A lesson in resiliency: How submitting balanced nurse schedules (or not) impacts final fill rates

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. Able to forecast staffing needs months in advance, 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. Data was collected from inpatient nursing units focused only on licensed nursing staff. The study found that while all organizations try to schedule the right amount and type of staff needed for demand, each have their own strategies to get there.

Initial findings are fairly straightforward: poorly submitted schedules tend to result in poor fill rates, more core employees in overtime and extra hours, more cancellations, and more schedule changes.

Less straightforward, the study found that when organizations under-scheduled to the predicted need, some organizations were more resilient than others when it came to the actual worked shift. More resilient clients tend to have good schedule oversight and accountability, high utilization of float pool resources and open shift, and utilize centralized resource management.

Having good schedule oversight means ensuring commitments are consistently met. Staff members should be scheduled to their FTE and working the expected holiday and weekend commitments. Employees who are not scheduled to their FTE – termed “FTE leakage” – creates a unit’s own staffing shortage. These lost hours are costly to the organization and completely preventable with effective oversight.

Organizations who are more resilient to poor scheduling have a high utilization of float pool resources and participation in open shift management programs.

A float pool is designed to flex up and down with patient volume to fill those needs after core staff has met their FTE. Advanced resource pool strategies of today are very different from the recent past, as they have changed dramatically in their design, functionality, and administration, driving down costs and ensuring the flexibility to meet fluctuating patient demand and staff behaviors.

Open shift management programs empower staff to pick up additional shifts that fit their lifestyle. Implementing an open shift program that automatically posts vacancies based on predicted demand beginning a month in advance of a shift solidifies staffing plans sooner.

Many nurse schedulers or managers may think it’s arbitrary to build the best schedule at the time of submission because so much can change between then and when the shift begins, but this research suggests it does matter. Having faith in predictive analytics to accurately predict patient demand is a major factor. Managers must trust the technology to do what it is designed to do. Along with this, having good schedule oversight and ensuring commitments are met, submitting schedules on time, making sure there is a competency mix of resources (charge nurses, experienced RN’s, etc.), and utilizing contingency resources such as float pool are steps to take to ensure a properly balanced schedule.

The full results of this study will be made available Q1 2019.

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