Appointment Scheduling Policies Research Articles

Competitive Algorithms for the Online Minimum Peak Job Scheduling

Algorithms to schedule medical appointments This paper was inspired by a field collaboration effort to develop and disseminate a real-time appointment scheduling decision support tool for an outpatient cancer infusion center in a large healthcare system. Two challenging aspects of scheduling daily medical appointments are that each patient is scheduled upon arrival without knowledge on future patients and that the appointments typically consume scarce physical resources (e.g., chairs, nurses, and doctors). A desirable schedule should have relatively smooth utilization over the course of a day to minimize the peak demand for the scarce resources. This paper develops new real-time (online) algorithms to schedule appointments in medical and other settings. It establishes theoretical properties of these algorithms, showing that they perform close to algorithms that could exploit full retrospective information on all the appointments. Additionally, it provides important insights to guide efficient real-time appointment scheduling policies in practice.

Appointment Scheduling for a Health Care Facility with Series Patients

This study focuses on determining the appointment scheduling for healthcare facilities with series patients. “Series” patients are patients who are scheduled for a series of appointments instead of a single appointment. Examples of healthcare services with series patients include radiotherapy/chemotherapy for cancer, physical therapy, kidney dialysis, diabetes treatment, etc. The aim of this study is to design appointment scheduling policies taking into account revenues per service per patient, costs of staffing, overtime, overbooking and delay. The appointment scheduling problem is formulated using an MDP model. However, due to the huge state space, computing the optimal policy is impractical. Hence, we propose the Index Policy (IP) based on a one‐step policy improvement algorithm applied to the MDP model. We study a further simplification obtained by approximating the distribution of the number of patient visits by a Geometric distribution. A key analytical contribution is to prove the MDP to be a uni‐chain, which implies that there exists an optimal policy that maximizes the long‐run average profit. We also find that the IP provides a significant improvement over the other policies. Especially with the Geometric approximation, the IP requires minimal effort in implementing, and works almost as well. To test the effectiveness of our proposed IP in a real‐world setting, we use the data from a local physical therapy center to compare its performance with two other commonly used policies, namely, the Next Available Day Policy and the Shortest Queue Policy. We recommend the IP with Geometric approximation for series patients’ scheduling, which is computationally efficient and can significantly increases profits by incorporating the series feature of the patients’ appointments. Finally, we provide analysis that incorporates several practical considerations such as accounting for patient heterogeneity in number of visits and inter‐visit times, the option to reject new patients when the system is at full capacity, and incorporating patients with known number of visits at the time of the scheduling decision.

Simulation of Appointment Scheduling Policies: a Study in a Bariatric Clinic.

Appointment scheduling systems traditionally book patients at fixed intervals, without taking into account the complexity factors of the health system. This paper analyzes several appointment scheduling policies of the literature and proposes the most suitable to a bariatric surgery clinic, considering the following complexity factors: (i) stochastic service times, (ii) patient unpunctuality, (iii) service interruptions, and (iv) patient no-shows. We conducted the study using data collected in a bariatric surgery clinic located in Rio de Janeiro, Brazil. The dataset presented 1468 appointments from June 29, 2015, to June 29, 2016. We comparatively evaluate the main literature policies through a discrete event simulation (DES). The proposed policy (IICR) provides a 30% increase in attendance and allows a decrease in the total cost, maintaining the level of service in terms of average waiting time. IICR was successfully implemented, and the practical results were very close to the simulated ones.

Simulation-based evaluation of patient appointment policies for a primary care clinic with unscheduled visits: a case study

Appointment scheduling represents an effective strategy to improve the health delivery process. The main objective of this study was to develop a framework to evaluate the impact of appointment scheduling policies on a primary care clinic with unscheduled, walk-in patient visits. A teaching hospital primary care clinic employing faculty and resident physicians was modelled and evaluated. The investigated clinic system included various types of patients, patient paths, and multiple clinic staff that served them under conditions of patient no-shows and unscheduled patient arrivals. A simulation model was developed to evaluate trade-offs in appointment scheduling policies in a primary care clinic that utilises a multi-server queueing process with stochastic arrivals and multiple processes. Recommendations for implementing walk-in policies with modifications to the appointment scheduling system were also provided.

