How AI Impacts Virtualization Needs
Integrating AI and automation with virtualization allows health systems to gain visibility into their data and manage it.
Health systems such as Mary Washington Healthcare in Fredericksburg, Va., have deployed VCF to gain visibility across IT operations. Mary Washington plans to automate its vCenter virtual machines with VCF Automation.
As health systems leverage AI as part of their virtualization strategy, they will experience a learning curve, according to Sachin Mullick, director of product management for OpenShift Virtualization and OpenShift Edge at Red Hat.
“Built-in generative AI assistants from the virtualization provider can significantly reduce the learning curve for these new technologies while providing troubleshooting assistance to solve hard problems in minutes,” Mullick says.
Health systems can use AI to detect sensitive patient health information and control access to sensitive data while also preventing leakage, he adds.
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“Integrating applications with proper sovereignty controls and data management tools is providing an improved patient experience,” Mullick says.
Miller says full-stack virtualization and full-stack automation can be considered “almost table stakes” when it comes to running AI workloads, including large language models.
“In a modern platform, you have to have both VMs and Kubernetes workloads supported on top of the platform to support AI,” Miller says.
The stack that runs LLMs should be both container-based and VM-based, he adds.
Here are some steps health systems can take to fine-tune their approach to virtualization.
Optimizing Virtualization Strategy in Healthcare
Upgrading core infrastructure should be a key approach to virtualization, according to Shannon Germain Farraher, senior analyst for healthcare at Forrester.
“Virtualization optimization is most effective when aligned with enterprise-wide modernization efforts,” Germain Farraher says.
Modernizing Core Infrastructure
To boost the performance and resilience of virtualized workloads, health systems should invest in cloud, security, SaaS/PaaS ecosystems and data infrastructure. In addition, by embedding a virtualization strategy into broader digital transformation goals such as clinician enablement, automation and analytics, health systems can avoid “siloed, piecemeal modernization,” she says.
Germain Farraher recommends that large health systems modernize their compute, network and storage capabilities to aid advanced analytics, AI and enterprise-wide digital platforms. Meanwhile, midsize health systems should build a robust cloud-ready base, and small practices should stabilize their infrastructure by developing compliance-first IT and secure telehealth capabilities.
“This ensures the virtual environment can handle clinical, operational and data‑intensive workloads reliably and cost‑effectively,” Germain Farraher says.
