Deaconess Health System cuts premium labor spend 14% with Works AI-powered dynamic pricing

Deaconess Health System deployed Machine Learning & Predictive Analytics for Workforce & Staff Scheduling in Hospital & Health System. As reported by www.works.ai: 14% premium spend reduction.

Maintained by Peter Korpak, Lead EditorHow evidence is checked
14%Premium Spend Reduction
2.5xRevenue Growth YoY (Works platform)

Source-reported figures (cited source: www.works.ai)

What Deaconess Health System was trying to fix

Deaconess Health System struggled with high incentive costs and over-reliance on expensive contingent labor. Manual contract management, credentialing, and recruiting tasks consumed significant operational resources, limiting the ability of internal core staff to pick up available shifts.

What Deaconess Health System deployed

Deaconess implemented Works, a workforce management platform featuring an AI-powered dynamic pricing algorithm that adjusts hourly shift rates based on market demand and nurse availability. The platform automates contract management, credentialing, and recruiting tasks, while giving nurses mobile visibility into available shifts with intelligently calculated incentives.

Results

Deaconess achieved a 14% reduction in premium spend by automating manual staffing tasks and enabling internal core staff to pick up more shifts. The platform gave nurses greater flexibility and ownership over their schedules, improving shift pickup rates and operational efficiency.

Key Takeaways

  • AI-powered dynamic pricing can meaningfully reduce contingent labor costs by matching incentives to actual demand signals rather than flat rate premiums.
  • Automating credentialing and contract management unlocks internal staff capacity that was previously absorbed by administrative burden.
  • Giving nurses schedule flexibility and mobile shift visibility improves pickup rates and retention simultaneously.

Evidence for Deaconess Health System's Workforce & Staff Scheduling deployment

Reported outcome metrics
2 cited below
Cited source
www.works.ai
Last updated
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