Defining Observability vs. Monitoring in Healthcare

“Observability helps identify security threats and potential breaches, and it assists in maintaining compliance with healthcare regulations like HIPAA,” says John Wilson, senior director for public sector and director of sales for healthcare at SolarWinds.

Observability is a core part of DevSecOps, which is the integration of security into the software development process without slowing down the work of developers, according to Patrick Lin, senior vice president and general manager of observability at Splunk.

“You can catch security issues early, right before they make it out, and do it in a way that does not slow down your development cycle,” Lin says.

“Observability integrates with DevSecOps practices to enhance the collaboration between development security and operations teams. That ensures that security is embedded throughout the development lifecycle and the operational process,” Wilson says.

He also explains how organizations use predictive analytics as part of observability.

“By leveraging predictive analytics, observability tools can forecast potential issues and system behaviors. That allows healthcare organizations to prepare and respond proactively, which enhances system reliability and patient care,” Wilson says.

Observability vs. Monitoring: How They Compare in Healthcare

While observability provides a holistic view of a healthcare network’s performance, monitoring is an element of observability that lets organizations track specific data, such as the performance of connected medical devices.

“Monitoring is a big component with hospital devices and the Internet of Things. Observability goes beyond monitoring to proactively look at everything holistically and give you insights into the internal state of the systems and how things are working collectively,” Wilson says.

Examples of monitoring entail tracking a network to make sure the right number of packets travels from point A to point B, or keeping tabs on CPU use or the number of requests an application is serving, Lin says.

Monitoring is “more of a reactive activity that’s taking place, but ideally we want to become more proactive and try to address issues before they become major events,” says Christopher Kunney, 2024 HealthTech influencer and host of the Straight Outta Health IT podcast. 

Healthcare organizations monitor the interoperability of practice management solutions, electronic health records and other systems to look for data discrepancies between them, whether they’re clinical, administrative, financial or customer-facing applications, Kunney says. Organizations also monitor data for accuracy and efficacy.

Lin notes how observability lets healthcare organizations monitor holistically the components of remote care, including the quality of connectivity in telehealth sessions and the performance of remote monitoring devices. Observability tools can detect whether a transaction such as scheduling or billing is taking longer than expected, which could lead a customer to call a provider rather than use online tools, Lin says. He notes that healthcare providers try to avoid these calls to improve productivity and allow apps to deliver information to customers instead.

How Health Systems Use Logs, Metrics and Distributed Tracing

“Similar to how providers invest in whole-person health, providers are beginning to invest in whole-system health,” Morgan says.

Just as healthcare providers aim to detect patient conditions early, IT managers use real-time detection and automation to gain visibility into system behavior and enable optimal performance by spotting issues before they escalate, she says.

By using observability tools to track data insights, healthcare organizations can address business challenges around supply chain and procurement, demand forecasting, and remote care provisioning, Morgan says.

Logs provide a historical record and enable healthcare organizations to respond to the data. Organizations can collect a snapshot of user activity and spot potential errors. They can also assign rules that react to anomalies in the historic data, Kunney says.

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In addition, predefined metrics allow organizations to track alerts to see if a system has gone beyond normal thresholds, he says.

Observability also includes distributed tracing, which happens during an application’s monitoring stage and involves forensics on an application. Distributed tracing lets healthcare organizations spot anomalies or inaccuracies in the data as well as performance issues, Kunney explains.

“That distributed tracing technique is a way to start to holistically track and visualize the flow of those requests. Then you can understand, end to end, what happens in the system and try to identify where the bottlenecks and failures are,” Kunney says.

“Logs are really great for troubleshooting, and traces are really about understanding different dependencies across services and systems,” Lin says.

Whether a healthcare organization uses logs or tracing depends on the type of data it’s trying to collect or the problem it needs to debug, he says.

How AI and ML Will Make Up Next-Gen Observability

Kunney notes that artificial intelligence and machine learning allow healthcare organizations to self-heal IT infrastructure. 

“As we try to move away from humans touching it and create more automated responses and self-healing responses, it can identify the gaps and autocorrect where these issues might be occurring,” Kunney says.

As observability evolves, AI and ML could enable healthcare organizations to digest and evaluate data from diverse systems faster and more accurately. Next-generation observability will also include more real-time metrics rather than checking logs from a week or a month ago, Kunney says.

ML models can analyze patterns in traces and logs to identify the cause of an issue, Wilson says.

“On the network side, AI-powered observability can analyze network traffic to detect potential security threats and initiate a response to mitigate them,” Wilson says. “On the patient side, observability tools can track the status of connected medical devices, such as pacemakers, and proactively alert us if performance metrics indicate a potential failure, allowing us to address issues before they become critical.”

Organizations are in the early stages of using generative AI for observability, Lin says.

“Generative AI and large language models are being applied to this area in a way that helps with reasoning across the different sources of data that could augment what a human would otherwise have to sort out,” he says.