Healthcare organizations are increasingly turning to artificial intelligence to improve operational efficiency, ease clinical workflows, and enhance patient care and experience. However, many organizations don’t have the proper infrastructure to support AI initiatives.
Infrastructure Readiness
The 8th Annual Nutanix Healthcare Vertical Enterprise Cloud Index highlights challenges including infrastructure readiness and shadow AI. Scott Ragsdale, vice president of sales for healthcare and SLED at Nutanix, notes that healthcare organizations are adopting AI at a fast pace, but their infrastructure isn’t evolving to keep up with it.
Ragsdale says that 88% of healthcare leaders told them their current infrastructure isn’t fully ready to support AI deployment, and it’s evident. Overcoming technical debt to take on new AI workloads is the biggest challenge for organizations.
Addressing Shadow AI
Shadow AI is a major concern among healthcare leaders. Ragsdale says that shadow AI is a symptom, not the root problem. Clinicians, researchers, and business users are looking for ways to solve problems quicker and faster to improve outcomes for patients.
If they can’t get what they need, they’ll go out and download something that is unsanctioned or unsupported by the organization. The answer isn’t to hinder innovation; the answer is to create governed environments where innovations can happen safely.
Healthcare organizations need to adopt approved AI platforms and have clear policies around their usage. Ragsdale notes that containerization plays an important role in deploying AI models, providing portability, scalability, and consistency across the environment.
Improving Infrastructure Readiness
The report states that healthcare leaders view their current infrastructure as not fully ready to support AI deployment on-prem. Ragsdale says that the first step is recognizing that AI readiness isn’t about buying a bunch of GPUs and sticking them in the data center.
It’s about modernizing the entire operating model. They need infrastructure that can support hybrid environments, manage data consistently, and provide strong governance to run AI workloads where it makes the most sense.
Related: How to get a medical marijuanas card in West Virginia online
They also need to address legacy infrastructure and technical debt.
AI Adoption in Healthcare
The report notes that healthcare has always approached transformative technologies carefully because the stakes are so high in healthcare. Patients’ lives and outcomes are on the line. They have to be very careful about what they adopt.
Ragsdale says that healthcare leaders want to understand governance, clinical validation, security, and accountability, and what that means for regulations like HIPAA.
The report reinforces that the future of AI in healthcare is going to be hybrid, in the cloud, containerized, in the data center, and living in multiple different constructs. AI has to be at the point of care in the patient rooms.
They need to be deliberate about how they adopt AI. They need to start with a specific business or clinical challenge, establish clear success criteria, and validate outcomes through a controlled pilot.
By taking a disciplined approach, organizations will be more likely to achieve meaningful outcomes and avoid the pitfalls that often accompany emerging technologies. As Ragsdale notes, learning from peers who are already further along in their AI journey and leveraging industry expertise where appropriate can also help organizations deliver measurable value.
Healthcare organizations can support AI workloads by adopting a hybrid infrastructure that can handle the demands of AI.