Addressing artificial variability in patient flow

Addressing artificial variability in patient flow is an effective approach to improving accessibility and quality of care and reducing waste and cost in healthcare systems. The most significant artificial variability in patient flow is attributable to dysfunctionally scheduled admissions that can be decreased or eliminated by load smoothing. In this study, we examine the impact of load smoothing of scheduled admissions on patient flow performance metrics of an obstetric unit to provide insights for capacity management. We also investigate the relationship between the impact of load smoothing of scheduled admissions on the patient flow performance metrics of the unit and the volume of unscheduled admissions. In doing so, we develop a detailed discrete-event simulation model of the patient flow of the unit in which patients are categorized into different classes. We compare the results of the simulation model before and after implementing different degrees of load smoothing by considering various ratios of average weekend daily load to average weekday daily load. We determine how different degrees of load smoothing reduce the number of beds required, the average waiting time, and the average probability of delay differently while they have no considerable impact on the average bed occupancy rate. Moreover, considering different volumes of unscheduled admissions, we quantify how the reduction in the average waiting time and the average probability of delay by load smoothing increases when the ratio of unscheduled admissions to scheduled admissions decreases.