Appointment Scheduling Rules Research Articles

A pull-strategy for the appointment scheduling of surgical patients in a hospital-integrated facility

ABSTRACT This paper addresses the daily appointment scheduling (AS) of patients in a hospital-integrated facility where outpatients and inpatients are treated simultaneously and share critical resources. We propose a lean approach based on the pull-strategy “Constant Work in Process” (ConWIP) to develop robust and easy-to-implement AS rules. Our objective is to reduce patients’ waiting time and maximise the use rate of resources while considering the global surgical process and stochastic service times. The AS rules based on ConWIP are evaluated using a Discrete-Event-Simulation model. Numerical experiments based on a real-life case study are carried out to assess the proposed appointment rules’ performance and compare them to AS rules developed in the literature. The results highlight the robustness of our approach and demonstrate its usefulness in practice.

Evaluation of appointment scheduling rules: A multi-performance measurement approach

Appointment scheduling rules are used to determine when a customer is to receive service during a service session. In general, appointment scheduling rules do not consider the sequencing of individual customers, but provide simple guidelines on how to assign appointment times to a set of (arriving) customers. Many appointment scheduling rules exist and are being used in practice (e.g., in healthcare and legal services). Which appointment scheduling rule is best, however, is still an open question. In order to answer this question, we develop an analytical model that allows to assess the performance (in terms of customer waiting time, server idle time, and server overtime) of appointment scheduling rules in a wide variety of settings. More specifically, the model takes into account: (1) customer unpunctuality, (2) no-shows, (3) service interruptions, and (4) delay in session start time. In addition, we allow the use of general distributions to capture system processes. We adopt an efficient algorithm (with respect to computational and memory requirements) to assess the performance of 314 scheduling rules and use data envelopment analysis to identify the rules that have good, robust performance in a wide variety of settings.

Appointment scheduling in multi-stage outpatient clinics.

Healthcare providers can benefit from adding less costly capacity to their existing resources in order to satisfy demand while maintaining the quality of patient care. The addition of mid-level service providers (MLSPs) such as physician assistants or nurse practitioners that carry out portions of patient care provides a viable alternative for adding physician capacity. This research considers the circumstances under which adding an MLSP to a single-physician outpatient office becomes the best strategy for the clinic, and determines how scheduling policies from the widely-researched single-stage environment should be adjusted for a multi-stage environment. Compared to a single-stage system where a physician completes all portions of the service, we show that adding an MLSP can reduce patient waiting time, patient flow time, and physician service time with patients. This, in turn, can enable the clinic to see more patients and/or free up physician time for other tasks. Appointment scheduling rules are developed for a multi-stage outpatient service system using a simulation optimization approach. Performance measures focus on the patient experience and clinic operation before and during each stage of service.

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

This paper considers how to schedule appointments for outpatients, for a clinic that is subject to appointment lead-time targets for both new and returning patients. We develop heuristic rules, which are the exact and relaxed appointment scheduling rules, to schedule each new patient appointment (only) in light of uncertainty about future arrivals. The scheduling rules entail two decisions. First, the rules need to determine whether or not a patient's request can be accepted; then, if the request is not rejected, the rules prescribe how to assign the patient to an available slot. The intent of the scheduling rules is to maximize the utilization of the planned resource (i.e., the physician staff), or equivalently to maximize the number of patients that are admitted, while maintaining the service targets on the median, the 95th percentile, and the maximum appointment lead-times. We test the proposed scheduling rules with numerical experiments using real data from the chosen clinic of Tan Tock Seng hospital in Singapore. The results show the efficiency and the efficacy of the scheduling rules, in terms of the service-target satisfaction and the resource utilization. From the sensitivity analysis, we find that the performance of the proposed scheduling rules is fairly robust to the specification of the established lead-time targets.

