Description:

Sustained inequities in the workload distribution can lead to increased stress, reduced job satisfaction, high turnover and shortages in the nursing team. These imply that healthcare quality could also eventually suffer from the imbalances in nursing workload. We develop a data-driven analytical framework to achieve balanced nurse workloads by optimizing the nurse-patient assignment decisions at the beginning of every shift. To this end, we utilize an extensive data set collected by a real-time location system (RTLS) installed in the surgical services department of a tertiary teaching hospital in Montreal. This enabled us to track the care providers as well as the surgical patients through their journey from the emergency department to the operating room, and the surgical ward. The nurse workload is modeled as a multi-attribute, multilinear function, where the significance of each attribute (for the nurse manager) is elicited using an inverse optimization procedure integrated into a clustering method. This involves inverse optimization with a nonlinear mixed integer forward problem, which has not been studied in the literature. The nurse workload balancing problem is then formulated for the upcoming shift, whereby the nurse-patient assignment decisions constitute the primary lever. This requires deploying the proposed dynamic panel-data model to predict each patient’s required direct care. We also robustify the model to incorporate the uncertainties in the attribute weights. Our findings through the real-life case-study are rather encouraging: The mean variation in total direct care is reduced by 46%. Furthermore, the mean of maximum travel distance and the number of assigned patients are shortened by 65% and 31%, respectively.