To safeguard patient confidentiality, researchers and developers typically rely on anonymised datasets to explore theories, train data models, test algorithms, or build prototypes. Interoperability challenges often hinder the integration of data from multiple sources, limiting the ability to thoroughly test analytical models or support the development of software applications.
The synthetic data, which is novel data created to reflect real data without containing identifiable patient information. The statistical properties and intervariable relationships in a synthetic dataset directly reflect the properties of the source data. Synthetic data has the same utility and can be analysed in the same way as the original, real-world dataset — all the while preserving patient privacy.
The value of synthetic data
When healthcare organisations can access, explore, and analyse healthcare data without obstacles, delays, or worry, the potential benefits are immense. Following are four key benefits of synthetic data in healthcare:
1.More efficient processes and resource utilisation.
2.Increased collaboration between stakeholders.
3.Faster research.
4.Enabling AI development and LLM validation.