Minimizing Total Cost in Scheduling Outpatient Appointments

Abstract

This paper considers various rules for scheduling appointments for medical clinic outpatients and investigates their ability to minimize a weighted sum of medical personnel's and patients' idle-time costs. It is shown that the idle times incurred by any given rule are affected by the following three "environmental factors" (in decreasing order of importance): the probability of no-show, the coefficient of variation of service times, and the number of patients per clinical session. Theoretically, an appropriate scheduling rule can be identified only if one knows the values of these parameters and the ratio between the medical personnel's and patients' idle-time costs. Under environments characterized by 27 different combinations of the three environmental factors, the performance of nine scheduling rules are evaluated using simulation. Some of the rules evaluated are original to this study. The results are presented in the form of "efficient frontiers," together with a simple procedure for identifying the best scheduling rule for given environmental-parameter values. This rule-identification procedure is shown to be easily adaptable for circumstances with limited knowledge about the environmental factors; it also reveals that the simple Bailey-Welch individual-appointment rules are surprisingly robust.scheduling, health care, simulation

Scheduling rules to achieve lead-time targets in outpatient appointment systems

“…Many subsequent studies have addressed the appointment scheduling problem to derive an appointment scheduling rule or to evaluate various proposed appointment policies. An evaluation of the different appointment rules under various scenarios has been conducted in [3,12,16,18,25,28,29]. Furthermore, the development of an appointment scheduling policy to minimize some objective function was examined in [4,6,10,11,20,22,26,27,31].…”

“…Su and Shih [25] paid extra attention to patients' throughput time and a utilization rate of the service provider. Ho and Lau [12], Klassen and Rohleder [16], Su and Shih [25], and LaGanga and Lawrence [18] assume that patients show up on time. Blanco White and Pike [3], and Vissers [28] considered the lateness of patients while Vissers and Wijngaard [29] allow for early arrivals of patients.…”

Genetic Algorithm–Based Outpatient Appointment Scheduling Systems

“…Each patient j after the first (j > 1) will have a service start time that is the maximum of their appointment time and the service end time of the previous patient. Therefore, we have bj = max(tj, ej-1) and ej = bj + sj (Ho and Lau, 1992).…”

“…We can find the waiting time of patient j, wj, by considering the patient's service start time bj minus the patient's appointment time tj; hence, wj = bj -tj, where w1 = 0 (Ho and Lau, 1992).…”

Adaptive resource allocation for efficient patient scheduling

“…We have partly based our scheduling approach on insights from scheduling theory, specifically scheduling problems with objectives related to MSL. Furthermore, the scheduling method can be optimized for other considerations such as minimizing doctor and patient idle time during the execution of a schedule [19,20]. Additionally, optimizing the logistical process in hospital practice can also be largely beneficial for resource efficiency [5,21].…”