Data-driven physician staffing tied to significant savings for health systems, study finds

A data-driven approach to physician staffing across multiple hospitals can significantly cut overtime and idle time, translating into meaningful cost savings for health systems, according to a new study.

In a study published in the February edition of Operations Research, researchers examined a dynamic staffing model implemented at the University of Pittsburgh Medical Center’s anesthesiology department. The model combines historical data with updated short-term forecasts to assign physicians either to specific hospitals or to a shared on-call pool weeks in advance. Those assignments are then adjusted closer to the day of surgery as demand becomes clearer. 

The model also accounts for real-world constraints, including which physicians are credentialed to work at certain facilities, fairness rules for on-call rotations and uncertainty in both projected surgery volumes and the total anesthesia hours ultimately required.

After implementation across 11 hospitals, researchers found the system reduced daily overtime by nearly 13 hours and idle time by 14 hours. Even after factoring in additional pay for on-call coverage, the changes led to net estimated savings of about $2,200 per day, or more than $800,000 annually.

Researchers said the framework could be applied to other areas of clinical staffing facing similar demand variability and workforce constraints, including nurse scheduling.

“Combining an on-call structure with robust, data-driven planning can substantially reduce overtime and idle time,” Kumar Rajaram, PhD, study author and professor at UCLA Anderson School of Management in Los Angeles, said in a news release. “Our approach also demonstrates how fairness constraints, such as ensuring no one is placed on consecutive on-call days, can be integrated without sacrificing efficiency.”

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