Outpatient Appointment Scheduling Research Articles

Using data-driven techniques to predict outpatient ultrasound examination time for the multi-clinic outpatient appointment scheduling problem

Appointment scheduling for hospital patients is becoming increasingly important because it can reduce patient’s waiting time and enhance hospitals’ medical quality. In the case of ultrasound examinations, multiple radiological technologists use multiple rooms to conduct examinations, which involves the sequencing of examination work, time uncertainty, and operational characteristics. To enable hospital managers to effectively evaluate radiological technologists’ effort when carrying out ultrasound examinations, this study uses classification methods in data exploration, including decision tree (DT), logistic regression (LR), and artificial neural network (ANN), to establish a time prediction model for outpatient ultrasound examination. This study found that the DT model (i.e. C4.5) had the highest classification accuracy. After reviewing and removing rules generated from the model with an accuracy below 70%, the overall accuracy of the model was 76.31%. The classification results of the model are a reference for outpatient ultrasound appointment scheduling. The results of the study can effectively predict the examination time needed for ultrasound outpatients requiring different examination positions, thereby fairly allocating the patients to each room for the examination and reducing patient waiting time and radiological technologists’ idle time. Therefore, hospital managers can improve the hospital’s overall medical quality.

When outpatient appointment meets online consultation: A joint scheduling optimization framework

While the provision of Online Consultation Services (OCS) has brought convenience to patients, the original offline outpatient workflow may struggle to maintain efficiency. Hospital managers’ concerns lie on how to reconcile the operations of offline service with the provision of OCS and what are the latent impacts of OCS on the operational performances of healthcare system. The outpatient appointment scheduling is a crucial part of the workflow decision that affects both quality of offline services and the fragmented idle time that doctors can reply to their Follow-up patients (FUP) during working hours. To smooth the fluctuations in workload of OCS, we introduce a novel decision variable—the recommended maximum number of FUP that a doctor should serve during overtime hours (FUP-MN)—and propose an inter-day service shifting strategy that any requests for OCS exceeding the FUP-MN will be deferred to be served in the fragmented idle time of the next session. We formulate a stochastic programming model to search for the optimal combination of the appointment schedule and FUP-MN. Propositions are conducted to demonstrate the model is valid not only in coordinating the dual demand for online and offline services, but also in restricting the potential overflow of OCS requests. We provide a general solution approach and also develop a heuristic algorithm to quickly obtain optimal workflows at different OCS demand levels. The numerical experimental results indicate that an appropriate introduction of OCS can give the healthcare system a strong ability to resist the adverse effects of uncertainty.

Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics

Most outpatient clinics apply deterministic block scheduling policies to patient visits even though patients utilize varying amounts of time, leaving patients, operations managers, and clinicians frustrated because patients and physicians are kept waiting. This paper offers a decision-making model for schedulers so that the service time needed for a specific patient can be predicted to allow outpatient clinics to schedule more effectively. We employed an analytical approach, with a data driven methodology consisting of two phases. In phase one, machine learning algorithms are used to predict service time for outpatient clinics servicing patients with various characteristics. This study supports the understanding of factors that impact service time. A large dataset from an outpatient clinic is obtained and used in the analyses. Four dominant data mining models are developed to predict service time, and their performances are compared: neural networks (NNs), generalized linear model (GLM), linear regression (LR), and support vector regression (SVM). The NN models performed the best. The reason for visiting the doctor and patient type are identified as the primary characteristics to aid in predicting patient service time. We compare the proposed NN models with commonly used scheduling policies in practice in the second phase via simulation modeling and analysis. This paper contributes to the literature in four ways. First, we obtained a large dataset and extracted quality data to test the prediction accuracy of multiple models to determine which one improves scheduling the best. Second, patient characteristics are identified through machine learning modeling and sensitivity analysis to understand which ones are most important for service time prediction accuracy. Third, we analyzed the performance of standard scheduling policies used in clinics. Lastly, we provide clinical policy implications and recommendations that will provide insights and support appointment scheduling decisions.

Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling

Outpatient Chemotherapy Appointment (OCA) planning and scheduling is a process of distributing appointments to available days and times to be handled by various resources through a multi-stage process. Proper OCAs planning and scheduling results in minimizing the length of stay of patients and staff overtime. The integrated consideration of the available capacity, resources planning, scheduling policy, drug preparation requirements, and resources-to-patients assignment can improve the Outpatient Chemotherapy Process's (OCP's) overall performance due to interdependencies. However, developing a comprehensive and stochastic decision support system in the OCP environment is complex. Thus, the multi-stages of OCP, stochastic durations, probability of uncertain events occurrence, patterns of patient arrivals, acuity levels of nurses, demand variety, and complex patient pathways are rarely addressed together. Therefore, this paper proposes a clustering and stochastic optimization methodology to handle the various challenges of OCA planning and scheduling. A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. The developed cluster-based stochastic optimization approaches showed superior performance compared with baseline and sequencing heuristics using data from a real Outpatient Chemotherapy Center (OCC).

Evaluation of Interventions Targeting Follow-up Appointment Scheduling After Emergency Department Referral to Ophthalmology Clinics Using A3 Problem Solving

Many patients seen for eye-related issues in the emergency department do not receive recommended follow-up care. Prior evidence supports that scheduling appointments is a barrier to accomplishing the transition to outpatient ophthalmology care. To evaluate time until appointment scheduling following emergency department discharge with urgent outpatient ophthalmology referral. The A3 problem solving process was implemented by a multidisciplinary team as part of a structured quality improvement program with the goal of reducing the mean time between urgent referral placement in the emergency department and outpatient ophthalmology appointment scheduling. The study was conducted at Stanford Health Care, an academic medical center in Palo Alto, California, affiliated with Stanford University School of Medicine. Using medical center administrative records, all patients discharged from the adult emergency department with an urgent outpatient referral to the Stanford Department of Ophthalmology from August 9 to September 19, 2020 (baseline; n = 43), and from October 26 to November 29, 2020 (after implementation of all interventions; n = 21), were included. Interventions developed to target the workflow of the ophthalmology resident, emergency department, ophthalmology clinic, and health system schedulers to address key drivers of the referral-scheduling process included medical record documentation guidelines, identification of responsible parties, preidentified appointment slots, patient education materials, and education of stakeholders, and were implemented by October 25, 2020. Mean time between urgent referral placement (ie, emergency department discharge) and appointment scheduling with outpatient ophthalmology at baseline vs postintervention. At baseline, appointments were scheduled a mean (range) 2.8 (0-7) days after referral placement. In the 5 weeks following implementation of interventions, the mean (range) decreased to 1.3 (0-4) days, a difference of 1.5 days (95% CI, 0.20-2.74; P = .02). This corresponds to 642 (95% CI, 86-1173) days of reduced patient wait time annually. In addition, there was less variability in the number of days between referral and appointment scheduling after intervention compared with baseline. The results suggest improvement in efficiency of outpatient ophthalmology appointment scheduling of urgent emergency department referrals could be achieved through application of a quality improvement methodology by a multidisciplinary team representing key stakeholders in the process.

Developing a fuzzy programming model for improving outpatient appointment scheduling

Appointment scheduling for outpatient services is a challenge in the healthcare sector. For addressing this challenge, most studies assumed that patients’ unpunctuality and the duration of service have constant values or a specific probability distribution function. Consequently, there is a research gap to consider the uncertainty of both patients’ unpunctuality and the duration of service in terms of fuzzy sets. Therefore, this research aims to consider fuzzy values for both unpunctuality and duration of service have to improve an outpatient appointment scheduling (the time interval between two patients) in a referral clinic with the objective of reducing the total weight of waiting time, idle time, and overtime. Four different fuzzy linear programming models and 36 scenarios have been developed based on the show, no-show of patients, single-book, and double-book by using GAMS software. These four models are as follows: (1) probability of no-show equal to zero, (2) probability of no-show equal to 20%, (3) probability of no-show equal to zero and with double-book factor, and (4) probability of no-show equal to 20% and with double-book factor. The results of the first, second, third, and fourth models, respectively, present the scenarios considering 10, 5, 15, and 15 minutes for the time interval between two patients that have the minimum total weight of patient waiting times, physician idle times, and physician overtime. By considering these findings, the investigated referral clinic can improve its appointment system’s performance. Moreover, other similar clinics can apply the proposed model for improving their appointment systems' performance.

