Predictive analytics delivers time back to managers to focus on patient care

When an organization overlooks staffing and scheduling habits it has a negative impact on staff morale, turnover, and patient care – all of which effect an organization’s bottom line.

No organization purposefully overlooks workforce management, but in practice, it very often does not get the continual attention it deserves. With increasing patient demand, an organization must find efficiencies to keep up with demand while staying focused on delivering quality patient care.

Staffing and scheduling in patient care facilities is a complicated process. Taking into account fluctuating patient volumes, scheduling staff to patient demand takes considerable thought. Nurse managers can spend a lot of time building unit schedules. Depending on the type of scheduling tools used, there may be a lot of guesswork involved when scheduling staff 4, 6 or 8 weeks out.

Inaccurate projections of staffing needs can lead to understaffed shifts, increased floating and cancellations, excessive overtime and incentive pay, last-minute schedule changes, and other issues that stress staff, raise costs, and impact the quality of patient care.

Automated scheduling software fueled by predictive analytics can greatly diminish the level of uncertainty when aligning the right number and types of staff with predicted volume. Data-driven solutions reduce the stress of scheduling for a manager, and delivers valuable time back in their day to focus on patient care and supporting their staff.

This best-practice approach to forecasting results in accurate predictions of staffing outcomes 30 days before the start of the shift. Healthcare enterprises that have adopted predictive analytics and advanced labor management strategies have realized outcomes that include reductions in agency nursing, increased staff satisfaction scores, improved nurse retention, and overall nurse labor spending reductions of 4 to 7 percent.

The University of Kansas Hospital Time Study
Interested in understanding how much time was being devoted to scheduling, staffing and payroll functions, The University of Kansas Hospital conducted a study in 2015 prior to implementing a scheduling software powered by predictive analytics. The findings revealed that nurse managers or schedulers were spending an average of 13.4 hours per person each four week schedule period to create the initial schedule. Post-creation schedule tasks – such as entering schedule adjustments like PTO and absences and correcting missed punches – were costing them an additional 40.3 hours, with over half of that being spent on daily staffing activities within 24 hours of the start of the shift. All scheduling tasks added up to more than 1,500 hours for all inpatient units each schedule period.

A year and half after going live with new scheduling software, they took another look at how much time was being spent on scheduling and staffing. The time savings were significant. The time each manager spent on schedule creation was reduced by more than 50 percent, averaging 6.6 hours. The post-creation scheduling outcomes were even more remarkable, averaging 4.4 hours. The average total hour savings per manager per schedule period added up to 61.1. This is a substantial amount of time that was given back to managers to apply toward more pressing clinical responsibilities.

Combining their utilization of predictive analytics with advanced labor management strategies, The University of Kansas Hospital was also able to reduce their instances of core staff in extra hours from 32.7 percent to 4.77, and overtime from 14.6 percent to 6.37. They have also achieved their lowest turnover rate in recorded history.

Scheduling staff can be a frustrating and often complicated task for managers to tackle. As The University of Kansas Hospital demonstrates, without effective scheduling tools that accurately forecast staffing needs well in advance, managers can spend a lot of time dedicated to scheduling tasks. But with scheduling software led by accurate forecasting, significant time-savings opportunities are possible.

Reaching economies of scale, predictive analytics delivers more than financial savings for a provider organization. Returning valuable time back to nurse managers means they can focus more attention on patient care and staff development. Streamlining the scheduling process with accurate forecasting reaps the greatest benefit that everyone pines for – time.

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