Mercy saves $70M+ in premium labor spend with AI-powered workforce optimization
“Mercy saves $70M+ in premium labor spend with AI-powered workforce optimization” documents a Workforce & Staff Scheduling deployment in Hospital & Health System at Mercy. works.ai reports premium labor spend saved (3 years): $70M+; this directory has not independently verified that result.
Evidence at a glance
- Evidence status:
- Automated evidence gate passed
- Deployment timeframe:
- Not reported by source
- Reported outcome metrics:
- 3 cited below
- Directory entry published:
- Source link checked:
The source-link check confirms reachability, not independent re-verification of every claim.
Source-reported figures — cited source: works.ai
The Challenge
Mercy, a 40+ hospital system, faced a pre-existing nursing shortage worsened by the pandemic, leading to over-reliance on costly overtime, agency staff, and incentive spend. Existing procurement processes required ongoing manual interventions that heavily burdened front-line managers. Travel nurses had grown to represent 25% of their workforce, driving unsustainable labor costs.
The Solution
Mercy partnered with Trusted Health to deploy the Works platform, implementing Works Flex for streamlined long-term agency procurement and Works OnDemand for autonomous open shift recruitment. An AI-powered dynamic pricing algorithm identifies optimal incentive levels to balance fill rate and spend. A labor mix optimization engine automatically matches open shifts with best-fit nurses across core staff, float pools, and gig workers across the entire 44-hospital system.
Results
Over three years, Mercy saved more than $70 million in premium labor spend and achieved a 62% reduction in system-wide agency spend and a 60% reduction in traveler utilization. Over 1,000,000 shifts have been filled across the system. RN turnover dropped 8% and staffing office scheduling time was reduced by 20%, while fill rates improved to 8% above goals.
Key Takeaways
- AI-powered dynamic incentive pricing can simultaneously improve fill rates and reduce incentive overspend by recruiting selectively rather than broadcasting maximum rates broadly.
- Unifying all workforce layers (core, float, gig, agency) into a single platform enables labor mix optimization that reduces reliance on the most expensive staffing sources.
- Automating multi-step procurement and scheduling workflows eliminates administrative burden on nurse managers and creates end-to-end analytics visibility for supplier performance management.
Details
- Industry
- Hospital & Health System
- Use Case
- Workforce & Staff Scheduling
- AI Technology
- Machine Learning & Predictive Analytics
- Company Size
- Enterprise
- Company
- Mercy
- Evidence status
- Automated evidence gate passed
- Deployment timeframe
- Not reported by source
- Directory entry published
- Source link checked
Cited source
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