Jun 06 2024

Maximizing AI Deployment Value in Healthcare Requires a Hybrid Edge-to-Cloud Strategy

Healthcare organizations must also optimize their computing resources to support innovative clinical workflows.

The main goal of a hospital is to provide patients with optimal care. Unfortunately, events of the past few years have made achieving that objective more difficult. Increasing cyberattacks have forced hospital IT administrators to turn their attention to cybersecurity to protect sensitive patient data. Decreasing revenue streams and rising labor costs have led to budget constraints. Staffing shortages are leaving clinicians with less time to devote to patients. Despite these challenges, many institutions are still committed to innovating patient care.

Hospital systems are investing in artificial intelligence–based solutions to help mitigate these and other challenges while expediting and improving the quality of patient care. However, when it comes time for deployment, healthcare organizations are often stymied by questions of how to optimize their use of AI tools. They know they need to provide clinicians with real-time intelligence at the point of care, but they may be unsure of how to do this in a way that’s practical, cost-effective and scalable, especially if their infrastructures are reliant on older legacy systems.

The answer is twofold. First, healthcare organizations should consider adopting flexible hybrid edge-to-cloud infrastructures that allow them to process data at the edge and in the cloud cost-effectively. Second, they must optimize their computing resources, so they are able to extract valuable information while maintaining maximum performance and efficiency.

Click below to learn how to optimize healthcare’s connection to the hybrid cloud.


Embracing a Hybrid Cloud Approach to Healthcare

Hospitals are swimming in data from patient diagnostics, admissions, billing and more. This data is being stored at the edge and in a combination of private and public clouds. Each has its benefits and drawbacks.

  • Information at the edge is often derived from devices such as MRI machines, X-rays, ultrasounds and so forth, which require near real-time insights. For example, an ultrasound running AI can help a technician locate nerves when administering anesthesia. But storage and processing capacity at the edge is still somewhat limited.
  • Private clouds offer organizational control and the ability to process AI workloads across a range of data sets. A private cloud allows hospitals to use AI to highlight concerning areas on an X-ray or MRI report to augment a radiologist’s knowledge. But data transfer times are longer than those used for edge processing.
  • Public clouds are great for complex workloads that benefit from public cloud AI training models that pull from de-identified diverse patient populations. But data transmission times can be lengthy, organizations need to sign business requirements to keep their data secure, and egress costs can be expensive.

A hybrid edge-to-cloud approach involving a combination of on-premises edge and cloud computing supports each of these environments, with the added benefits of being highly cost-effective and flexible. With a hybrid edge-to-cloud environment, hospitals can choose to manage some workloads onsite while transferring others to the cloud, thereby optimizing the cost of their computing resources. They can perform onsite processing of even the most complex workloads, allowing clinicians to receive actionable recommendations quickly while avoiding some costs associated with data transfer.

READ MORE: Overcome the top three challenges of a hybrid cloud environment.

Optimizing Healthcare Organization’s Compute Resources

Successful implementation of an efficient and effective hybrid cloud and edge infrastructure is dependent on hospitals’ ability to optimize their compute resources. This is a two-step process.

The first step is to prioritize the data being collected by myriad devices, workflows and technologies within the healthcare organization. Many hospitals continue to rely on legacy solutions and devices that are actively compiling information. There’s often real value in that data, but solutions that are a decade old or more aren’t equipped to run machine learning algorithms on that information. Therefore, hospitals should focus on the data that is most important to their clinical operations, find the machines that are producing that data and extract the information.

The second step is to determine where that data needs to go. Some information may require only incremental analysis that can be handled easily at the edge of the network, while deeper algorithmic analysis may entail transferal to a private or public cloud. Perhaps the workload requires a combination of edge and central processing.

Whatever the case, workloads can be shifted appropriately to maximize the use of all computing resources. This will minimize bandwidth and storage bottlenecks and strike a good balance between optimal workload performance and lower costs.

How Hybrid Edge-to-Cloud Computing Supports Clinicians

As hospital systems continue to onboard AI and real-time analytics, they will need a flexible infrastructure that supports the demands of each of these solutions without breaking their budgets. Adopting a hybrid edge-to-cloud approach and optimizing resources appropriately will allow them to effectively leverage these technologies, overcome many of their current challenges, and help clinicians provide exceptional patient care.

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