In many hospitals across the country, nurse scheduling is still being done on basic spreadsheets or even pen and paper, with managers using little more than intuition, guesswork, or simply the previous schedule period to plan the appropriate number and type of staff to match patient volume. In a time of innovative technology, the complicated task of creating a nursing schedule continues to steal valuable time from nurse managers, cause frustration, and create last-minute staffing chaos.
There is a variety of scheduling software on the market that is designed to improve the process, but the truth is not all are created equal. Many fall short of expectations, forcing users to find workarounds, defeating its intended purpose. When searching for healthcare staff scheduling software, there are three things to know:
1. Predictive analytics in healthcare is improving the accuracy of staff scheduling
Predictive analytics has seemingly only begun its expansion into healthcare after being used successfully in other industries such as retail and transportation for over a decade. Predictive analytics has snuck quietly into nurse scheduling software without a lot of healthcare professionals even knowing the technology exists.
Automated scheduling software powered by predictive analytics offers optimized staffing across a health system by forecasting patient demand weeks (and months) in advance. Analyzing historical census data and other metrics for each unit, accurate forecasts help ensure the right type of provider is in the right place at the right time. This technology offers an array of configuration abilities to suit each organization’s need, including self-scheduling and open shift management, ultimately securing staffing needs weeks in advance of a shift.
2. Predictive analytics software is only as good as organization-specific data and user engagement
Predictive analytics technology is not magic. You can’t plug it in, press a button and expect perfection. Nurse scheduling software that is fueled by predictive analytics is only as good as the data it is fed and the people who use it. Building accurate predictive models relies on reliable data from each organization. The more accurate data that is fed into the software, the better the prediction will be.
This requires committed engagement by nurse managers and functional users. If adjustments are being made, those must be entered into the software. Nurse scheduling software powered by predictive analytics isn’t a static technology. Rather, it’s an interactive technology that is constantly updating and improving to deliver the most accurate staffing prediction. A provider organization and a vendor must partner and communicate about what’s happening on each and every unit to ensure that the predictions are accurate, and become even more accurate over time.
3. Predictive analytics sets the stage for an effective open shift program
A tremendous amount of waste, in both time and dollars, is tied to traditional processes of filling open shifts. But it doesn’t have to be that way. Optimal staff scheduling software will have the ability to manage an effective open shift program that successfully fills open shifts in a fair, cost-effective, and standardized manner.
The utilization of predictive analytics to forecast staffing needs sets the stage for an effective open shift program that rewards staff for picking up shifts several weeks in advance. When it comes to filling open shifts, many nurse managers have to resort to bargaining and making frantic, last-minute recruitment calls to fill their critical needs.
Using an open shift program through an automated scheduling system takes this frustrating process off of the manager’s plate. Once all staff is scheduled to their commitments, open shifts are automatically posted to individuals approved to pick up the shifts, based on their certifications and skills profile. Staffing needs change as volume projections rise and fall and more shifts become available or are picked up.
If an organization chooses to incentivize staff for picking up open shifts, incentives can be offered through the open shift program and aligned with budgeted bonus targets that correspond to the severity of the need. In this scenario, incentives would peak at 30 days before the shift, then decline in terms of dollars as shifts are picked up and the date of the shift approaches. This proactive approach to filling open needs rewards staff for picking up shifts well in advance, thereby solidifying staffing plans sooner.
When weighing the options of nurse scheduling software, it is beneficial to know about the tremendous opportunities that predictive analytics offers. Going beyond simply automating the scheduling process, provider organizations that have leveraged 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 decreased time managers spend on schedule creation and staffing tasks – delivering valuable time back to focus on staff development and patient care.