NKP acts as the Kubernetes layer, enabling seamless management and scalability of containerized environments, whether for small-scale tasks like summarizing clinical notes or larger projects like patient intake modeling. The flexibility of NKP also allows workloads to run on-premises or in public clouds as needs evolve.
Finally, NUS delivers secure, high-performance storage for large file and object data sets, ensuring compliance with healthcare regulations such as HIPAA.
This unified approach means healthcare organizations can deploy, scale and manage LLMs efficiently. For example, provisioning a new AI workload to automate clinical summaries can now be achieved with minimal manual intervention, reducing setup time and simplifying operations.
HEALTHTECH: What specific features of NAI address concerns about data security and patient privacy in healthcare?
GRANATA: Nutanix ensures that security and patient privacy are treated as priorities across every layer of its platform. NKP lets healthcare organizations keep sensitive workloads on-premises, meeting compliance standards including HIPAA and HITRUST. Nutanix also offers globally recognized certifications, including ISO/IEC 27001 for information security and SOC 2 Type 2 for security controls.
Other robust security features include role-based access controls, encryption for data at rest and in transit, and a zero-trust architecture. These safeguards are embedded into every aspect of the platform, from its Kubernetes management layer to its unified storage solution.
All of this security gives healthcare organizations the confidence to deploy AI applications with the knowledge that sensitive data is secure and compliant.
HEALTHTECH: How does the platform’s compatibility with public clouds enhance flexibility for healthcare organizations adopting AI?
GRANATA: Nutanix’s solutions are built on the principle of choice, enabling healthcare organizations to tailor infrastructure to their needs. With NAI, organizations can integrate NKP with leading cloud-native Kubernetes solutions such as Microsoft’s Azure Kubernetes Service, Amazon Web Services’ Amazon Elastic Kubernetes Service or Google Cloud Platform’s Google Kubernetes Engine.
For example, hospitals can use NKP to manage sensitive AI workloads on-premises, such as LLMs trained on patient records to ensure privacy and compliance. Simultaneously, they can leverage public clouds for nonsensitive tasks such as training models with synthetic data sets or handling overflow workloads during peak periods.
NAI bridges these environments by allowing workloads to move fluidly between on-premises and cloud platforms without sacrificing efficiency or governance. This hybrid approach provides the scalability and adaptability to meet evolving demands while maintaining control and cost-efficiency.
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HEALTHTECH: How does NAI’s user interface reduce the complexity of managing GenAI deployments?
GRANATA: The intuitive interface of NAI simplifies the management of GenAI deployments, much like a well-designed electronic health record system streamlines patient care. User-friendly dashboards and automation tools guide IT staff and reduce time spent on manual infrastructure tasks.
This simplicity extends beyond IT. NAI enables AI-driven automation across departments, unlocking AI use cases such as scheduling patient transport, managing medical supply orders or generating discharge instructions.
Keeping the administrative burden low through a purpose-built infrastructure stack equips IT staff to focus on mission-critical responsibilities and allows healthcare organizations to deploy AI solutions without requiring extensive technical expertise or significant time investment.
HEALTHTECH: What strategies do you recommend for healthcare organizations to balance the costs of deploying GenAI while ensuring scalability and security?
GRANATA: Cost management in AI deployment is similar to managing a hospital’s budget for medical supplies: You prioritize high-impact needs while avoiding unnecessary spending. Nutanix’s pay-as-you-grow model supports this approach, allowing organizations to scale AI infrastructure incrementally rather than overcommitting resources up front.
For sensitive workloads, running AI models on-premises can reduce costs and risk compared with outsourcing to the cloud. A hospital could train billing automation models in-house while using public clouds for tasks that don’t involve sensitive data.
Ultimately, the key is to start with understanding the targeted pain points that a customer is confident can be solved with AI tools, such as billing or triaging patient inquiries. By addressing specific needs, healthcare organizations can achieve meaningful results while maintaining control over costs, security and scalability.
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