We built On-Call Health, an open source tool from Rootly AI Labs that helps teams detect early burnout from incident response patterns. It connects to tools engineers already use (Rootly, PagerDuty, GitHub, Slack, Jira, Linear) to surface workload trends that traditional incident dashboards miss.
What we noticed:
-Incident volume alone doesn’t show the full picture. When alerts happen matter as much as how many occur
-After-hours interruptions add up. Late-night pages, weekend work, and repeated disruptions often drive burnout more than raw incident volume.
-Workload snowballs. Incident response happens alongside development work, reviews, tickets, and meetings.
-Look for trends, not only metrics. Changes in workload trends reveal signs of stress before dashboards
What On-Call Health does:
-Detects workload patterns (after-hours, consecutive on call days, uneven incidents, etc.)
-Compute a risk level from incident response data, engineering workloads, and work-pattern signals
-Tracks trends relative to each engineer’s baseline instead of static thresholds
-Slack check ins to combine operational data with how responders feel
The goal is to help teams catch burnout signals early. Try it with mock data here and checkout the blog to learn more:
Website: oncallhealth.ai
GitHub: github.com/Rootly-AI-Labs/On-Call-Health
Blog: dev.to/hamza_2315/on-call-burnout-what-incident-data-doesnt-show-2kap
Would love to hear feedback from the community. Feel free to comment below or open an issue on github!