Evaluating the long-term effects of appointment scheduling policies in a magnetic resonance imaging setting

This study addresses the problem of scheduling magnetic resonance imaging examinations to reduce indirect waiting time—that is, the time that elapses between the patient’s call for an appointment and the scheduled appointment time. Two alternative scheduling approaches are proposed: online and offline characterized, respectively, by the absence or presence of a batch of patients waiting for an appointment. Specifically, with an online approach, patients are scheduled when they call for an appointment, and consequently there is no need to have a batch of patients. On the contrary, the offline approach assumes that patients are given an expected waiting time when they call; they are subsequently called back and assigned an appointment within few days. With an offline approach, patients are thus collected in a batch until a scheduling policy is run; clearly, the batch size or equivalently the frequency according to which patients are scheduled impacts on the performance of the scheduling policy. The offline approach allows a better planning with respect to the online approach where the decision regarding a patient is greatly affected by the schedule of patients who called before him. On the other side, the online approach allows a prompt accommodation of the patient. In an attempt at trade-off these two approaches, the offline one is experimented with three different scheduling frequencies: once per week, two times per week, and three times per week. The paper describes a novel MIP model for implementation of the offline approach and a greedy heuristic for implementation of the online one. Online and offline approaches are then compared in terms of effectiveness, equity, efficiency and discrimination power, using a rolling horizon of 52 weeks and assuming different demand patterns. The comparison includes an evaluation of the impact of two managerial practices—examination overlapping and radiologist cross-training—when using the two forms of scheduling. The key findings are that the offline approach achieves smaller, less variable values for tardiness and enables scheduling of a higher number of patients in situations where capacity is scarce. As to managerial practices, overlapping is found to be relatively more effective than cross-training when an offline approach is adopted, while for online scheduling, cross-training is more effective than overlapping. The results presented are based on an extensive experimental campaign based on real data coming from a leading Italian hospital.

Outpatient appointment scheduling with unpunctual patients

This study examines the design of appointment scheduling policies with considerations of not only the conventional factors, for example, the random consultation time and multiple patient types, but also of a new factor, particularly, patient unpunctuality, that is, one patient may arrive earlier or later than the appointment time. Patient unpunctuality negatively affects the appointment scheduling system, for example, such behaviour, reduces provider productivity and clinical efficiency, increases health care costs, and limits the ability of a clinic to serve its patients population by reducing the clinic’s effective capacity. In this study, while considering patient unpunctuality, we first introduce an analytical model and show the optimality of a fixed-interval policy for a simplified two-patient model. Motivated by the result, we propose an easy-to-implement heuristic policy with a simple structure using a simulation framework to improve the performance of the appointment scheduling system. The simulation result shows that our policy is overwhelmingly preponderant in current practice. We also measure the effect of patient unpunctuality and other factors. Actual data are used to add realism to the input parameters, and practical guidelines are developed for appointment scheduling.

Simulation modelling and analysis of appointment system performance for multiple classes of patients in a hospital: A case study

Appointment systems for scheduling patients to a hospital facility play an important role in controlling and synchronizing the arrival of patients with resource availability thereby reducing the waiting time of patients and increasing the utilization of resources. In this paper, hospital appointment systems with multiple classes of patients are considered where different classes of patients may vary in punctuality, no-show probabilities, mean service times and service time variability. Classification of patients may help in providing important insights for designing an appointment system considering different sequencing schemes and adjustments of inter-appointment times. For illustration, a case study is conducted for appointment scheduling of patients to the Magnetic Resonance Imaging (MRI) scanning machine of the Radiology department of a multi-speciality hospital situated in eastern India. The patient flows in the system are modelled using discrete-event simulation wherein a number of appointment scheduling policies (combinations of different sequencing and appointment rules) are analysed and evaluated. The performance measures of interest include the waiting times of all classes of patients and utilization of the server (MRI machine). The simple sequencing rule of ordering the patient classes in increasing order of mean service time performs best among all the sequencing rules. The individual block variable interval appointment rule, i.e., scheduling a single patient at a time with inter-appointment times adjusted according to the mean service times of patient class performs best.

