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Burnout Risk Index

The Burnout Risk Index (BRI) is a composite operational metric that combines four workforce signals into a single 0–100 score per unit. It is designed to surface units where compounding pressures suggest elevated workforce stress risk — before those pressures result in turnover or patient care impact.
The BRI is an operational metric derived from workforce data. It is correlated with workforce stress risk but is not a clinical measure of employee wellbeing or burnout. It should be used to prioritize operational attention, not to make clinical determinations about individual staff members.

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Formula

Each component score is normalized on a 0–100 scale based on configured thresholds.

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Component weights

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Scoring scale

Each component is scored 0–100 using linear interpolation between the following anchor points: Thresholds are configured in Alert Rules. The default values are set to healthcare industry benchmarks but can be adjusted for your organization.

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Risk levels

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Trend interpretation

Each unit’s BRI is compared to its previous period to determine trend:
  • Improving — BRI decreased by more than 3 points
  • Stable — BRI changed by 3 points or less in either direction
  • Worsening — BRI increased by more than 3 points

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Dominant factor

Each unit’s BRI shows its dominant factor — the component contributing most to the overall score. This tells you where to focus: a unit with a high BRI driven by absence requires a different response than one driven by overtime.

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Org-level BRI

The organization-level BRI is a headcount-weighted average of all unit BRIs. It provides a system-wide stress signal but should always be interpreted alongside the unit-level breakdown — a moderate org-level score can mask a high-risk unit that needs immediate attention.

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Limitations

  • The BRI does not disaggregate stress causes — it identifies signal clusters, not root causes
  • Units with very small headcounts may show volatile scores due to statistical sensitivity
  • The BRI is only as reliable as the data uploaded — data quality directly affects output quality
  • Geographic and seasonal factors are not accounted for in the base model

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See also