November 1, 2018 – Building nurse schedules is a complex and time-consuming process.
With a lot of moving parts and circumstances to take into consideration, determining the right amount and types of staff needed can change up until the start of a shift.
Many organizations have turned to advanced technology to improve and streamline the process. Predictive analytics has found a place in healthcare, and it can greatly improve the accuracy of nurse scheduling if it is used correctly. Using modern modeling techniques and machine learning, predictive analytics can clearly identify demand versus scheduled staff. Predictive analytics can forecast the need within one staff member of what is actually needed 96% of the time by 30 days out from the start of the shift.
With such dynamic technology, one may assume staffing challenges would be few and far between. However, predictive analytics is not magic or a “plug-and-play” solution. Building the best schedule takes strategy, and any schedule balancing must include faith in the predictions at the time of schedule creation.
In 2018, Avantas conducted a study of three healthcare clients to analyze the outcomes of balanced scheduling at the time of schedule submission.
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