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On-call Health: identify signs that incident responders are overworked.

43 starsPython

On-Call Health – spot burnout before it hits your engineers

by sylvainkalache·Mar 12, 2026·2 points·0 comments
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AI Analysis

●●SolidSolve My ProblemSlick

PagerDuty and Slack integration tracks incident patterns to flag burnout risk before it escalates.

Strengths
  • •Six devtool integrations collecting objective incident data plus self-reported Slack surveys is comprehensive
  • •OCH Score trend relative to individual baseline avoids one-size-fits-all burnout thresholds
Weaknesses
  • •Engineering analytics for burnout is crowded with LinearB, Haystack, and people analytics platforms
  • •Risk of punitive use despite disclaimers could limit team adoption and honest self-reporting
Category
Productivity
Target Audience

Engineering managers and teams running on-call rotations

Similar To

LinearB · Haystack · Lattice

Post Description

We built On-Call Health because our industry has gotten very good at keeping systems healthy, but does little for the engineers on call managing them.

On-call can be rough: 97% of engineers experience stress during incidents, 87% after, and 56% experience sleep disruption even without getting paged.

We integrate with devtools like Rootly, PagerDuty, Linear, Jira, and Slack (for self-reported data). The tool doesn't provide a diagnosis, but it surfaces signals that a team or individual might be struggling.

It's free to use and open-source (we also have a hosted version with mock data if you want to try it out quickly). Happy to answer questions about the data or how we built it.

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