Breaking Down Barriers in Healthcare Data Analysis and Sharing: The Answer is Synthetic Data (11/16)

Breaking Down Barriers in Healthcare Data Analysis and Sharing: The Answer is Synthetic Data (11/16)

Healthcare Data

1. Introduction

The development of artificial intelligence (AI) has revolutionized the healthcare industry, highlighting the critical need for extensive healthcare data. However, collecting, utilizing, and sharing healthcare data poses significant challenges. This article explores how synthetic data can effectively address these issues.

2. General Knowledge

he advent of AI in healthcare has brought about a growing emphasis on the necessity of diverse medical data. Despite its potential, several issues hinder the effective collection and sharing of such data:

• Compliance with Data Privacy Laws: Medical data contains highly sensitive information about individuals’ health. Therefore, strict legal regulations are in place to protect it. For instance, the Health Insurance Portability and Accountability Act (HIPAA) plays a crucial role in the United States in safeguarding healthcare data.
• Risk of Data Breach: Medical data is particularly vulnerable to breaches related to personal information protection. A 2023 report revealed that over 13.3 billion healthcare records and data were stolen, setting a new record for data breaches in the healthcare sector.
• Accessibility Issues: Obtaining medical data for commercial or academic purposes can be incredibly time-consuming and costly. For example, it can take up to two years to gain approval for using healthcare system data, and accessing patient-level data may incur costs exceeding hundreds of thousands of dollars.

Sources:

• https://www.shaip.com/blog/what-synthetic-data-means-in-the-age-of-data-privacy-concerns/
• https://www.shaip.com/blog/synthetic-data-in-healthcare/

3. Specific Example

Healthcare Data – Medical Synthetic Data

Medical synthetic data imitates the statistical properties of real patient data while effectively preserving individual privacy. This data is generated artificially through computer simulations or algorithms and can replace real data for various applications.

Benefits of Medical Synthetic Data:

• Privacy Preservation: Synthetic data retains the statistical features of real data while protecting sensitive information. This ensures that individuals’ privacy is maintained.
• Enhanced Data Accessibility: Obtaining real patient data can be costly and time-consuming. Synthetic data overcomes these constraints by allowing researchers to quickly secure and analyze data.
• Balanced Data Utilization: Synthetic data enables researchers to share and analyze data across different domains, accelerating data-driven research and supporting the creation and sharing of solutions for precision medicine.
• Provision of Edge Case Learning Data: Synthetic data supplies learning data for edge cases, enabling AI models to rapidly learn from new scenarios.
• Overcoming Data Silos: Healthcare institutions often expend significant effort to share and collaborate on data. Synthetic data helps overcome these data silos, facilitating collaboration among healthcare institutions, medical systems, pharmaceutical developers, and researchers.

4. Transition or Conclusion

While traditional synthetic data generation methods are susceptible to external attacks and have a high risk of being reconstructed back to the original data, there are advancements addressing these security concerns.
For example, CUBIG’s Data Safe Technologies (DTS) applies data non-access techniques and differential privacy protection technologies to generate secure synthetic data. This process does not require direct access to the original data, yet achieves a performance similarity of up to 99% with the original data, ensuring both security and efficacy.

With DTS, healthcare data can, of course, be generated as synthetic data. As an example, I will show you a synthesized lung X-ray image. Below is a comparison between an original X-ray lung image and the secure synthetic data generated by CUBIG. The X-ray images are classified into two categories: normal and pneumonia. CUBIG’s DTS has successfully created secure synthetic data that preserves the characteristics of each class.

If you’re interested in learning more about DTS and the company that developed it, CUBIG, please visit the link below.

CUBIG Link: CUBIG

Blog Link: Blog