If you do a quick survey of the news coming out of the major healthcare conferences over the last year, it’s quite clear that Artificial Intelligence (AI) and how it will impact healthcare delivery and patient outcomes is top of mind for clinicians and administrators.
“Everyone knows AI will have an impact on healthcare, but most are still trying to understand the technology and how it can be applied to their endeavors,” said Mike McNamara Senior Manager, Product & Solution Marketing at NetApp. “In talking to our healthcare customers there’s no question about the benefits AI is delivering today in medical imaging, patient care, and research and development but it will make a far bigger impact in the future.”
AI is poised to transform industry by using advanced data-based learning to identify patterns, develop predictive insights, and enable increasingly accurate autonomous systems. AI offers the promise of faster and more customized pharmaceutical development, more effective fraud detection for insurers, the opportunity to reduce physician burn out with improved EHR workflows, and other operational efficiencies that will vastly increase productivity, help manage costs of care, and, of course, speed the development of new treatments and therapies.
There are two requirements for organizations to be successful in integrating AI into clinical workflows and research and development. The first is access to large amounts of data and the second is that this data is usable, accessible, and protected. While healthcare organizations have no trouble meeting the first requirement with petabytes of data created each and every day, McNamara notes that nearly all organizations, regardless of industry, struggle with data usability, accessibility, portability, and security.
“A robust AI infrastructure needs to be flexible and future-proof to enable the organization to unlock the potential of data science on their AI journey from predictive analytics to where we will be in the future with autonomous decision making,” he said.
A key aspect of an AI-ready infrastructure is the ability to succeed without the limitations of where data resides. The infrastructure must enable an integrated data pipeline across edge, core, and cloud and streamline the flow of data from ingest, to prep, to training, and inference. It must support seamless, cost effective movement of data across on-premise and clouds.
“AI isn’t about machines churning out answers, it’s about unlocking the value of data. It’s about developing insights and knowledge that can be put to work for the good of the patient and provider that were previously unrealistic in terms of time and complexity,” McNamara concluded.
Learn more about building AI-ready data environments here.