A simulation study of outpatient scheduling with multiple providers and a single device

Abstract

Effective outpatient appointment scheduling aims at reducing patient waiting time and operational costs, and improving resource utilization, especially given the stochastic nature of patient arrivals. Unlike many western developed countries, China faces challenges due to imperfect appointment systems and ineffective resource allocation. Those challenges lead to long patient waiting times and significant pressure to provide accurate and reliable medical diagnosis that can handle the increasing demand for patient care. In this paper, an outpatient appointment scheduling model for primary care settings was developed. The model was structured for a multiprovider clinic with a single medical device that allows for revisit (i.e., follow-up) procedures. A total of six scheduling rules were designed and tested. Then, sensitivity analysis was performed with respect to two parameters: the percentage of patients with fixed appointments and walk-ins, and the scheduled physician assessment times. The experimental results of the simulation model were used to determine the optimal values for the two parameters.Among the six scheduling rules tested, the one that involves patients with fixed appointments and walk-ins to be served alternatively every hour, led to the shortest waiting time and the highest equipment and staff utilization. In this scenario, patients with fixed appointments are served by Provider 1, while walk-ins are served by Providers 2 and 3. The sensitivity analysis under this rule revealed that when the percentage of patients with fixed appointments and walk-ins was 30-70%, and the scheduled service time was 20 minutes, the total patient waiting time and total resource idle time were minimized.

Using Simulation Optimization to Solve Patient Appointment Scheduling and Examination Room Assignment Problems for Patients Undergoing Ultrasound Examination

“…For this research related to patient appointment scheduling and exam room assignment at a radiological department, discrete event simulation is most suitable because this problem matches the characteristics of discrete event simulation best: (1) complex patient flows (2) transient analysis of system performance, and (3) a scheduling and resource allocation-related problem. Wu et al [ 40 ] studied the minimization of total patient waiting times and total idle time in a scenario where patients waited in line following a first-in-first-out approach to be examined at one of three examination rooms. Chen et al [ 1 ] compared the traditional fixed patient number appointment scheduling strategy and the mixed patient number appointment scheduling strategy.…”

Grid Patient Appointment Template Design to Improve Scheduling Effectiveness

“…One may feel the scheduled appointment time at 'odd' times (such as 8:18 or 8:26) is difficult for patients to follow and for clinics to implement. Actually, clinics could easily round appointment times to the nearest 5-minute interval [11,36] since the effect is minimal [12]. The schedule will then look like 8:00, 8:20, 8:35, 8:55, 9:10, 9:30, 9:50, and so on.…”

“…To help alleviate negative effects from these factors, a number of methods have been used to determine possible solutions, such as Monte Carlo simulation [3], stochastic modeling [4,5], dynamic/deterministic optimization models [6,7], and testing different scenarios for a clinical setting through simulation [8,9]. Solutions that have been studied to improve outpatient delivery systems include scheduling of resources [10,11], studying forecasting models [12], evaluating patients before visits [13], the best sequence for scheduling patients [14], predicting patient show or no-show [15], and adjusting appointment times [16]. One of the solutions that is studied by many researchers is redesigning patient appointment templates, also known as appointment rules.…”

Multi-objective optimisation for constructing cyclic appointment schedules for elective and urgent patients

“…The arrival of the latter patients is then addressed via insertion using a reactive approach that involves rescheduling elective patients (Pham & Klinkert, 2008;Bargetto et al, 2018). There are only a few studies in the domain of appointment scheduling that include strategic and tactical planning decisions related to the allocation of capacity to patient groups (Wiesche et al, 2017;Klassen & Rohleder, 1996;Zhou et al, 2018) and determine the timing of time slots for non-elective patients, i.e., walk-in, urgent and/or emergency patients (van der Lans et al, 2006;Wu et al, 2012;Kortbeek et al, 2014;Wu et al, 2014;Cayirli & Yang, 2014;Morikawa & Takahashi, 2017), which is the subject of this study. The lives of emergency patients are in danger, while those of urgent and walk-in patients are not.…”