Perceived characteristics of outpatient appointment scheduling association with patient satisfaction and treatment adherence: An innovation theory application

Background Despite the growing interest in Appointment scheduling (APS) in hospitals of developing countries, empirical assessment of their effectiveness in improving patients’ experiences and adherence to treatment remains limited. We drew on the attributes of an innovation theory to hypothesise and test the extent to which perceived attributes of APS: relative advantage, compatibility and complexity influences patient satisfaction and treatment adherence in a neurology clinic of a large teaching hospital in Ghana. Methods A structured questionnaire was used to collect cross-sectional data from a sample of 295 scheduled patients visiting the clinic for follow-up care. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to analyse the hypothesised direct relationships of the APS attributes with patient satisfaction and treatment adherence, and the moderating effect of cordial doctor-patient communication in the relationships. Results While the compatibility of APS with patient preference was positively associated with patient satisfaction (p < 0.01) and adherence to treatment (p < 0.01), the reverse was observed for complexity of the APS system (p < 0.01). We also found statistically significant relationship of patient satisfaction with treatment adherence (p < 0.01) which was positively moderated by cordial doctor-patient communication. Conclusion Findings suggest that making the APS system more compatible with patient preference can greatly improve upon patient experience satisfaction and adherence to medical treatment for effective therapeutic outcomes. Practice implication In the light of its benefits to patient care and outcomes, APS should be given priority over walk-in services in specialty clinics of developing countries.

Integrating nurse assignment in outpatient chemotherapy appointment scheduling

In outpatient chemotherapy, nurses administer the drugs in two steps. In the first few minutes of each appointment, a nurse prepares the patient for infusion (drug administration). During the remainder of the appointment, the patient is monitored by nurses and if needed taken care of. One nurse must be assigned to prepare the patient and set up the infusion device. However, a nurse who is not busy setting up may simultaneously monitor up to a certain number of patients who are already receiving infusion. The prescribed infusion durations are significantly different among the patients on a day at a clinic. We formulate this problem as a multi-criterion mixed integer program. The appointments should be scheduled with start times close to patients’ ready times, balanced workload among nurses, few nurse changes during appointments, and few nurse full-time equivalent (FTE) assigned to the schedule of the day. As the number of nurse FTEs is an output of the model rather than a fixed input, the clinic can use the nursing capacity more efficiently, i.e., with less labor cost. We develop a 3-stage heuristic for finding criterion points with the minimum weighted average deferring time of appointments for the minimum feasible number of nurse FTEs or a desired value above that. By not constraining the number of chairs or beds, we can find solutions with better (dominating) criterion points. Drug preparation, oncologist visit, and the laboratory test can be scheduled based on the drug administration appointment start time. Thus, the drug administration resources are efficiently used with desirable performance in taking the interests and requirements of various stakeholders into consideration: patients, nurses, oncologists, pharmacy, and the clinic.

Arquitectura de procesos con indicadores estratégicos en los servicios médicos de consulta externa en hospitales de alta especialidad.

The importance of granting high specialty outpatient services calls for a diagnosis through process architecture in order to measure outpatient services deferral with key performance indicators and to evaluate the opportunity of scheduling appointments at high specialty hospitals. To apply the process architecture model and key performance indicators in high specialty hospitals, in order to analyze outpatient appointment scheduling activities and to improve the quality of medical care and patient safety. Quantitative, sequential and deductive reasoning study, with correlational scope, to assess the degree of association between variables, and to establish and define key performance indicators, according to the importance of activities in medical services. A convenience sample in time and by accessibility to two high specialty hospitals in Mexico city was used. Deferral in outpatient services was measured with an emphasis on the analysis of 19 indicators for a proposal of strategies. The model of priority indicators of the architecture of medical processes is presented through the analysis of activities, which comprehensively visualizes medical care and allows improving the quality of the medical services provided to the patient.

