“`html
Health systems are increasingly turning to artificial intelligence to manage the growing complexity of their data, and virtualization is proving to be a crucial component of that strategy.Integrating AI and automation with virtualization allows healthcare organizations to
AI and the Future of Healthcare Virtualization
Table of Contents
The integration of AI is poised to reshape how health systems approach virtualization, offering both opportunities and challenges.
- AI can help health systems detect sensitive patient information and control data access.
- Full-stack virtualization and automation are becoming essential for running AI workloads.
- Modernizing core infrastructure is key to optimizing virtualization strategies.
- Health systems should align virtualization optimization with broader digital transformation efforts.
Mary Washington Healthcare in Fredericksburg, Virginia, has already deployed VMware Cloud Foundation (VCF) to enhance visibility across its IT operations and plans to automate its vCenter virtual machines with VCF Automation.
As health systems embrace AI within their virtualization strategies,a learning curve is unavoidable,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 substantially reduce the learning curve for these new technologies while providing troubleshooting assistance to solve hard problems in minutes,” Mullick says.
AI isn’t just about streamlining processes; itS also about bolstering security. Health systems can leverage AI to detect sensitive patient health information and control access to that data, effectively preventing potential data leakage, Mullick adds. “Integrating applications with proper sovereignty controls and data management tools is providing an improved patient experience,” he says.
The Foundation for AI Workloads
According to industry experts, full-stack virtualization and full-stack automation are quickly becoming “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,” says Miller.
The ideal infrastructure for running large language models (LLMs) isn’t simply one or the other-it’s a hybrid approach. The stack should be both container-based and VM-based to maximize adaptability and performance.
Optimizing Virtualization Strategies
Upgrading core infrastructure is a basic 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 for the Future
To enhance the performance and resilience of virtualized workloads, health systems should prioritize investments in cloud technologies, robust security measures, SaaS/PaaS ecosystems, and extensive data infrastructure. Embedding a virtualization strategy into broader digital transformation goals-such as clinician enablement, automation, and advanced analytics-can definitely help avoid fragmented, piecemeal modernization efforts.
Germain Farraher recommends a tiered approach to modernization. Large health systems should focus on modernizing their compute, network, and storage capabilities to support advanced analytics, AI, and enterprise-wide digital platforms. Midsize health systems should build a robust cloud-ready base,while smaller practices should prioritize stabilizing 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.
