Healthcare technology · Infrastructure reliability

Detecting infrastructure risk before users report it

A proactive monitoring model across servers, application services, databases, capacity and escalation.

MonitoringAlertingRunbooks

servers covered

4 min

detection time (from 22m)

39%

less infrastructure downtime

80%

fewer manual daily checks

In brief

A healthcare technology organisation ran a distributed server estate where many incidents were discovered through user reports rather than monitoring. ClimsTech built an infrastructure inventory, classified workload criticality, introduced monitoring and service checks, calibrated thresholds per workload, and defined escalation runbooks — moving from routine checking to exception-based response.

Working constraints

Mixed operating systems
Distributed server locations
Varying application criticality
Existing support team processes
Need to avoid excessive alerting
Limited downtime windows
Healthcare-related service sensitivity

The problem

What was actually going wrong

The organisation lacked a common view of server health and service availability. Disk exhaustion, stopped processes, resource saturation, and database issues could remain unnoticed until users were affected.

What discovery surfaced

1Some servers had only basic availability checks.
2Application process health was not monitored consistently.
3Thresholds were uniform despite different workload behaviour.
4Alert ownership was unclear.
5Manual checks consumed engineering time.
6Incident history was difficult to analyse.

The engineering

What we built and changed

1Asset and service inventory

Servers, applications, databases, dependencies, and ownership were documented.

2Monitoring baseline

CPU, memory, disk, filesystem, service availability, and selected application processes were monitored across the estate.

3Threshold calibration

Thresholds were adapted by workload rather than applying one universal default.

4Escalation and runbooks

Severity, routing, acknowledgement, and first-response actions were documented for structured incident response.

5Dashboards

A shared operational view showed current health, trend, capacity risk, and unresolved alerts.

Support moved from routine checking and user-led discovery to exception-based monitoring and structured response.

The architecture

Before and after

Before

Distributed server estate
Basic availability checks only
Inconsistent application process monitoring
Uniform alerting thresholds
Unclear alert ownership
Manual daily checks

After

Application servers
Database servers
Integration servers
Monitoring collectors
Central monitoring platform
Operational dashboards
Severity-based alerting
Operations team and runbooks

Judgement calls

Decisions that shaped the outcome

Why monitor services separately from servers?

A server can be available while the application process is unavailable, so server-level checks alone give an incomplete picture of service health.

Why avoid universal thresholds?

Workloads have different normal behaviour; uncalibrated thresholds create false alarms that erode trust in the alerting system.

Why retain informational events without paging?

Operational history is valuable for analysis, but not every event should interrupt engineers — separating information from actionable alerts reduces noise.

Verified outcomes