Scheduling outpatient day service operations for rheumatology diseases

Appointment scheduling systems represent a method to manage patient waiting lists effectively. This work advances an innovative quantitative approach for the outpatient appointment scheduling problems, based on an optimization model, to manage outpatient Day Service operations. It focuses on outpatient appointment scheduling. We start from earlier works in the literature to design models with the objective to maximize the number of patients’ appointments, to reduce patient’s waiting time, and to increase patient’s satisfaction. The proposed combinatorial problem is solved by Answer Set Programming, which is a declarative logic formalism, widely used in Artificial Intelligence and recognized as a powerful tool for Knowledge Representation and Reasoning, to show the advantages of declarative programming for modelling and fast prototyping problem requirements. We apply the model to solve real-life scenarios of the Rheumatology domain. We compare the results on the real instance already solved in our earlier work and extend the computational experiments on some new generated and realistic instances. Since the computational times increase with the size of instances, we develop a three-phase solution approach based on patient’s priority. The heuristic approach is hierarchical and enables to solve more instances than the one-run approach within the computational time limit.

A Capacity Allocation Planning Model for Integrated Care and Access Management.

The prevailing first-come-first-served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non-urgent patients. This paper develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (1) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non-urgent patients, and (2) increase throughput by 31% with the same service levels and overtime.

Enhancing outpatient appointment scheduling system performance when patient no-show percent and lateness rates are high

PurposeHigh lateness and no-show percentages pose great challenges on the patient scheduling process. Usually this is addressed by optimizing the time between patients in the scheduling process and the percent of extra patients scheduled to account for absent patients. However, since the patient no-show and lateness is highly stochastic we might end up with many patients showing up on time which leads to crowded clinics and high waiting times. The clinic might end up as well with low utilization of the doctor time. The purpose of this paper is to study the effect of scheduled overload percentages and the patient interval on the waiting time, overtime, and the utilization.Design/methodology/approachActual data collection and statistical modeling are used to model the distribution for common dentist procedures. Simulation and validation are used to model the treatment process. Then algorithm development is used to model and generate the patient arrival process. The simulation is run for various values of basic interval scheduled time between arrivals for the patients. Further, 3D graphical illustration for the objectives is prepared for the analysis.FindingsThis work initially reports on the statistical distribution for the common procedures in dentist clinics. This can be used for developing a scheduling system and for validating the scheduling algorithms developed. This work also suggest a model for generating patient arrivals in simulation. It was found that the overtime increases excessively when coupling both high basic interval and high overloading percentage. It was also found that: to obtain low overtime we must reduce the basic interval. Waiting time increases when reducing the basic scheduled appointment interval and increase the scheduled overload percentage. Also doctors’ utilization is increased when the basic interval is reduced.Research limitations/implicationsThis work was done at a local clinic and this might limit the value of the modeled procedure times.Practical implicationsThis work presents a statistical model for the various procedures and a detailed technique to model the operations of the clinics and the patient arrival time which might assist researches and developers in developing their own model. This work presents a procedure for troubleshooting scheduling problems in outpatient clinics. For example, a clinic suffering from high patient waiting time is directly instructed to slightly increase their basic scheduled interval between patients or slightly reduce the overloading percentage.Social implicationsThis work is targeting an extremely important constituent of the health-care system which is the outpatient clinics. It is also targeting multiple objectives namely waiting times, utilization overtime, which in turn is related to the economics and doctor utilization.Originality/valueThis work presents a detailed modeling procedure for the outpatient clinics under high lateness and no-show and addresses the modeling procedure for the patient arrivals. This 3D graphical charting for the objectives includes a study of the multiple objectives that are of high concern to outpatient clinic scheduling interested parties in one paper.

