Simulation analysis of a neonatal unit with complex patient flow patterns – an enhanced model for capacity planning (inpress)

Abstract

Steadily increasing demand requires neonatal units and networks to improve their overall capacities. Given the operational complexities involved, simulation is a popular choice for modelling patient flow and analysing its impact on resource capacities. Clinical pathways, designed to reduce variation of care and improve the quality of care for a specific group of patients, broadly define patient flow patterns. The literature points to many simulation studies where the interactions between clinical pathways and resource planning have been addressed. For efficient model building, these simulation studies have, however. assumed a unidirectional flow of patients, i.e., progressively moving to lower levels of care. Patient flows are, however, much more complex. In some instances, patients may require a higher level of care than the current level of care. In such cases, bi-directional flows are created. This paper explores the impact of bidirectional flows on capacity planning. Using a real-world neonatal unit as an example, two scenarios of patient flow, i.e., unidirectional and bidirectional, are modelled and extensively analysed. This study revealed that the bidirectional flow model, which is the more realistic model, produces significantly different capacity planning estimates. For example, the number of admission requests rejected by the unit increased by 5-7 times, i.e., the uni-directional model significantly underestimates the overall capacity. The bidirectional model also revealed that there is a need to double the number of beds required for high-level care, and bed utilisation, in general, is higher than the estimates produced by the unidirectional model. Given that there is a need to generate accurate capacity estimates to ensure better services for patients and minimise regular changes due to poor capacity estimates, this paper argues that bidirectional modelling should be used to produce more accurate capacity estimates.