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How Predictive Analytics Works Behind the Scenes to Fill Open Shifts and Boost Patient Care

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By Jackie Larson, President, Avantashealthcare blog jackie larson avantas president

Walking into a hospital, the use of technology can be seen in nearly every turn — from surgical instruments in an OR to monitors beeping by a patient’s bedside.

But, you can’t always see the most sophisticated technology. Data-driven, technology-enabled solutions for staffing and scheduling are gaining traction in healthcare to ensure the right clinician is at a patient’s bedside at the right time. Predictive analytics in healthcare automates the scheduling process by forecasting needs up to 120 days before the shift. This forecast is updated in real-time, and is 97% accurate by 30 days prior to the start of the shift.

You can’t see predictive analytics models for healthcare. But you can see the results.

Nurse managers can spend a lot of time scheduling their staff. If using manual tools such as paper-based methods or spreadsheets, there may be a lot of guesswork involved when trying to schedule staff a month or two in advance. Automated scheduling using predictive analytics greatly reduces uncertainty by aligning the right number and types of staff with patient volume forecasts. Data-driven solutions reduce the stress of scheduling for nurse managers — and deliver back valuable time to focus on patient care and supporting their staff.

The use of predictive analytics to forecast staffing needs also 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, nurse managers are often forced into bargaining and making frantic recruitment calls at the last minute. Using an open shift program through an automated scheduling system takes this frustrating process off of the manager’s shoulders.

Instead of gift cards and cash tucked away in a manager’s drawer, ready to offer to staff for picking up a shift at the last minute, incentives can be offered through the open shift program and aligned with budgeted bonus targets that correspond to the severity of the need.

For example, incentives peak at 30 days before the shift, then decline in dollar amounts as shifts are picked up and the date of the shift approaches. This proactive approach rewards staff for picking up shifts well in advance, thereby solidifying staffing plans sooner. This practice significantly reduces stress levels for everyone involved.

Many organizations are used to an open shift practice in which staff hold out on picking up shifts so that increasing incentive dollars — bonus, double bonus, etc. — will be offered by managers. Last-minute open shift chaos is actually incentivized!

In contrast, a proactive strategy incentivizes clinicians who are more money-motivated to pick up open shifts in advance. Then, staff members who are more schedule-motivated will pick up the remaining shifts that best fit their schedules. This provides a winning solution for staff, managers — and patients. Scheduling chaos can undermine the work environment, which studies show impairs the patient experience.

Predictive analytics demand forecasting is not a shiny piece of advanced high-tech equipment that amazes patients. Instead, it’s a practically invisible computer-enabled scheduling tool that works behind the scenes to ensure consistent high-quality care for patients.

Related Articles:

Predictive Analytics in Healthcare On-Demand Webinar

For Nurses, Compassionate Scheduling through Predictive Analytics  

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