We are too used to — even numb — to read stories about data breaches, abuse, and evocative privacy debates. Malicious cyber attacks aimed at gathering information are now the standard for this course.according to HIPAA Magazine, In April this year alone, there were 62 health care-focused data breaches, exposing the content of 2.5 million medical records—a number that is at the highest level in history, but unfortunately, compared to previous months ratio. Coupled with the significant increase in ransomware attacks against the industry and it is frightening, the risks to patient data are unprecedentedly high, and medical institutions have not been prepared to fight it for a long time. Therefore, it is easy to understand the reasonable paranoia of data custodians on data management, coupled with the restrictions imposed by legislation, self-imposed restrictive governance severely restricts access to production data.
The reality of locking sensitive data so tightly is that the value it may have is also inaccessible. Organizations know that data helps to better understand patients and customers, supports smart business decisions, and most importantly, can support patient care research. In addition, as pharmaceutical and medical device companies strive to make breakthroughs in diagnosis and treatment, the trend of monetization of healthcare data has become increasingly apparent. Therefore, the ultimate problem to be solved is to have a safe and reliable way to freely use data for critical medical research while meeting the high governance expectations of data custodians.
Fortunately, groundbreaking artificial intelligence technology is emerging, which provides a viable solution for organizational leaders and data custodians to share and gain insights from user data while still maintaining strong security and empowering patients Absolute privacy. The use of advanced synthetic data engines enables ethical and risk-free analysis, sharing and even monetization of data. As a result, this enables healthcare organizations to autonomously extract maximum value from the data they have, opening the door to breakthrough research, enhancing all aspects of the patient experience, and supporting more efficient business operations.
What exactly is synthetic data?
Leading synthetic data technology can now generate a “twin” data set, which has a highly verifiable high accuracy and statistical equivalence with production data, but without any private information. The generated synthetic data is significantly more powerful than traditional shielding and anonymization techniques because it creates new data values to maintain the precise relationship and distribution of production data. The insights and analysis provided provide equivalent results observed from the original data. More advanced software will also provide analytical tools to measure the accuracy and privacy of “new” data, and allow forensic analysis of the creation of synthetic data to increase user safety assurance and governance.
This revolutionary feature allows companies to share data between internal teams and third parties (often located in multiple jurisdictions) in ways that exceed the requirements of data privacy legislation. Since it never reveals any personally identifiable information, synthetic data allows highly sensitive and privileged medical information to be transformed into unprecedented analysis and processing resources.
Break down barriers to using synthetic data
As with any new technology, educating potential users about the benefits of synthetic data can be a time-consuming task, especially when healthcare data becomes the focus. However, once the functionality and potential are realized, and backed by measurable privacy and accuracy, it is clear that the use of synthetic data should soon become mainstream.
Clear use cases to fully understand how to deploy synthetic data is an important part of the decision-making process. Although synthetic data is not “real” in nature, its existence in the data supply chain should still be included in data governance and security policies. Early adopters mainly use synthetic data for internal purposes to support broader analytical capabilities or to support artificial intelligence and machine learning. In other words, by distributing data to external technology partners to support onboarding and testing, there is a growing trend to utilize the degrees of freedom provided by synthetic data. Where small and medium-sized companies are encouraged to introduce innovation into organizations, synthetic data is now being used to incorporate more authenticity and accuracy into the process. It is obvious that the use and application of synthetic data will only increase as confidence in its robustness and capabilities increases and use cases proliferate.
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