Big data and analytics is a growing business intelligence trend in nearly every industry—including, most recently, health care. Provider organizations have a plethora of data that can be harnessed to reveal valuable insights to drive improvements in patient care. The key to unlocking meaningful information is through analytics.
Although big data is a hot topic, many people are still not sure about what it is or how it works. Let’s take a look at Michael Lewis’ popular book, “Moneyball,” to break down the benefits of analytics and how it can be applied to health care.
Accurately Forecasting Staffing Needs
It’s been 15 years since Lewis released “Moneyball,” and yet it is still as relevant and buzzworthy as ever. The groundbreaking analytics used to shake up the sentimental game of baseball offers a glimpse of what predictive analytics can do for provider organizations. 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.
In Lewis’ book, the romanticized 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 struck a chord with the masses. It was an inspiring underdog story that allowed the A’s to compete against big-budget teams by using statistics in a way that had not been done before in the game of baseball.
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 allowed them to see the hidden potential in undervalued players.
While advanced analytics are weaving their way into many areas of health care, 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 for cautious-prone individuals working in health care, embracing a new technology takes time and proof that it produces reliable information. But algorithms are not magic. It takes accurate data being fed into the predictive model to churn out accurate forecasts. This requires an unbiased view to filter out emotional responses to the data and a good amount of trust—which is a lot to ask when managers feel no one knows their department better than them.
Combination Effort
As “Moneyball” pointed out, 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.
Predictive analytics will not solve all of an organization’s problems alone. It is a solution to be used in combination with extensive knowledge of staffing strategies. Experts are needed to routinely monitor the predictive model, and provider organizations should have a strong core team to make sure the model is being used as intended.
Being knowledgeable about big data and analytics gives an organization solid footing on what outcomes they can expect. Provider organizations that have used predictive analytics for scheduling and staffing have achieved outcomes that include increased staff satisfaction scores, improved nurse retention, reductions in their annual labor spending, and a decrease in the amount of time managers spend on schedule creation and staffing tasks—which delivers valuable time back to managers to focus on patient care and staff development.
Jackie Larson is the president of Avantas, a company that leverages evidence-based solutions to help health-care providers better manage their workforce. She is a staffing and workforce management guru, passionate about helping health-care organizations automate and streamline their health-care labor management strategy.
