Healthcare generates vast volumes of information across diverse settings, from routine clinic visits to research and post-acute care. As expectations for efficient collaboration and improved outcomes rise, the data centres underpinning clinical and operational systems face mounting pressure. New artificial intelligence capabilities are accelerating this shift, prompting organisations to reassess where workloads reside, how infrastructure scales and which designs deliver resilience. The landscape now includes legacy platforms alongside emerging solutions that do not always integrate cleanly, altering refresh cycles and the skills needed to manage environments. With electronic health records moving to the cloud, and on-premises workloads demanding more power and cooling, the imperative is to modernise deliberately, align architecture with strategy and prepare for higher-density compute to support evolving clinical workflows.
Evolving Architectures and Skills
Healthcare’s infrastructure reveals a mix of advanced innovations and antiquated architectures. That balance is becoming harder to maintain due to rapid adoption of AI-powered tools, which expose integration gaps between older systems and newer platforms. Traditional refresh forecasts are shifting and so are staffing models. Smaller teams may be required to support multiple platforms rather than specialise in hardware technology, increasing the premium on broad expertise. Comfort with legacy approaches can delay change, yet the desire to streamline workflows and optimise environments is pushing organisations to revisit long-standing assumptions.
A central task is placing workloads where they operate most efficiently. Many providers are migrating electronic health records from on-premises to the cloud, while those retaining local workloads are introducing more advanced processors that raise requirements for power, cooling and rack density. Agility to incorporate high-density environments becomes essential as projects emerge. By aligning placement with application needs, organisations can reduce licensing costs and maximise the benefits of AI. Because improvements arrive quickly year by year, an explicit strategy helps institutions act intentionally even before modernisation milestones are reached. The design objective is a cohesive architecture that supports complex environments without forcing ill-fitting solutions.
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VDI And Hyperconverged Choices
Virtual desktop infrastructure (VDI) is widely used across the sector to support mobility and secure access. Thin clients enable clinicians to connect from varied locations while maintaining compliance and keeping patient data in the data centre rather than on endpoint devices. Standardised images can lower management overhead, and consolidating physical machines reduces the hardware footprint, which can in turn lessen power and cooling demands. These operational efficiencies contribute to a more resilient environment that allows teams to act on patient information consistently.
Financial considerations also shape architectural choices. Many organisations now prefer a hybrid approach rather than committing fully to a single model, reflecting the varied workloads that healthcare must support. Economic uncertainty encourages a tilt toward operational expenditure over capital expenditure, and hyperconverged infrastructure can be appropriate for specific healthcare verticals that align with its characteristics. The critical step is to evaluate enterprise applications comprehensively and determine where they are best served. That assessment guides the selection of platforms and placements that optimise performance across the portfolio. When environments are designed properly, they can be supported and maintained effectively without resorting to compromises that limit capability or inflate cost.
GPUs, Cyber Recovery and AI Use Cases
Graphics processing units (GPUs) are becoming more important within VDI, driven primarily by imaging and virtual care. As operating systems advance graphics features, some organisations plan for approximately 1 gigabyte of allocation per VDI user. Telemedicine uptake has heightened attention to the graphics intensity required at thin clients to sustain resolution and image quality for virtual visits and collaboration. Incorporating GPUs into VDI helps clinicians such as radiologists and cardiologists view high-resolution images wherever and whenever needed, supporting faster diagnostic decision-making and real-time teamwork across locations. As these use cases grow, GPU utilisation within VDI is expected to increase accordingly.
Resilience is also evolving through cyber recovery readiness. Changing HIPAA compliance expectations point to defined recovery timeframes following cyber events. In response, providers are exploring clean rooms for cyber recovery, implemented either on-premises or in public cloud environments. These clean rooms enable recovery processes that aim to mitigate operational disruption and protect continuity of care. Alongside resilience investments, AI conversations continue to expand. Organisations are investigating ambient listening capabilities that can understand patients’ native languages to streamline intake and care interactions. They are also examining ways to optimise electronic health records through retrieval-augmented generation so clinicians can surface relevant information without extensive scrolling. Agentic AI is being explored to remove repetitive administrative tasks from clinical and support teams, freeing capacity for higher-value work.
Healthcare data centres are entering a period of purposeful change as AI, cloud migration and resilience requirements converge. Success depends on clear workload placement strategies, readiness for higher-density compute and pragmatic choices around VDI, hyperconverged infrastructure and GPU utilisation. Clean room approaches for cyber recovery and targeted AI use cases in intake, documentation and information retrieval reflect the same priority: maintain secure, efficient access to data while supporting clinicians at the point of care. By aligning architecture with application needs and operational objectives, providers can modernise at a sustainable pace, control costs and position their environments to meet growing expectations for collaboration, performance and reliability.
Source: HealthTech
Image Credit: iStock
