The following is a guest article by Jon Kimerle, Global Strategic Healthcare Alliances at Pure Storage
The digital transformation of healthcare continues to accelerate, and technologies like artificial intelligence (AI) are predicted to have an increasing impact on efficiency, quality, and scalability of health outcomes.
At present, AI is already driving high-quality, predictive patient care and better outcomes by making connections humans can’t, adding context, and unearthing new insights. The data generated by AI can facilitate early disease detection, personalized medications and care, the prevention of diagnostic or prescription interaction errors, analysis of treatment risks, and much more. Clinical AI algorithms have already begun to catalyze progress and promise in fields such as image-based diagnosis in dermatology and radiology, patient monitoring and management, and genome interpretation and drug discovery.
While adoption of AI applications in healthcare is low, a recent study found that more than half of IT buyers are prioritizing AI/Machine Learning technology investments for the next five years, meaning there is a unique opportunity for healthcare and life science organizations to leverage AI for faster, safer, and more impactful outcomes. To succeed, however, organizations need to address critical issues slowing adoption and limiting impact: limitations on data storage, data access issues, and data security and protection.
Rethinking Data Storage and Accessibility
Data is the lifeblood of healthcare and continues to reshape how the industry functions. While the volume of data will dramatically increase, with new data sets like social determinants of health, data from the IoMT, and genomic information, at the same time emerging technologies and AI applications will be accessing and using these larger data sets. The number of algorithms using these data sets will also dramatically increase, all adding up to stress on the entire solution stack, but especially on data storage tech and software. Legacy health IT infrastructure can only do so much to manage the influx of new information. Organizations need to consider where all of this new data is supposed to live, where AI systems and workloads will run, and how they can optimize their workflows and infrastructure to power analytics that genuinely impact outcomes.
With more data comes the need for smarter storage, requiring the underlying IT infrastructure to evolve and meet the new standards of scale and dimensionality previously unheard of. Without a future-proof data storage solution, AI will fail to be useful and provide the necessary support to the healthcare workforce it has promised. Analytics will be slowed, scaling will be stunted, and data silos will continue as a result, further hindering the true impact AI can make in life sciences research and healthcare.
As healthcare and research become more patient-centric, a shift towards personalized medicine will rely on more data, and AI workflows can reduce the time it takes to go from theory to insight. With a well-established data storage location and a place for these AI applications to operate from, healthcare and research organizations can turn ideas into actionable therapies that could improve patient lives. Teams distributed across different health care sectors, research centers, medical device companies and beyond will need to leverage petabytes of data seamlessly, yet current storage systems bottleneck performance and reduce easy access to the trove of information that AI sorts through or produces. Mining data for timely insights then becomes a task as daunting as the adoption of high-tech tools itself.
It currently takes quite a long time to integrate data and extract meaningful insights, leading to researchers spending more time engineering their data than leveraging it for clinical decisions and improved patient care. IT solutions need to offer data mobility and integration that allow researchers and healthcare workers to access data quickly and efficiently and enable them to know what they are looking at. Seamless data access will support superior AI analytics to enable faster therapeutics, diagnostics, and vaccine pipelines, and will decrease time to insights that affect patient care the most.
Protecting Data from Cyber Attacks
As more data is continually generated, and emerging technologies get integrated with critical life saving and prolonging operations, questions of data security and safety begin to arise. Ransomware attacks continue to dominate headlines as they’ve become more sophisticated, more frequent with longer times to recovery. At this point, it’s not a matter of “if” an attack occurs, but “when,” and organizations not only have to have the proper precautions in place to prevent and respond to an attack, but also, just as essential, a plan for rapid recovery.
Ransomware attacks prey on the inefficiencies of current complex IT environments filled with outdated data storage components and we cannot afford to increase the amount of patient and diagnostic data being created and sorted through with new emerging technologies and AI applications to be at risk for a cyber-attack. Downtime in a ransomware attack is the most costly and impactful to patient care and organizations should be investing in a more robust disaster recovery plan. Solutions need to look for ways to integrate AI and emerging tech into healthcare and life sciences without compromising the safety and security of patient data and information as we continue to move into the future of healthcare technology.
Upgrading Data Management to Facilitate AI
To fully reap the benefits of AI adoption in healthcare and life sciences, organizations need to harness insights effectively, which requires a holistic data management strategy. Without being able to quickly visualize and interact with healthcare data, healthcare and life sciences organizations will continue to be data rich but information poor. The underlying infrastructure issue stunting scalability, bottlenecking progress, and inhibiting timely insights can no longer be ignored. High-performing infrastructure is not a luxury but a necessity and will allow healthcare and research teams to have consistent access to vast amounts of data, scale storage capacity on-demand, and run compute-intensive analytics without compromising the security of the data it hosts.
By upgrading IT infrastructures, healthcare organizations have an incredible opportunity to capitalize on the real benefits of AI and other emerging technologies. When it comes to the future of healthcare, the potential of AI and big data to improve health to people demands that healthcare leaders ensure their organizations are data-ready to enable those outcomes.
About Jon Kimerle
Jon Kimerle serves as the Epic Alliance Manager at Pure Storage where he is responsible for managing the relationship with Epic Systems in Verona, WI and contributing on the healthcare solutions team with business development. Jon has 29 years of healthcare leadership experience with the past 20 years in senior healthcare IT leadership roles. His focus has been in taking a more strategic approach to IT and has significant experience in formulating complete, IT-enabled solutions and delivering enterprise-wide transformational projects. Jon led Epic implementations which transformed 20+ hospitals in four states, 3,000 employed physicians practices and 40,000 users. Prior to joining Pure, he served in several senior IT leadership roles including Interim CIO, VP of IT Strategy and Planning, and VP of Clinical Transformation. Jon holds both a BS in Health Administration and a Master of Health Administration (MHA) from the University of Missouri-Columbia.
