NEW YORK, NY — As hospitals across the U.S. face rising labor costs, persistent nurse shortages, and growing pressure to improve access to care, new research from Columbia Business School finds that predictive, data-informed staffing models can help emergency departments better match workforce levels to real-time patient demand—reducing costs while maintaining quality of care. The findings come at a pivotal moment for the healthcare sector, as health systems are exploring how artificial intelligence and advanced analytics can support operational decision making, particularly in managing workforce constraints, improving efficiency, and stabilizing margins, all while facing heightened expectations from patients and policymakers.
In the paper, Implementing a Prediction-Driven Framework for Emergency Department Nurse Staffing to Optimize Real-Time Decisions, Columbia Business School Professors Jing Dong and Carri W. Chan, together with Columbia Business School Ph.D. graduate Yue Hu, Assistant Professor of Operations, Information and Technology at Stanford Business School, and co-authors Alice Kazekjian, Chayapol Ophaswongse, Gregory Sugalski, Joseph P. Underwood, and Rimma Perotte of Hackensack University Medical Center, found that using predictive analytics to guide nurse scheduling reduced reliance on surge staffing and travel nurses while improving the alignment between staffing levels and patient demand, enabling emergency departments to lower labor costs without meaningful changes in wait times, treatment duration, or patient flow. The approach reduced hourly staffing costs by more than $160, translating to roughly $1.4 million in annual savings for a single emergency department.
“Healthcare leaders are under enormous pressure to do more with fewer resources,” said Jing Dong, the DeRosa Family Associate Professor of Business at Columbia Business School. “Our research shows how AI combined with mathematical modeling and optimization can support operational decisions in real time. As hospitals face ongoing workforce shortages and cost pressures, incorporating AI tools into everyday workflows has the potential to improve efficiency and expand access to care.”
To conduct the study, the researchers tested a prediction-driven nurse staffing approach that uses data and real-time information to help hospital leaders anticipate patient demand and adjust staffing accordingly. They implemented the system in the emergency department of a large academic medical center, serving roughly 90,000 patients each year. The model first projected baseline staffing needs weeks in advance using historical patterns, then updated those forecasts closer to each shift using more timely operational data to guide decisions about whether to bring in additional nurses.
The team piloted the approach over several months and compared performance before and after implementation using electronic health records and payroll data. They found that the system reduced hourly nursing labor costs by more than $160—equivalent to about $1.4 million in annual savings for a single emergency department—while maintaining stable wait times, treatment duration, and patient flow. The approach also reduced reliance on travel nurses and high-cost surge staffing, suggesting a more sustainable workforce strategy.
Importantly, the study also found that even small staffing gaps had measurable consequences for patients. When staffing fell below recommended levels, wait times increased, with each missing nurse per hour associated with a roughly 2-minute delay. Larger shortfalls led to even greater disruptions, showing the value of proactive, data-driven staffing in improving patient access and operational performance.
Key findings from the research include:
- Data-driven staffing can lower costs without reducing quality: Using predictive analytics to guide nurse scheduling reduced labor costs while maintaining stable wait times, treatment duration, and patient flow.
- Better matching staff to patient demand improves efficiency and workforce stability: The approach helped emergency departments more closely align staffing with real-time patient volume, reduce reliance on travel and surge nurses, and strengthen the use of core staff.
- Accurate staffing is critical for patient access: Even small staffing shortfalls lead to longer wait times, underscoring the importance of proactive workforce planning to maintain timely care and avoid disruptions.
“This study shows how predictive analytics can help hospitals make smarter staffing decisions,” said Carri Chan, the Cain Brothers and Company Professor of Healthcare Management and Faculty Director of the Healthcare and Pharmaceutical Management Program at Columbia Business School. “Scaling these approaches could improve efficiency, reduce workforce strain, and expand access to care, especially as health systems face ongoing staffing and cost pressures.”
This research is part of Columbia Business School’s broader efforts to advance applied artificial intelligence in healthcare safety and translate emerging technologies into real-world impact. Through its AI+Healthcare initiative, the School is focused on how data and AI can improve patient care, strengthen health systems, and expand access to care. To learn more, visit https://business.columbia.edu/ai-healthcare.
