Clinical App Observability · OnStak Use Case

Find the slowdown before the doctor does.

When a clinical application stalls, a clinician is waiting, and a patient is too. If a third of those slowdowns have no clear cause, every fix is a guess. Here is how one pediatric hospital got sight of the clinical experience, from the device to the data centre.

100%

end-to-end visibility, user to data centre

55-70% lower

mean time to resolve

40% fewer

network team escalations

Clinician using a clinical application at a pediatric hospital bedside

Story highlights

  • End to end, at last. From the clinician’s device across Wi-Fi to SaaS to the data centre, the whole path became visible.
  • Guesswork out of troubleshooting. The 30-40% of SaaS slowdowns that had no clear cause could finally be traced, and MTTR fell 55-70%.
  • From reactive to predictive. Baseline alerting let the NOC catch issues early, and network-team escalations dropped 40%.

The situation

A hospital running on clinical apps it could not fully see.

The pediatric system’s doctors depended on clinical applications delivered over Wi-Fi, LAN, and SaaS, but had little visibility into how those apps actually performed at the point of care. Leadership set out to see the clinical experience end to end.

The problem

A third of the slowdowns had no clear cause.

Doctors felt the delays; the team could not always explain them. Three things made it hard.

Clinical apps were a black box

Doctors lacked visibility into how clinical applications were performing.

Slowdowns with no root cause

30-40% of SaaS slowdowns had no clear root cause to fix.

Reactive troubleshooting dragged

Wi-Fi and LAN issues took longer to resolve because the team worked reactively.

What OnStak did

Sight of the clinical experience, device to data centre.

OnStak instrumented the path clinicians actually use, and gave the NOC the signal to get ahead of problems.

1

Monitored the endpoint and the journey

Endpoint and synthetic monitoring configured for the clinical apps.

2

Built dashboards people could act on

Role-based dashboards with actionable KPIs for each team.

3

Alerted on baselines, not noise

Proactive alerting with baselines flagged issues before users felt them.

4

Shifted the NOC to predictive

The team moved from reactive firefighting to predictive operations.

What changed

From a black box to a clear line of sight.

Before

The doctor felt the delay first.

  • Clinical app performance was a black box.
  • A third of SaaS slowdowns had no root cause.
  • Wi-Fi and LAN issues dragged on.
After, with OnStak

Now the team sees it first.

  • 100% end-to-end visibility, user to data centre.
  • MTTR down 55-70% through proactive alerts.
  • Network escalations down 40%.

The outcome

Anonymized pediatric hospital · clinical apps

The clinical experience, finally in view.

100
End-to-end visibility
User to Wi-Fi to SaaS to data centre.
55-70%
Lower MTTR
Through proactive alerts.
40
Fewer network escalations
Network team escalations reduced.
Minimized
Patient care disruptions
Fewer disruptions to care.

The idea that holds it together

Care cannot wait on a slow app.

“In a hospital, observability is not an IT nicety. It is minutes returned to the people giving care.”

Awais Janjua  /  Chief Technology Officer, OnStak

Built on
Splunk observability platform Endpoint monitoring Synthetic monitoring Role-based dashboards Baseline alerting End-to-end path (user → Wi-Fi → SaaS → DC)

Coffee? No Slides

No pitch deck. No 47-page proposal. Just a straight talk about what’s broken and what to fix first.

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