An Inside Look
In today's data ecosystem, organizations are struggling to keep up with the complexities of data collaboration at scale while maintaining a level of security required as stewards of highly sensitive information. This "linkage problem" poses a significant challenge in managing and analyzing personal data while complying with privacy rights and regulations.
This becomes particularly critical in the context of healthcare, where patient data is generated and stored across multiple systems and organizations. Healthcare providers and researchers often face difficulties in combining data from various sources - whether it be electronic health records, lab results, and even wearable devices - to create a comprehensive view of an individual's health.
Until recently, the industry has had to rely on the best practices available at the time which focused on simple de-identification and linkage methods for maintaining HIPAA compliance when connecting patient data. These methods have come with limitations in terms of what can be connected and with whom, data integrity challenges, and cost concerns.
In addition, de-identification of data "upstream" makes it difficult for "downstream" players to get accurate data at the patient level.
While these methods have been adequate enough to date, in the world of AI-driven technology, where vast amounts of accurate, individual-level data make the difference between medical breakthroughs and the status quo, these limitations and concerns are no longer acceptable.
