Staff scheduling tools can improve the bottom line
Predictive analytics provide greater accuracy
The labor force is the largest cost center in the healthcare industry, and organizations are always looking for ways to manage it more effectively and efficiently.
New tools using predictive analytics offer advancements in workforce management that can improve the bottom line and patient care by predetermining appropriate staffing levels.
Omaha, Neb.-based workforce management solutions firm Avantas offers Smart Square scheduling software. Based on the firm’s Predictive Model, the tool was first used in 2007 to forecast future inpatient staffing needs and was expanded to ancillary units such as the operating room, psychiatry, rehab and senior health in 2010.
The model is built with priority forecasting software with algorithms that use multiple indices such as historical census data, the Google flu index, the CDC flu index, daily temperature patterns and regional and local events to determine how much of each type of staff will be needed six weeks in advance.
“What we try to do with the Predictive Model is align the right amount of staff against demand. It’s a more proactive model, which analyzes the end-to-end process. It allows hospitals to schedule four to six weeks out, instead of making last minute decisions,” said Chris Fox, Avantas’ senior vice president.
How successful is Smart Square?
“We find that when you compare the historical volumes and incorporate the ongoing volumes and combine it into a living, breathing forecast, we’ve been able to achieve a 95 percent accuracy rate,” said Fox.
The return on investment is there, too, according to Fox.
“Clients that use predictive modeling have averaged labor savings of $100,000 per nursing unit,” he said.
Chelmsford, Mass.-based workforce management firm Kronos also uses predictive analytics.
“Staffing levels are monitored and managed using census, acuity and current staffing levels to determine what changes in coverage may be needed on the upcoming shift for staffing,” said Brian Graves, global practice leader, healthcare at Kronos. “Beyond the next few shifts, historic and seasonal staffing patterns provide a level of coverage that satisfies most staffing needs, which are then reviewed based on actual volume and acuity to get the most accurate needs by unit.”
Kronos clients have seen major cost savings. For example, Virginia Commonwealth University Medical Center was able to reduce overtime costs by almost 50 percent and its use of an outside staffing agency by 13 percent thanks to the Kronos tool.