Case Study: Gale Healthcare Solutions Improves Shift Fill Rates with Provectus AI/ML

Gale accurately predicts shift fill rates for nurses and caregivers with AI/ML, to improve staffing predictability, increase operational efficiency, and improve care delivery

Gale Healthcare Solutions is a healthcare staffing marketplace that connects facilities with a network of credentialed nurses and caregivers. To improve scheduling predictability, reduce missed shifts, and support better care delivery, Gale needed a machine-learning approach to identify which open shifts were most likely to be filled. Vendor Provectus was brought in to help build this capability on AWS.

Provectus developed an ML solution using Amazon SageMaker that predicts shift fill rates and helps Gale focus outreach on shifts needing attention, while reducing excess notifications to caregivers. Delivered in six weeks, the model achieved 75% accuracy and 85% recall, and the solution contributed to a 7% improvement in shift fill rate. Provectus also transitioned the workflow to SageMaker Pipelines, giving Gale a foundation for future automation, monitoring, and retraining.

Gale Healthcare Solutions