Cloud-Based Platforms Provide a Secure Path to AI
As healthcare organizations consider AI adoption, many are looking toward platform-specific environments that are hosted in the cloud because scalability is easier, adoption is easier, and it costs less to support their infrastructure as AI usage grows.
Platforms can also provide more security for organizations that have less experience in the AI space. The risk of using a generative AI tool is that all of the organization’s data could potentially be ingested into that platform, including HR data and salary information, that then becomes available to employees who shouldn’t have access to it.
For that reason, many organizations are considering SaaS platforms as they begin their AI journeys. However, the most critical data, such as patient data, will likely stay in-house to better protect it from the outside world.
Artisight, for example, is a platform that adds an AI layer to patient care while improving clinical workflows. It can’t access data beyond what’s being fed into the platform through sensors or the electronic health record.
READ MORE: Maximizing AI value in healthcare requires a hybrid edge-to-cloud strategy.
How a Hybrid Cloud Strategy Supports Healthcare’s AI Initiatives
Large language models are the foundation of many AI tools used in healthcare today. As organizations build statistical algorithms or predictive models using LLMs, they begin to consume large amounts of data that require high compute power. The question for healthcare IT leaders becomes, do I spent money in the cloud for all this compute power or do I stay on-premises? The use case should help determine the right solution for an organization.
For use cases with smaller data sets, those computations will likely be run in the cloud. However, if an organization is going to consume so much compute power that its premium is maxed out, or if it doesn’t have the ability to consume enough, then it might be better for the organization to rely on its on-premises infrastructure.
Another risk of being 100% in the cloud, when it comes to AI, is that leveraging a cloud platform for the tool could give the organization capabilities beyond their current staff scope. Healthcare organizations often choose to use the cloud for AI use cases related to automation, especially AI security and automated compliance checks.
Best Practices for an AI and Hybrid Cloud Strategy
Before healthcare organizations begin their cloud journey, they need to ensure they are validating that their on-premises environment has no gaps. The security measures an organization has today will translate to the cloud, meaning it’s crucial to establish a strong security posture and good cyber resiliency before migrating to the cloud where those security measures will be equally important.
Going through rationalization and application dependency mapping exercises is another best practice. These steps will allow the organization to migrate only the desired workloads to the cloud, while rightsizing the assets. Migration isn’t a one-to-one process. For instance, an organization may have a set of servers for an application that lives on-premises. It’s likely that the servers are oversubscribed with more memory than needed because the organization didn’t want to have to touch it for a while. If that’s the case, and the organization doesn’t go through a rationalization exercise to understand what the workload consumes, but instead moves ahead using a one-to-one perspective, then the organization will likely be paying for more resources than it needs. This can cause costs to skyrocket when moving to the cloud.
CONSIDER: Ask yourself these three questions before modernizing your infrastructure.
As IT leadership starts the conversation about disaster recovery in the cloud and begins to build a landing zone with an IaaS model to test workloads in the cloud, the No. 1 best practice is to align with a good partner that can validate the configuration and security posture.
Healthcare organizations don’t have to approach infrastructure modernization and cloud migration alone. An experienced partner such as CDW can help IT teams from beginning to end, starting with an assessment of the organization’s current infrastructure and cloud readiness.
CDW can conduct assessments for security, application rationalization, and application categorization, along with application dependency mapping. All of these services are available to healthcare organizations as they begin their cloud migration process.
Once healthcare IT leaders understand their current state and their business goals, CDW can use its knowledge of the healthcare ecosystem to guide them through their entire journey.
