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New Functionality from Goliath Provides Near Real-Time End User Experience Monitoring for Healthcare

Goliath Technologies has officially released products designed to enable systems administrators to get advanced warning of potential end user experience issues.

The new feature and functionality enhancements were added to Goliath Performance Monitor and Goliath Logon Simulator.

Goliath Performance Monitor is deployed on Citrix servers running Cerner applications, which enable healthcare organizations to get near real-time performance metrics, reporting and analysis on end user experience and application logon times.

Goliath monitoring capabilities combined with Cerner’s remote hosting technologies and services, provides health care organizations with advanced warning of potential end user experience issues and evidence of root cause to prevent future issues.

Goliath’s Logon Simulator proactively logs on to applications and alerts IT administrators to logon failure or slowness before it is noticed by the end user community.

“Our End User Experience Monitoring and Management Technology for Citrix delivered applications is already deployed in hundreds of hospitals across the US and has been very well received by both hospitals and partners,” said Thomas Charlton, Chairman and CEO of Goliath Technologies.

Charlton continued, “Our strategy of developing technology that improves the experience of clinicians and healthcare workers when they use major electronic health records applications continues to deliver value to all stakeholders who are ultimately concerned with delivering high quality patient care.”

The Logon Simulator can be configured in minutes from any hospital location.

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New Functionality from Goliath Provides Near Real-Time End User Experience Monitoring for Healthcare

Goliath Technologies has officially released products designed to enable systems administrators to get advanced warning of potential end user experience issues.

The new feature and functionality enhancements were added to Goliath Performance Monitor and Goliath Logon Simulator.

Goliath Performance Monitor is deployed on Citrix servers running Cerner applications, which enable healthcare organizations to get near real-time performance metrics, reporting and analysis on end user experience and application logon times.

Goliath monitoring capabilities combined with Cerner’s remote hosting technologies and services, provides health care organizations with advanced warning of potential end user experience issues and evidence of root cause to prevent future issues.

Goliath’s Logon Simulator proactively logs on to applications and alerts IT administrators to logon failure or slowness before it is noticed by the end user community.

“Our End User Experience Monitoring and Management Technology for Citrix delivered applications is already deployed in hundreds of hospitals across the US and has been very well received by both hospitals and partners,” said Thomas Charlton, Chairman and CEO of Goliath Technologies.

Charlton continued, “Our strategy of developing technology that improves the experience of clinicians and healthcare workers when they use major electronic health records applications continues to deliver value to all stakeholders who are ultimately concerned with delivering high quality patient care.”

The Logon Simulator can be configured in minutes from any hospital location.

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Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

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