Mercy

Mercy, a 50-hospital health system in St. Louis, faced a nursing workforce crisis driven by pandemic burnout, changing generational preferences, and a heavy dependence on expensive agency/travel nurses (25% of its staffing mix). Nurses lacked meaningful scheduling flexibility, fueling dissatisfaction and exits. Existing incentive models and staffing approaches failed to optimize the workforce or reduce reliance on external labor.

Mercy partnered with Works & Trusted Health to deploy an AI-powered workforce management platform combining Works Flex (a VMS tool for streamlining agency procurement and manager workflows) and Works OnDemand (autonomous open-shift recruitment). The platform uses an AI engine and proprietary behavioral shift-pricing algorithm to predict scheduling gaps, match available clinicians to open shifts based on preferences and experience, and apply dynamic incentive premiums—minimizing overspend while maximizing fill rates.

In 2023 Mercy realized $30.7 million in savings from reductions in premium labor spend by cutting agency staff from 25% to just 8% of the workforce while growing flexible/gig staff from 8% to 23%. Administrative burden fell 20%, shift fill rate rose from 83% to 86%, and overall nurse turnover dropped 8% (including a 9% reduction in first-year turnover).

• AI-driven dynamic shift pricing can eliminate overspending on agency labor while still ensuring open shifts are filled.
• Giving nurses self-scheduling flexibility reduces burnout and turnover as effectively—or more so—than pay incentives alone.
• Continuous daily monitoring and iterative adjustment of the staffing model are required to sustain savings at scale across a multi-hospital system.