Simulation-Based Approximate Policy Iteration with Generalized Logistic Functions

We present an approximate dynamic programming method based on simulation, policy iteration, a postdecision state formulation, and a logistic value function approximation. This method was developed as part of our efforts to determine whether nonlinear value function approximations could provide cost-effective policies for advance patient scheduling problems, and as a way of identifying the main advantages and disadvantages of using simulation versus linear programming to approximately solve dynamic capacity allocation problems. We first apply the proposed method to a queueing problem and then study a more practical application based on an advance multipriority patient scheduling problem. We investigate the quality and practical implications of the resulting appointment scheduling policies using simulation, and compare their performance to that of four other policies. Patient scheduling policies obtained by the new method not only depend on the number of appointments already booked on each day but also on the overall system workload. In particular, these policies provide lower discounted cost values and shorter average wait times for higher priority patients than policies directly obtained using linear programming and an affine value function approximation in the predecision state variables.

Capacity planning and appointment scheduling for new patient oncology consults.

To ensure that patients receive timely access to care, it has become increasingly important to use existing care provider capacity as efficiently as possible and to make informed capacity planning decisions. To support this decision-making process at a regional cancer center in British Columbia (Canada), we undertook a simulation and optimization based study that investigated the simultaneous impact of the available number of new patient consultation slots, appointment scheduling policies and oncologist specialization configurations on the timeliness of patient access to care and physician workload. The key contribution of this paper is the methodological framework it provides to decision makers who manage specialty clinics to ensure that they are using their resources efficiently and making informed strategic short- and mid-term capacity planning decisions for new patient demand.

The Effect of Integrated Scheduling and Capacity Policies on Clinical Efficiency

In outpatient healthcare clinics, capacity, patient flow, and scheduling are rarely managed in an integrated fashion, so a question of interest is whether clinic performance can be improved if the policies that guide these decisions are set jointly. Despite the potential importance of this issue, we find surprisingly few studies that look at how the allocation of capacity, paired with various appointment scheduling policies and different patient flow configurations, affects patient flow and clinical efficiency. In this paper, we develop an empirically based discrete‐event simulation to examine the interactions between patient appointment policies and capacity allocation policies (i.e., the number of available examination rooms) and how they jointly affect various performance measures, such as resource utilization and patient waiting time. Findings suggest that scheduling lower‐variance, shorter appointments earlier in the clinic (and, conversely, higher‐variance, longer appointments later) results in less overall patient waiting without reducing physician utilization or increasing clinic duration. Additionally, exam rooms exhibited classic bottleneck behavior: there was no effect on physician utilization by adding exam rooms beyond a certain threshold, but too few exam rooms were devastating to clinic throughput. Some significant interactions between these variables were observed, but were not influential to the level of managerial concern. Clinicians' intuition about managing capacity in healthcare settings may differ substantially from best policies.

The Effects of Patient No-Shows on Traditional and Open-Access Appointment Scheduling Policies

This paper compares two types of appointment scheduling policies for single providers: traditional and open-access. Under traditional scheduling, each of a specified number of patients per day is booked well in advance, but may not show up for his or her appointment. Under open-access scheduling, each of a random number of patient calls in the morning to make an appointment for that same day. Thus the number of patient arrivals will be random, for different reasons, under both policies. We find that the open-access schedule will significantly outperform the traditional schedule - in terms of a weighted average of patients’ waiting time, the doctor’s idle time, and the doctor’s overtime - except when patient waiting time is held in little regard, or when the probability of no-shows is quite small.

Scheduling Patients' Appointments: Allocation of Healthcare Service Using Simulation Optimization.

In the service industry, scheduling medical procedures causes difficulties for both patients and management. Factors such as fluctuations in customer demand and service time affect the appointment scheduling systems' performance in terms of, for example, patients' waiting time, idle time of resources, and total cost/profits. This research implements four appointment scheduling policies, i.e., constant arrival, mixed patient arrival, three-section pattern arrival, and irregular arrival, in an ultrasound department of a hospital in Taiwan. By simulating the four implemented policies' optimization procedures, optimal or near-optimal solutions can be obtained for patients per arrival, patients' inter-arrival time, and the number of the time slots for arrived patients. Furthermore, three objective functions are tested, and the results are discussed. The managerial implications and discussions are summarized to demonstrate how outcomes can be useful for hospital managers seeking to allocate their healthcare service capacities.