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Back in 2011, Brad Pitt and Jonah Hill hit the big screen to bring Michael Lewis’ 2003 underdog story of the Oakland A’s to life. Moneyball debuted the application of big data analysis to the sentimental game of baseball. Where once was a game built on a romantic view of skill and big hitters was transforming into a calculated statistical examination.

Moneyball opened people’s eyes to the possibilities and valuable insights that were waiting to be unlocked by predictive analytics, sparking an obsession with data that has expanded to many industries.

Big data and analytics has been rapidly expanding in the healthcare market. Provider organizations can utilize data analytics for a variety of solutions, improving the delivery of patient care and streamlining efficiencies.

But while advanced analytics are seeping their way into many areas of healthcare, one that remains untapped is in accurate forecasts of staffing needs. Using historical census data, predictive analytics can help improve staffing problems by accurately aligning staff to meet patient demand weeks in advance of a shift.

Using time series analysis, predictive models are created and validated and continually refined based on what actually happened to adjust to projections going forward. Within 60 days in advance of a shift, the prediction can get within one staff member of what is actually needed 96 percent of the time.

PREMIUM CONTENT: US Staffing Industry Pulse Survey Report: September 2020 Selected Highlights

But data isn’t perfect, and algorithms are not magic. It requires a clear-eyed view to filter out emotional responses to the data to avoid errors.

This requires a good amount of trust. For the cautious, control-prone individuals working in healthcare, it’s often a big leap to trust staffing predictions when they feel that no one knows their hospital or department better than them.

Predictive analytics is a tool to be used in combination with extensive knowledge of staffing strategies; it will not solve all of an organization’s problems alone. You need experts to routinely monitor the predictive model and functional leaders within the healthcare organization to make sure the model is being applied as intended.

In Moneyball, the application of data analytics didn’t replace the need for scouts, coaches and good, old-fashioned effort. It is simply a tool for recruiters to use to gain a more objective view of players. And most didn’t accept the new statistical method right away. It took time to prove that the statistics were reliable.

Organizations may be looking to achieve their own Moneyball effect, but they should be knowledgeable about how the process actually works to avoid unrealistic expectations.

Predictive analytics is only effective if key stakeholders feed the model reliable data and buy into using the information that it produces. People must trust the predictive model in order to see success. Finding the right partner to provide guidance on predictive analytics and staffing strategies is fundamental to hitting the home run.