Moneyball: Healthcare Organizations Seek the Benefits of Analytics

Fifteen years after its release, Michael Lewis’ wildly popular book, Moneyball, still resonates. The story of how Oakland A’s General Manager Billy Beane and Assistant General Manager and economist Paul DePodesta built a playoff-bound team by valuing statistics over instinct continues to flourish because analytics and advanced statistical methods are more popular than ever in seemingly every industry.

At odds with the traditional, subjective method of scouting players, the solution embraced by Beane and DePodesta was influenced by a school of baseball statistical analysis known as sabermetrics – a type of advanced statistical analysis that crunches data from player performance – which enabled them to see the hidden potential in undervalued players.

It got people thinking, “If analytics can do that for baseball, what can it do for me?”

Healthcare organizations have been the latest to hop on the analytics bandwagon. When used smartly, predictive analytics can improve the accuracy of numerous types of forecasts. And when you’re considering patient care outcomes, you can’t put a value on accurate forecasts.

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Overflowing with data, healthcare organizations are looking to uncover its hidden value. 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.

But data isn’t perfect, and algorithms are not magic. An unbiased view is necessary to filter out the 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 asking a lot to trust staffing predictions when they feel that no one knows their hospital or department better than them.

In Moneyball, the application of sabermetrics 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 and proof that the statistics were reliable.

The key takeaway is that predictive analytics is a solution to be used in combination with extensive knowledge of staffing strategies; it will not solve all of an organization’s problems alone. Experts are needed to routinely monitor the predictive model, and an organization should have a functional leader to make sure the model is being used as intended.

Healthcare organizations may be looking to achieve their own Moneyball effect, but they should be knowledgeable about how the process works to avoid unrealistic expectations. Predictive analytics is only effective if the model is fed reliable data and staff is bought into using the information that it produces. Trusting the predictive model and having guidance on best practice staffing strategies is fundamental to hitting the home run.

Jackie Larson

Jackie Larson
Jackie Larson is president of Avantas, a provider of workforce management technology, services, and strategies for the healthcare industry.

Jackie Larson

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