The Bedside Revolution: Scaling AI, Containers, and Clinical Continuity
Healthcare organizations are embedding AI into clinical and administrative workflows at an unprecedented pace. But as AI moves from the back office to the Point of Care, the gap between clinical ambition and infrastructure readiness is widening.
The latest Nutanix Enterprise Cloud Index surveyed healthcare IT leaders globally to understand how they are deploying AI, why containerization has become a clinical necessity, and what barriers stand in the way of achieving real-time AI at the bedside.
Inside, you’ll find data-driven insights on how healthcare leaders are navigating the technical friction of running modern, cloud-native workloads across complex hybrid environments while maintaining patient safety and data sovereignty.
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AI adoption is no longer confined to central data centers. Today, most healthcare data is generated at the bedside. In high-density environments like ICU beds, which can feature 15 to 20 connected devices, a single patient room can generate up to 7TB of data annually.
Relying solely on the public cloud introduces latency risks that can delay critical, real-time clinical decisions. By moving AI processing to the Point of Care (the clinical edge), healthcare organizations can eliminate cloud latency, manage massive data gravity, and ensure clinical continuity even if an external network connection fails.
Containers have become the foundational architecture for modern healthcare applications because they bundle code and dependencies into secure, highly portable environments.
According to our latest ECI report, 86% of healthcare IT leaders say AI is actively accelerating their container adoption. Containers are uniquely suited for deploying AI models across distributed, high-density device environments. They allow you to run modern, AI-enabled applications right alongside your traditional VM-based applications, balancing rapid innovation with strict data control.
It is a widespread and largely unmanaged challenge. A striking 79% of healthcare organizations report encountering AI applications or agents implemented by employees in non-IT functions.
This lack of official oversight creates massive security and business risks for 83% of IT leaders. Furthermore, 83% report that existing silos between business units and IT make it incredibly difficult to execute cohesive technology initiatives. Addressing Shadow AI requires an infrastructure that provides corporate governance and visibility without choking the innovation clinical teams need.
Data sovereignty is a non-negotiable priority, with 72% of healthcare IT professionals naming it a must-include factor in infrastructure decisions. Additionally, 54% feel compelled to keep their infrastructure within a single country due to stakeholder and regulatory expectations.
Running containerized AI applications on-premises or on a private cloud allows hospitals to keep sensitive Protected Health Information (PHI) entirely within their physical walls, ensuring compliance while maximizing technical performance and operational efficiency.
Honestly, no. 88% of healthcare IT leaders view their current infrastructure as not fully ready to support on-premises AI workloads.
True readiness requires a flexible, hybrid cloud model. Healthcare organizations cannot afford to completely abandon their current systems, nor can they rely exclusively on managed service providers. Over the next three years, the industry is balancing out: roughly half (50%) of containerized applications will run on-premises or private clouds, while the other half (54%) will utilize managed service providers. Nutanix bridges this gap by providing a unified platform that spans on-premises data centers, managed services, and the edge.
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