It’s no secret that the key to medical breakthroughs, more precise clinical research, and better patient care lies in the ability to leverage healthcare data quickly and accurately. But for organizations across the healthcare continuum, doing so is easier said than done.
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.
Until now, the quest to unlock the full potential of precision healthcare data while maintaining data privacy and organizational control has been elusive. But in order to solve the linkage problem, we need to explore innovative new solutions that allow us to safeguard PHI while still connecting data at an individual level.
The linkage problem can be defined as:
Two independent entities (public or private) are each managing a dataset about individuals. The understanding of each individual’s identity is achieved using various identifiers such as name, postal address, email, and/or social security number. However, these components of personal data are sensitive and are tied to personal privacy rights, regulatory restrictions, and/or ethical handling concerns.
Some real-world examples of the linkage problem in healthcare include:
The linkage problem is ultimately delaying progress in medical research, patient care, and public health initiatives. Without a solution, healthcare organizations will continue to face many significant challenges in leveraging the full potential of their data. A few of these challenges include:
Addressing the linkage problem is vital for unlocking the transformative power of healthcare data, with the power to advance healthcare research, improve patient care, and develop more effective public health strategies. But how do we do that without sacrificing the accuracy and security of this vital patient information? It’s time to look at a new approach to technology that enables better data exchange throughout healthcare organizations without putting PHI at risk.
That’s where Karlsgate steps in. Karlsgate’s next-gen technology was designed to provide a privacy-enhancing layer that is easily integrated into all data operations – allowing a free flow of insights while maintaining control of sensitive information. You’re able to connect with any partner, anywhere, making the data you really need for precision healthcare insights more accessible than ever before.
Gone are the days of thinking about healthcare data linkage as a problem. Data interoperability should be easier – and it can be. If you’re ready to simplify your healthcare data connectivity, check out our demo video, or contact us to learn more and get started.