On a recent tour of healthcare organizations across the nation, Riddhiman started closely evaluating how different organizations are securing their data and even more important, securely accessing/sharing data.
From developing new drugs and medical devices to allocating scarce resources amidst supply chain issues, most advancement in healthcare hinges on having access to the right data. Moreover, some of the most sensitive and highly regulated data requires technology solutions that take all of that into account to solve this complex challenge.
Riddhiman recognizes how the traditional solutions used to tackle data problems but has solutions on how the next wave of innovation can allow the healthcare industry to gain insights from health data while maintaining privacy.
M.R. Rangaswami: Data is arguably the most critical driver of innovation in healthcare today. What trends is this driving and what are some key “amount of data” stats in healthcare?
Riddhiman Das: I believe that data is the most critical driver of innovation in healthcare but there are limitations because the data is sensitive and as a result, regulated. Everything in healthcare hinges on having access to the right data: From developing new drugs and medical devices to allocating scarce resources amidst supply chain issues.
It’s no secret that having continuous access to raw health data is invaluable— this fact is well established. However, recent advances in analytics, machine learning, and artificial intelligence have brought us to a tipping point where healthcare can no longer ignore the value of having access to data.
And get this, privacy and compliance concerns have trapped two Zettabytes of data in silos and removed $500B in value creation for healthcare organizations.
M.R.: If we know healthcare has a data problem, how have we traditionally been trying to tackle it?
Riddhiman: Historically, organizations have tried to get around limited access to data by using synthetic, abstracted, or pre-anonymized datasets, but that strategy just doesn’t cut it. The method tends to be expensive and can result in flawed insights if the data contains errors or is missing a key element – that doesn’t really benefit anyone.
We need access to data to drive the next wave of innovation—people’s health and well-being depend on it. We can only achieve this if the data is kept private to maintain patient privacy and the intellectual property rights of healthcare companies and their industry partners.
Over the years, initiatives have emerged to address this. Everyone has heard of HIPAA, which was enacted to protect patients’ health information from disclosure without their consent or knowledge. It also features standards designed to improve efficiency in the healthcare industry. The less-talked-about Sentinel Initiative was created to monitor the safety of medical products via direct access to patients’ electronic health records. Despite legislation and initiatives to help with this problem, the challenge remains and will only become more amplified as health data grows in volume and complexity.
Organizations have been shooting themselves in the foot by relying on manually de-identifying, abstracting, or normalizing data to get the insights they need. It’s nearly impossible to obtain meaningful, accurate, real-time insights from health data in this manner. This outdated method is hardware dependent, poses potential risks for re-identification, offers only partial security, and generally only works on structured or specific types of data.
M.R.: What are some fresh solutions to data and data privacy in healthcare you have seen?
Riddhiman: We’ve seen quite a few technology solutions developed in recent years that tackle this issue in a way that allows healthcare organizations the ability to gain insights from data and maintain privacy beyond what regulations require.
Privacy-enhancing technologies (PETs) were specifically designed to make gleaning insights from health data scalable, accurate, and secure: a true win-win. One PET we’re truly excited about? Federated analytics.
Federated analytics improves upon prior PETs and keeps health data safe in three ways. First, the data is secured at its point of residence so that external parties cannot access it in any meaningful way. Second, the data is kept secure as parties collaborate to decrease the risk of interception. Finally, the data is secured during computation, reducing the risk of sensitive information extraction. Organizations can also track how the data is used to ensure it is only leveraged for its intended purpose.
Federated analytics software lowers the risks associated with sharing health data by eliminating decryption and movement of raw data, while allowing privacy-intact computations to occur. Additionally, technology improvements driven by federated analytics minimize the computational load necessary to analyze data, which reduces hardware dependency and increases scalability.
Other benefits include access to raw data beyond just structured data, including video, images, and voice data; more secure internal (across regulatory boundaries) collaboration and external (between organizations) collaboration; and a lower chance of non-compliance due to simplified, more cohesive contracting processes.
Federated analytics is driving healthcare towards the future. By safely scaling access to raw health data, organizations can optimize processes for clinical trials, develop and deploy groundbreaking AI algorithms, and bolster pharmacovigilance. Thanks to the development of federated analytics solutions, there is no longer a need to choose between gaining powerful insights that will shape the future of healthcare and keeping patient data private.
M.R. Rangaswami is the Co-Founder of Sandhill.com