Healthcare Industry Utilization of AIOps: An Explanation

In the old days when healthcare's IT infrastructure was limited to servers in closets, keeping tabs on system performance, access, and security was a walk in the park. But times have changed, and with the rise of Software as a Service tools, and consumers' rights to access their data, things have gotten a bit complicated.

"Back in the day, you could just power down and reboot," says Patrick Lin, senior vice president and general manager of observability at Splunk. "Nowadays, systems have become more complex, and there's a helicopter-load of data to process and analyze."

This explosion in the volume and complexity of workloads has resulted in lots of noise that's increasingly difficult to separate from valid signals. According to Krishna Sai, group vice president of engineering at SolarWinds, it's impossible to do that manually, like finding a needle in a haystack.

Enter artificial intelligence for IT operations, or AIOps. By automating the analysis of systems and applications, AIOps helps IT teams understand what's going on and orchestrate a response. Mature solutions are even designed to predict bottlenecks or security vulnerabilities before they cause any trouble.

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The Difference Between AIOps and Observability

Observability is often the first step for many organizations in making sense of what's happening with software systems. "Observability means gaining insight into system performance based on the data you gather through logs, performance metrics, and traces," Sai says.

The primary benefits of observability are lowering the cost of downtime and improving digital resilience, allowing IT teams to find and fix problems faster. AIOps takes it a step further by applying intelligence to data, recognizing patterns in that performance data, and predicting potential issues.

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Domain-Agnostic vs. Domain-Centric AIOps

The choice of AIOps flavor an organization uses can greatly impact its degree of assistance.

A domain-agnostic approach pulls data from various sources to solve problems across multiple domains, such as networking, storage, and security. These tools can provide a holistic view of overall performance but may lack the specificity needed to address a particular pain point, use case, or industry requirement.

On the other hand, a domain-centric tool homes in on a specific domain—whether it's an IT environment or a vertical industry. Its AI models of detection and analysis have been trained on data sets specific to that domain, giving it a specialized understanding.

"If you apply a domain-centric tool to a network to identify the cause of a bottleneck, the models have a specialized understanding of standard network protocols and patterns," Sai says. "It knows the difference between a distributed denial-of-service attack and a misconfiguration."

Regardless of the approach, organizations must ensure AI models are deployed responsibly, advises Sai. This involves using robust data sets, transparent models with a high fairness coefficient, ensuring there's always a human in the loop to verify the model's output, and aiming for a natural transition for IT teams as they begin using AIOps tools.

Krishna Sai, Group Vice President of Engineering, SolarWinds

Additional Benefits of AIOps: Responding and Reporting

One immediate advantage of AIOps in healthcare is insight into how mission-critical clinical applications are doing. "It's useful for the things you care most about being always up, available, and performing in the right way," Lin says. The same goes for the infrastructure running those apps.

From there, IT teams have the power to take appropriate action, with good AIOps tools analyzing events and patterns, and determining if they're related. AIOps is helping IT teams understand what's not normal so they can do something about it, reducing the need for late-night "war room" meetings.

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Additionally, AIOps is well equipped to assess a situation and recommend the right level of incident response, from a routine help desk ticket to serious overnight alerts. It even provides junior staffers with peace of mind, knowing that the AI tool is doing most of the heavy lifting.

The AIOps tool's ongoing log of incidents and responses helps organizations in two critical ways, says Sai. One is by providing an audit trail that complements compliance reporting. The other is by identifying and even predicting system vulnerabilities, such as outdated medical devices.

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The increasing complexity of workloads has necessitated the use of artificial intelligence for IT operations (AIOps) to automate the analysis of systems and applications, helping IT teams understand system performance and predict potential issues. In contrast, observability serves as a foundation for understanding system performance and lowering downtime costs but lacks the intelligence to recognize patterns and predict issues, as provided by AIOps.

Healthcare Industry Utilization of AIOps: An Explanation