A simulation study of appointment scheduling in outpatient clinics: Open access and overbooking

Abstract

Patient appointment scheduling (AS) in outpatient clinics is a widely studied subject and plays an important role in facilitating the efficient use of clinical resources and patients' timely access to quality care. This paper considers two AS systems: open access (OA) and overbooking (OB). Clinics make strategic decisions on selecting an AS system and then make tactical decisions on the efficient or optimal use of the system based on the selection. This study proposes some guidelines for the strategic choice of an AS system. For this purpose, we conduct a discrete-event simulation to compare the two AS systems under various environments. We employ four performance measures for the comparison: overtime work, the proportion of unmet demand, in-clinic waiting times, and the use of appointment time slots. For the analysis, we devise an integrated measure representing a linear combination of the four measures. We divide the analysis into two phases. In the first phase, well-performed OA and OB policies are separately identified, and in the second phase, the two scheduling systems with the identified policies are compared. We find overbooking is more robust to various clinic environments and performs better than open access in general. Along with that result, we additionally suggest some rules for determining best open access and overbooking policies.

Maximising Patient Throughput using Discrete-event Simulation

“…Studies include making strategic decisions for various departments (Ballard and Kuhl, 2006;Denton et al, 2006;Vanberkel and Blake, 2007;Leskovar et al, 2011); estimating capacity levels and measuring waiting times (Werker et al, 2009); analysing patient flows (Brenner et al, 2010;Zeng et al, 2012); measuring policy impact (Fletcher et al, 2007); and simulating patient scheduling and utilisations (Harper and Gamlin, 2003;Werker et al, 2009;Lee et al, 2013;Quevedo and Chapilliquén, 2014). It has been argued that the extensive use of process modelling is limited in healthcare compared with other industries (Harper and Pitt, 2004) due to the complexity of the processes and the vast amounts of data required to provide accurate models (Antonacci et al, 2016).…”

“…Those that have used discrete-event simulation to analyse patient scheduling do so in an attempt to resolve issues such as reducing waiting times (Harper and Gamlin, 2003), reduce planning time for schedules (Werker et al, 2009) or compare scheduling models (Lee et al, 2013). With the exception of Lee et al (2013) there are few studies which measure the performance of scheduling models against a range of metrics.…”

“…With the exception of Lee et al (2013) there are few studies which measure the performance of scheduling models against a range of metrics. Typically, studies have focused on one key metric, while Lee et al (2013) evaluated four metrics including clinic overtime, waiting times, unmet demand, and use of appointment slots, but did not measure such metrics as clinic room utilisation or clinician utilisation.…”

“…There have been attempts in the literature to tackle these concerns by accommodating variations into scheduling, with the implementation of decision trees (Huang and Kammerdiner, 2013), or by using discrete-event simulation to compare scheduling techniques (Lee et al, 2013).…”

Modeling and simulating hospital operations in a 3D environment

“…Previous healthcare related dashboards have been developed to analyze bed occupancies (Daley et al 2013), readmission prevention (Stadler et al 2016) and performance management (Mesabbah and Arisha 2016). Other dashboards have been built as Discrete-Event Simulators (DES) to estimate capacity levels (Werker et al 2009), measure policy impact (Fletcher et al 2007), simulate patient scheduling (Lee et al 2013;Werker et al 2009;Quevedo and Chapilliquén 2015;Harper and Gamlin 2003) and aid strategic decision making (Leskovar et al 2011;Vanberkel and Blake 2007;Ballard and Kuhl 2006).…”