A Dynamic Staffing & Scheduling Solution: The Build and Implementation of a Logistics Engine to Optimize Nurse Schedules and Rosters

Abstract

The use of electronic scheduling and staffing tools should be standard practice in health care. Gone are the days of paper scheduling, replaced with mobile apps and sleek desktop applications. The frontline clinician has access to scheduling, paid time off (PTO), trades, and timecard management in the palm of their hand. Ease of use and work flexibility are top of mind for many frontline clinicians and leaders. Yet even with the newest technology and sleekest design, there continues to be a gap in functionality and practice: predictive scheduling. The tools facilitate the management of actions such as balancing a schedule, automating trades, simple approval of time off, and clean record keeping, but have limited decision support functionality to accurately predict how many nurses should be scheduled to match projected demand, or how many nurses should be hired to meet the schedules modeled to match that projected demand. Therefore, nurse managers and leaders spend hours trying to figure out how many nurses are needed on a schedule that would require as little rebalancing as possible for the actual shift in hopes of limiting the chance of cancelations or needing to expensively recruit for unfilled shifts with incentive pay or overtime. The need for decision support tools for predictive scheduling is paramount and critical for effective labor management. To overcome the gap in technology and capability, St. Luke’s Health System designed, built, and implemented their own logistics engine to move from reactive nurse staffing to proactive nurse scheduling, reaping the rewards of managerial time saved, controlled labor cost, a more predictable roster, and a more satisfied nursing workforce.