Executive Summary
Accurately predicting patient volumes is critical for efficient healthcare staffing, but it’s a complex challenge. AMN Healthcare, a leading Healthcare Workforce Solutions Company in the industry, faced new operational challenges with their Best in KLAS (2025) predictive scheduling solution, Smart Square®. To address these challenges, they desired to further enhance speed and stability, scalability across solutions, and ultimately maximize the accuracy of its demand predictions.
Despite being a current market leader in demand predictability, they aimed to further refine their capabilities to achieve these comprehensive business objectives. Resolving these inefficiencies would result in more precise staffing, cost savings for clients, and improved patient care in the industry. This case study explores how AI transformed their workforce planning, cutting forecasting time by 90%, IT costs by half, and increasing census predictability accuracy by 15% (RMSE).
Client Profile
AMN Healthcare tackles the multifaceted challenges faced by healthcare organizations with workforce optimization solutions. Their comprehensive total talent service suite encompasses staffing, talent management, and technology solutions.
Business Problem
With hundreds of thousands of healthcare professionals to manage across its large client base, AMN Healthcare’s predictive scheduling solution, Smart Square, needed patient volume predictions for each shift, for each unit, and on a tailored daily cadence for every health system they serve.
Their existing forecasting methods faced several roadblocks:
- Models & Manual inputs that slowed predictions to over 24 hours
- Inflexible algorithms built within an inefficient platform
- Accuracy opportunities, that would result in better staffing, reduced client costs, and better patient care
This bottleneck affected AMN & Client operational efficiency, and overall costs, and Client patient satisfaction.
Elastiq Solution
Elastiq built an ensemble of predictive analytics models, leveraging tools like AWS Step Functions, SageMaker, S3, and PostgreSQL. Key features included:
Seamless Data Integration
Combined historical staffing data, patient volume trends, and shift-specific needs into a unified data platform.
Predictive Analytics Models
Built ML models to forecast patient volume for each department and shift with high accuracy.
Continuous Optimization
Integrated real-time data and back testing to refine model accuracy over time.
Cloud-Based Processing
Deployed parallel ML pipelines in the cloud for rapid, cost-effective forecasting.
Results
The AI solution delivered immediate, measurable benefits for AMN Healthcare. This transformation had a profound impact not just on operations but also on patient satisfaction and care outcomes. Improved staffing efficiency ensured the right staff is available at the right time.
“Elastiq has modernized our approach to predicting healthcare staffing needs. We now operate with greater precision, speed, and at a significantly reduced internal cost. This improvement empowers us to better serve our clients, enabling them to better plan and deliver consistent and effective care to their patients.”
Conclusion
The partnership between Elastiq and AMN Healthcare showcases the power of AI in workforce management. By reducing prediction time and costs while improving operational efficiency, AI empowered AMN Healthcare to deliver better patient care.