Cross-training of nurses during a global pandemic: a two-stage stochastic programming approach

During a global pandemic, hospitals face challenges of uncertain patient influx and increased risk of absenteeism among medical personnel, both of which adversely impact patient safety. To address these challenges, we propose a two-stage stochastic program for nurse staffing that incorporates uncertainties in patient demand and absenteeism. Our model supports two critical staffing decisions to optimize nurse allocation. The first is a tactical decision regarding the number of nurses to be cross-trained, and the second is an operational decision concerning the number of temporary nurses that need to be hired. Applied to data from a Norwegian tertiary public hospital during the first wave of the COVID-19 pandemic, our model identifies a bottleneck in intensive care unit nurse availability. Sensitivity analyses reveal that the effect of increasing the penalty for untreated patients is much larger than the effect of changes in cross-training parameters, such as the number of trainees per mentoring nurse or cross-training cost. Moreover, we highlight that cross-training helps to reduce bottlenecks and improves future service levels. Thus, cross-training remains advantageous overall, despite temporarily reducing nurse availability during the cross-training period. Despite the study's limited scope on a single patient pathway and its focus solely on nurses, it provides valuable insights into nurse staffing strategies for practitioners. To the best of our knowledge, this is the first study to model cross-training as a tactical staffing decision and its implications for workforce availability during the cross-training period, while also accounting for an increased risk of absenteeism.