Mercy saves $30 million in 2023 with AI-powered nursing workforce management tech

Mercy saves $30 million in 2023 with AI-powered nursing workforce management tech

Before deploying the technology, staff was 67% core staff, 8% flexible staff and 25% agency staff. Afterward, the mix became 69% core, 23% flexible and gig staff, and only 8% agency staff.

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A recent study by Works & Trusted Health highlighted the importance of providing flexibility in work arrangements for nurses to increase job satisfaction and mental well-being. The need to schedule life four to six weeks in advance was cited as a significant source of dissatisfaction among 24% of nurses surveyed.

The nursing workforce has experienced a notable decline, particularly among nurses under the age of 35, attributed to various factors including an aging patient population, residual burnout from the COVID-19 pandemic, and changing workforce preferences.

To address these challenges, Mercy, a 50-hospital health system based in St. Louis, Missouri, collaborated with Works & Trusted Health to improve its staffing model. Mercy sought to offer more flexible work opportunities to nurses, reduce reliance on external staffing sources, and enhance administrative efficiencies.

Works & Trusted Health provided Mercy with a technology platform to enable nurses to self-schedule their shifts seamlessly through a user-friendly app. The platform also offered data and analytics to optimize care delivery and manage clinical labor effectively.

By leveraging artificial intelligence (AI) and behavioral shift pricing algorithms, Mercy was able to identify available clinicians, balance-fill shift matches, and minimize reliance on external staffing agencies. The result was improved operations efficiency, reduced labor costs, and decreased turnover.

Mercy's success was attributed to its commitment to monitoring data and outcomes regularly, making continuous tweaks to sustain success. The advice for other healthcare leaders emphasized the importance of automation and continuous analysis in managing workforce staffing effectively across multiple locations.

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