Design and analysis of an outpatient orthopaedic clinic performance with discrete event simulation and design of experiments

Considering that demand for healthcare services is constantly increasing, outpatient services must improve their performance. Being able to satisfy the demand with a limited outpatient service capacity is an important operational challenge. The objective of our research consists in studying the relationships and interactions between patient flows, resource capacity (number of consulting rooms and number of nurses) and appointment scheduling rules in order to improve an outpatient orthopaedic clinic performance. Discrete event simulation is used to model outpatient flows. An experimental design was developed to test how to assign consulting rooms and nurses to each orthopedist considering four appointment scheduling rules and three patient flow types of varied complexity. Analysis of variance and the Tukey test are used to evaluate the simulation results. Our conclusion is that in order to improve the outpatient orthopaedic clinic performance, resources (consulting rooms, nurses) and appointment scheduling rules must be adapted to the different patient flows.

Evaluation of Appointment Scheduling Rules: A Multi-Performance Measures Approach

Appointment scheduling rules are used to determine when a customer is to receive service. Many appointment scheduling rules exist and are being used in practice (e.g., in healthcare and legal services). Which appointment scheduling rule is best, however, is still an open question. In order to answer this question, we develop an analytical model that allows to assess the performance (in terms of customer waiting time, server idle time and server overtime) of appointment scheduling rules in a wide variety of settings. More specifically, the model takes into account: (1) customer unpunctuality, (2) no-shows, (3) service interruptions and (4) delay of the service process. In addition, we allow the use of general distributions to capture system processes. We adopt an efficient algorithm (with respect to computational and memory requirements) to assess the performance of 314 scheduling rules and use data envelopment analysis to compare results.

Simulation analysis for reducing queues in mixed-patients' outpatient department

Customers desire short waiting times whereas service providers want to maximize resources utilization. Long waiting time is not uncommon in many service organizations and it is familiar especially in outpatient departments. To make the study more realistic, some assumptions were removed and uses an animated simulation model for a mixed-patient type environment in an outpatient department. A special purpose data generator is designed to explore bottlenecks in consultation rooms. Four appointment scheduling rules and their possible combinations are evaluated in two steps. First, experimentation concentrates on appointed and non-appointed patients. Second, it considers new patients in addition to those two categories. It is revealed that the rule which records the lowest waiting time is not feasible due to the high portion of server idle time. The rule that shows the lowest server idle time is not viable due to increased waiting time. It is possible to find the best rule which lies between these two times in a mixed-patient type environment.

Evaluating the impact of operating conditions on the performance of appointment scheduling rules in service systems

The general practice in implementing an appointment scheduling rule (ASR) is to enforce a certain rule, such as “block appointment”, to schedule customer arrivals in service systems. The operating environments of service systems are expected to affect considerably the performance of a selected ASR. The objective of this paper is to evaluate the impact of the environmental factors which include probability of no-show ( ρ), the coefficient of variation ( C ν ) of service times, and the number of customers per service session ( N). The extent to which a certain environmental factor affects the performance of ASR is examined to see if there is any ASR that performs well under most operating conditions. Under situations characterized by 27 different combinations of the factors ρ, C ν , and N, the performance of nine scheduling rules are evaluated by a simulation study. The simulation results show that an ASR designed to reduce customer waiting time performs very well in most operating environments considered. One commonly used ASR in real-world service systems, which schedules several customers to arrive at the start of each service session, tends to induce long customer waiting time.

Introducing variable‐interval appointment scheduling rules in service systems

The general practice in implementing an appointment scheduling rule (ASR) is to enforce a certain rule, such as “block appointment”, to schedule customer arrivals in service systems. There are several commonly used ASRs that have been used in such service systems as public and private clinics and restaurant reservations, most of which tend to minimize the idle time or optimize the utilization rate of the service facility while neglecting customers′ waiting times. One commonly used ASR in real‐world service systems, which schedules several customers to arrive at the start of each service session, tends to induce long customer waiting time, but manages to keep the facility idle time fairly low. Introduces an ASR to reduce customers′ waiting time considerably while increasing the facility idle time at a manageable minimum. Evaluates environmental factors that may affect the performance of ASRs, such as the probability of no‐shows, the coefficient of variation of service times, and the number of customers per...