug Development & Safety Profiling
By modeling diverse patient responses, synthetic
data helps predict adverse drug reactions and
optimize treatment protocols, accelerating safe
drug development.
Digital Twin Simulations
Virtual patient replicas powered by synthetic
data enable personalized treatment simulations,
enhancing precision medicine and tailoring
therapies to individual needs.