The possibilities of artificial intelligence, and more specifically generative AI (Gen AI) in healthcare are vast — streamlining administrative tasks, expediting patient diagnosis, and even helping with medical research. Right now they’re focused on point AI solutions producing real but limited impact on healthcare outcomes. The potential impact is much greater.
The first step for any healthcare organization leveraging AI is a sound, comprehensive data storage strategy. Any large or small language model is only as good as the data upon which it’s trained. As a result, poor data storage runs the risk of AI outputs based on incorrect, incomplete, and/or biased data. If AI is directly connected to patient care, the stakes are too high for hospital staff to leverage this technology incorrectly. Before the latest AI wave hits healthcare, organizations have the opportunity to build a strong foundation with proper data storage.
Use Cases for Early Adopters
Combining modern data storage with AI presents advantages for organizations across each segment of the healthcare industry. For example, payers can create and/or implement models that reduce claims processing times or accelerate fraud detection. For providers, models can streamline clinical diagnosis or assist doctors in obtaining prior authorization, which expedites patient care. In enterprise imaging, healthcare organizations can leverage algorithms that reduce turnaround time on MRI results from a half hour to five minutes.
To bring the best out of each model, health system CIOs and their teams must have access to clean, organized, and relevant data. For instance, if payers are trying to detect fraud, they must train their models on data that outlines typical fraud schemes such as double billing, upcoding, or identity fraud. In the case of providers, the AI tools they leverage must be trained on data relevant to diagnosis — such as common risk factors and health patterns.
The best way to ensure clean and organized data is through central, unified, accessible databases. The good news is that most health systems are home to volumes of relevant data that can make their AI algorithms extremely effective; they just need to find the data, bring it together, and make it easily accessible.
Avoiding Missteps to Speed Innovation
In my experience helping health systems with AI, I’ve seen missteps in the early phases of planning and implementation that can affect budget, impair data output, and slow business outcomes. Here are two missteps that can be avoided when modern data storage is in the mix.
- Not clearly defining the problem to be solved — and what opportunity that leads to. Health systems must decide “What are we trying to solve,” “What type of model already exists, or do we need to create our own model” and “How fast should we scale these use cases.” When an AI plan lacks a specific path forward, ultimate consequences can be negative ROI, potential legal issues, and negative impact on patient care.
- Investing in twice the amount of AI technology needed now or in the future: The right data storage platform will allow scalability as AI use cases grow. This could mean leveraging a data-as-a-service platform that gives health systems the power to choose how much storage is necessary as they build their AI strategy.
With a combination of AI and a data storage platform, health systems can avoid these missteps and others. This results in greater cost savings, more accurate output and impactful business outcomes, and faster innovation.
Improved Healthcare at the Intersection of AI and Data Storage
Above, I mentioned the importance of finding data and bringing it together. However, that doesn’t necessarily mean keeping it in one centralized location. There are multiple benefits of having specific workloads/data on-premises and others in the cloud or somewhere in between. The ultimate goal is to leverage a data storage platform that seamlessly connects them and makes data accessible and useful regardless of where it resides.
For example, St. Joseph’s Health, a world-class hospital and healthcare network offering a full continuum of care with a special concern for underserved populations in the northern New Jersey area, is improving and advancing patient care by harnessing more advanced data. With a flexible, secure, and scalable data platform, St. Joseph’s IT infrastructure can support multiple workloads — whether they’re on-premises or in the cloud. These workloads include data analytics, virtual desktops, and AI implementation. For instance, the health system leverages AI in its radiology department. Through leveraging AI, the department gains more accurate image analysis, treatment recommendations, and, ultimately, more streamlined patient care.
Recently, I spoke with Imran Salim, Vice President, Strategic Solutions, Vertical Markets, at CDW about health systems that are leveraging a modern data storage platform with AI. I asked for his take on some of the benefits he’s seeing that are leading to higher levels of patient care.
“It’s undeniable how the power of an AI strategy that incorporates a data storage platform sets health systems up for greater patient outcomes. We’re seeing providers gleaning better data insights allowing them to make more informed decisions. In enterprise imaging, we’re working with radiologists that are experiencing far faster image retrieval – resulting in overall operational efficiencies and improved patient care.”
Data storage is the foundation for AI in healthcare
The implementation of AI will soon exist in most of the healthcare industry — from payer organizations to major hospitals to local doctors’ offices. In fact, not having AI will place certain health systems at a disadvantage, which in turn will have a negative impact on their ability to serve patients or push research forward. Leveraging a data storage platform that enables a true data ecosystem, faster workload performance, and scalable AI use cases is the best way for health systems to adequately prepare for when AI becomes table stakes. It’s time for our industry to realize that the combination of data storage and AI will ultimately speed innovation, generate targeted business outcomes, and help patients thrive.
About Jon Kimerle
Jon Kimerle serves as Sr. Manager, Global Healthcare Strategic Alliances at Pure Storage. He is responsible for managing the relationship with Epic Systems, leading Epic’s implementation to transform 20+ hospitals in four states, 3,000 employed physicians practices, and 40,000 users. He is also a strategic contributor for Pure Storage healthcare solutions business development.
Prior to joining Pure, Jon served in several senior IT leadership roles including Interim CIO, VP of IT Strategy and Planning, and VP of Clinical Transformation with SSM Health, a $8B Integrated Delivery Network in the Midwest. He has over 29 years of healthcare industry experience.
