How Predictive Analytics Can Help Cut Costs

Healthcare providers are using data analytics to forecast staffing requirements, which improves care and reduces costs.

Predictive analytics has helped Vanderbilt University Medical Center identify appropriate operating room staffing levels over the past six years, Vikram Tiwari, the organization’s director of surgical business analytics, recently told HealthTech.

“Every week, there are days that we could have excess staffing, or not enough and have to call in nurses, or the existing staff could stay later for overtime pay,” Tiwari says. “Managing these swings is the objective.”

By leveraging analytics-driven staffing efficiencies, the medical center recouped costs that equate to the salaries of 2.8 anesthesiologists. Knowing more clearly the number of surgeries expected to occur on a given day ensures that operating room teams are scheduled appropriately. 

LEARN MORE: Find out how predictive analytics applications are changing oncology.

When Is the Busiest Time for Surgeries?

Tuesdays and Wednesdays are the busiest days for surgeries; Friday is the least busy. December is a high-volume month because people want to reach their health insurance deductibles, says Tiwari. Pediatric surgeries spike in late summer before school starts.

But trends change over time. Regular use of data analytics can confirm or refute past assumptions to better anticipate patient needs and improve scheduling. “People have perceptions, gut feelings and expert feelings, and the data can validate it,” Tiwari says.

For example, the day after Thanksgiving isn’t a busy surgery day. Last year, however, volume was surprisingly higher than normal. This year, data in hand, the VUMC nursing staff is better prepared for a potentially busy schedule after turkey day.

Thanks to data analytics, they have better insight into staffing needs on any random Tuesday too.

Sam Edwards/Getty Images
Nov 15 2019

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