Outpatient clinic scheduling with limited waiting area capacity

Abstract

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 multiappointment 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.

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

“…To settle for the additional waiting time created by appointment scheduling, the provider will approve the service requirement of arrival time. ey analyzed [153][154][155] capacity allocation and appointment scheduling in the presence of arrival time and developed a connect rule dealing with helping to address decisions. Regarding how many slots to reserve for arriving and scheduled patients, the clinic session was given a fixed daily capacity to reduce missed appointments.…”

Limited waiting areas in outpatient clinics: an intervention to incorporate the effect of bridging times in blueprint schedules

“…The intervention involved an iterative optimisation and simulation approach, based on ILP and MCS, to obtain a blueprint such that the 95% CI of the number of patients in the waiting area did not exceed available capacity. Both the ILP and MCS models are presented in detail in Otten et al 16 and were implemented in Python V.3.9. The ILP was solved using Gurobi V.9.1.0.…”

“…15 Waiting area occupancy has only emerged as a relevant outcome measure in the literature since the COVID-19 pandemic and is therefore understudied. 16 This paper evaluates the impact of an intervention, based on mathematical modelling and computer simulation, in combination with multidisciplinary intervention team meetings, to optimise clinics' blueprint schedules and analyse the impact of these schedules on waiting area occupancy from a patient trajectory perspective. As only part of the in-person appointments may be replaced by digital appointments, 10 the intervention specifically aims to schedule all appointments of the pre-COVID-19 case mix either digitally or in person under distancing measures, maximising the number of in-person appointments.…”

“… 15 Waiting area occupancy has only emerged as a relevant outcome measure in the literature since the COVID-19 pandemic and is therefore understudied. 16 …”

“…Operations research techniques may be used to optimise and prospectively analyse the impact of blueprint schedules on various outcome measures, before actual implementation in practice 15. Waiting area occupancy has only emerged as a relevant outcome measure in the literature since the COVID-19 pandemic and is therefore understudied 16…”