This paper proposes an iterative simulation optimisation approach to maximise the number of in-person consultations in the blueprint schedule of a clinic facing same-day multi-appointment patient trajectories and restrictions on the number of patients simultaneously allowed in the waiting area, taking into account the combined effects of early arrival times (patients arriving early from home), bridging times (minimum time required between appointments) and waiting times (due to randomness in patient arrivals and provider punctuality). Our approach combines an Integer Linear Program (ILP) that maximises the number of in-person consultations considering the effect of average early arrival and bridging times and a Monte Carlo simulation (MCS) model to include the effect of waiting times due to randomness. We iteratively adapt our parameters in the ILP until the MCS model returns a 95% confidence interval of the number of patients in the waiting area that does not exceed its capacity. Our results reveal the impact of early arrival, bridging and waiting times on the number of in-person appointments that may be included in a blueprint schedule. Our results further show that careful design of the blueprint schedule allows our case study clinics to organise a vast majority of their appointments in-person.
- Health services
- Mathematical programming