Case Study: Cerner achieves faster Mean Time to Knowledge (MTTK) and improved uptime with Elastic

Cerner Powering the Search for Mean Time to Knowledge (MTTK)

Cerner, a leader at the intersection of healthcare and IT serving more than 27,000 contracted provider facilities and hundreds of thousands of clinicians, faced a critical challenge: monitoring a vast, mission‑critical infrastructure that generates terabytes of monitoring data and billions of syslogs from thousands of devices. Rampant alerts and a “needle in a haystack” problem made it slow and costly to get to root cause knowledge, so Cerner redefined their metric to Mean Time to Knowledge (MTTK) to focus on rapid, shareable insight rather than prolonged investigation.

To solve this, Cerner deployed the Elastic Stack (Elasticsearch, Kibana, Metricbeat, machine learning) alongside OpenNMS, ingesting high‑resolution and threshold feeds into real‑time analytics and dashboards, tagging events by client and infrastructure layer, and using ML to surface anomalies. The result: MTTK dropped from hours to minutes (near real‑time), incident counts fell, uptime and client satisfaction improved, and engineers were freed to focus on strategic work while the platform scaled to meet explosive data growth.

Cerner

Chris Asby

Vice President of Infrastructure and Operations