Design of a Patient-Centered Appointment Scheduling with Artificial Neural Network and Discrete Event Simulation

Quality of medical services is even more critical than ever in a highly competitive health care industry. Long waiting time is a major source of patient dissatisfaction, thus, the issue of how to reduce waiting time to improve service quality is very important. A well-designed appointment scheduling system can effectively shorten the patient waiting time and enhance their satisfaction. This study aims to propose a framework for individualized outpatient appointment scheduling (OAS) in a dental clinic which composed of one attending dentist and two resident dentists. To design the OAS, firstly, the prediction model of the treatment duration of an individual patient was established by using artificial neural network. Secondly, discrete event simulation method was used to develop the simulation model which simulates the operations of the studied dental clinic. Finally, the established simulation model was used to evaluate the performance of the appointment scheduling. The proposed model consists of numbers of main features: 1) the service providers composed of multiple dentists with different professional competence levels; 2) there are two types of patients (return and not-return patients); 3) patient no-shows was considered; and 4) a variety of medical treatments (requiring different treatment time) are provided to the patients. The results of the study show that the proposed OAS can effectively improve the service performance of the dental clinic, this could be caused by patient’s characteristics were taken into consideration of building an appropriated appointment interval.

A Capacity Allocation Planning Model for Integrated Care and Access Management

The prevailing first-come-first-served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non-urgent patients. This paper develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (1) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non-urgent patients, and (2) increase throughput by 31% with the same service levels and over.

A stochastic programming model for outpatient appointment scheduling considering unpunctuality

Many papers on outpatient appointment scheduling assume that patients arrive on time. However, unpunctuality is a stochastic factor that is inevitable in practice, which leads to patients arriving out of order. A schedule may not be reasonable if a clinic neglects the influence of patient unpunctuality. This paper addresses the outpatient scheduling problem considering unpunctuality (OS-U) by developing a stochastic programming model. We compare the performance of the OS-U system with the strict-punctuality (OS-P) system. We illustrate that the model has an exact and unified formula for cases of patients arriving in the appointment order and arriving out of order. The OS-U problem is solved by using Benders decomposition combined with the sample average approximation (BD-SAA) technique to determine the global optimal set of appointment intervals with the goal of minimizing the weighted sum of all patient waiting times, doctor idle times, and overtime. Numerical experiments indicate that the appointment rule changes when considering unpunctuality, although the set of optimal appointment intervals still takes the shape of dome (interval width increases at first, then remains nearly constant and eventually decreases for the last patients). The OS-P system schedules the first two patients together at the start of a session, whereas the OS-U system schedules them with different appointment times and requires a longer slot between the first two patients if patients tend to arrive early rather than late. The variance of unpunctuality has little impact. The no-show probability has a greater influence on system performances in an OS-U system than those in an OS-P system.

Resource Allocation and Outpatient Appointment Scheduling Using Simulation Optimization.

This paper studies the real-life problems of outpatient clinics having the multiple objectives of minimizing resource overtime, patient waiting time, and waiting area congestion. In the clinic, there are several patient classes, each of which follows different treatment procedure flow paths through a multiphase and multiserver queuing system with scarce staff and limited space. We incorporate the stochastic factors for the probabilities of the patients being diverted into different flow paths, patient punctuality, arrival times, procedure duration, and the number of accompanied visitors. We present a novel two-stage simulation-based heuristic algorithm to assess various tactical and operational decisions for optimizing the multiple objectives. In stage I, we search for a resource allocation plan, and in stage II, we determine a block appointment schedule by patient class and a service discipline for the daily operational level. We also explore the effects of the separate strategies and their integration to identify the best possible combination. The computational experiments are designed on the basis of data from a study of an ophthalmology clinic in a public hospital. Results show that our approach significantly mitigates the undesirable outcomes by integrating the strategies and increasing the resource flexibility at the bottleneck procedures without adding resources.

Multi-Criteria Decision Making in Outpatient Appointment Scheduling: An Application

Multi-Criteria Decision Making in Outpatient Appointment Scheduling: An Application

Comparison between Outpatient Appointment Scheduling and Chemotherapy Outpatient Appointment Scheduling

Healthcare management has received tremendous interest among researchers as it deals with human health. In this paper, two applications of scheduling on healthcare systems are studied. These two applications are outpatient appointment scheduling and chemotherapy outpatient appointment scheduling. We hold a scientific comparison based on computational models between the two appointment systems to investigate common and distinguishing features. The comparison is supported with two integer programming models computing the appointment times for patients. The two models are coded using CPLEX solver followed by numerical analysis of the results. The comparison findings are outpatient appointment problem has rich literature, whereas the chemotherapy outpatient problem has limited literature. Moreover, the appointment scheduling of chemotherapy outpatient units differs from outpatient clinics in some characteristics such as variability in treatment length, multiple steps in treatment and shared resources. On the other hand, the two systems have some common characteristics such as objectives and uncertainty in some parameters.

Abstract 142: Design and Implementation of an Electronic Process for Provider-ordered Post-hospitalization Follow-up Appointment Scheduling for Inpatients in an Academic Cardiology Department

Introduction: Post-hospitalization outpatient follow-up visits are important components of high quality care for patients with cardiovascular diseases. However, challenges in scheduling appointments prevent many patients from obtaining timely post-discharge care. Hypothesis: We hypothesized that a streamlined process for follow-up appointment scheduling utilizing the electronic order entry system would be an effective means of scheduling post-discharge visits for many inpatient admissions. Methods: In effort to increase patient access to post-discharge care, we designed a process for electronic provider-ordered follow-up appointment scheduling that was implemented on cardiology inpatient services in a large academic medical center in January 2014. During a patient hospitalization, providers were given the capability to order a follow-up appointment via the electronic order entry system. The order allows providers to designate the time-frame for follow-up, desired outpatient providers, and patient scheduling preferences. These orders are electronically transmitted to appointment schedulers, who schedule visits within two hours of order entry six days per week. Appointment information is reviewed with patients prior to discharge. Results: During the first 12 months after implementation (January to December 2014), 3406 follow-up appointment orders were placed for 2749 patients, or 1.24 visits ordered per patient. At least one visit ordered for 49.8% of the total 5534 cardiology admissions. The percentage of admitted patients for whom an outpatient visit was ordered from the first through fourth three-month interval in 2014 significantly increased: 28.2%; 48.0%, 53.4%, and 63%, respectively (Figure, p&lt;0.0001). Conclusion: A post-hospitalization outpatient appointment scheduling process using the electronic order entry system can be an effective method for scheduling follow-up visits for many inpatients prior to discharge.

Bilevel Fuzzy Chance Constrained Hospital Outpatient Appointment Scheduling Model

Hospital outpatient departments operate by selling fixed period appointments for different treatments. The challenge being faced is to improve profit by determining the mix of full time and part time doctors and allocating appointments (which involves scheduling a combination of doctors, patients, and treatments to a time period in a department) optimally. In this paper, a bilevel fuzzy chance constrained model is developed to solve the hospital outpatient appointment scheduling problem based on revenue management. In the model, the hospital, the leader in the hierarchy, decides the mix of the hired full time and part time doctors to maximize the total profit; each department, the follower in the hierarchy, makes the decision of the appointment scheduling to maximize its own profit while simultaneously minimizing surplus capacity. Doctor wage and demand are considered as fuzzy variables to better describe the real-life situation. Then we use chance operator to handle the model with fuzzy parameters and equivalently transform the appointment scheduling model into a crisp model. Moreover, interactive algorithm based on satisfaction is employed to convert the bilevel programming into a single level programming, in order to make it solvable. Finally, the numerical experiments were executed to demonstrate the efficiency and effectiveness of the proposed approaches.