Outpatient Appointment Systems Research Articles

Outpatient appointment systems: A new heuristic with patient classification

PurposeThis study aims to develop a heuristic for an outpatient appointment system considering patient classification. Design/methodology/approachThe proposed heuristic was applied in simulations with eighteen scenarios, combining different environmental factors. Total cost was adopted as a performance metric, composed of the patient's wait time and the service provider's idleness and overtime. The patients were divided into two classes according to their no-show probability, in an arrivals sequence with a binomial distribution. As a significance test of the results, Bonferroni-adjusted repeated measures analysis was applied. FindingsHaving Dome rule as baseline, an increase in performance in terms of total cost (TC) was observed, which varied between 0.46 % and 5.94 % among the means of the simulated environments, validated using the proposed significance test. The greatest benefits were obtained in the scenarios with lower ratios between service provider costs and patient costs (CR), as well as lower coefficients of variation for service times (Cv). It was also found that the heuristic is more efficient when patients from the class with the highest no-show rate predominate in the session. OriginalityThe single study identified in the literature that contemplates recalculations adopts deterministic service times to make its model viable. The present research, in turn, makes more realistic assumptions for the simulated environments, considering the variables and probability distributions most commonly observed in practical contexts Practical implicationsThe proposed heuristic provided a significant increase in performance for some combinations of environmental factors analyzed, preserving flexibility in the choice of appointment slots and covering a wide range of healthcare services found in practice.

Research and Design of the Outpatient Appointment System Based on WEB

With the continuous development of the internet industry, most hospitals have started to adopt online appointment and registration systems. Patients can easily make appointments and register online, saving the waiting time for patients to queue up and register at the hospital. At the same time, it effectively avoids the problem of personnel concentration and cross infection caused by centralized hospital registration. After registering with a real name, the user logs into the registration system, selects the corresponding hospital department, determines the required doctor, makes an online appointment for registration, and then goes to the hospital for treatment at the agreed time. Experiments have shown that the hospital appointment and registration system not only facilitate patients, saves patient time, but also makes hospital registration work efficient and standardized, with good application value.

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 ​= ​286,503), and a validation set (N2 ​= ​95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N ​= ​42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations.

'No more routine outpatient appointments in the NHS': it is time to shift to data-driven appointment.

Currently, outpatient care in the UK is expensive and needs improvement, with traditional systems having been identified as no longer fit for purpose. Making sustainable changes to outpatient appointment systems is vital in order to meet increasing demands and cost. Shifting to data and technology-driven outpatient care may be one way to tackle these demands. As technology becomes more diverse and accessible, its implementation into healthcare systems can make services more efficient and help with transitioning from outdated practices to more effective protocols. Patient Recorded Outcome Measures (PROMs) and home-monitoring devices could be the key step in identifying which patients require input and help shift to more data-driven appointment scheduling based on clinical need, rather than at regular intervals of time. Virtual care and technology-driven service provision could also revolutionise outpatient systems, maintaining high quality care while improving accessibility to patients. Patient involvement and empowerment while making these changes will assist shared decision making surrounding their care and allow them to be champions of their own health, helping clinicians to provide a patient-centred service. Understanding how these may be implemented will help clinicians take an active role in the development of these practices.

Dynamic appointment scheduling with forecasting and priority-specific access time service level standards

This paper analyzed a multi-priority system with access time service level requirements for dynamic arriving requests. Motivated by the public outpatient appointment systems, a dynamic scheduling algorithm is proposed for systems with demand influenced by seasonal and trend effects. Performances are measured by the proportion of requests that can access the service within the target time of their priority class, the access time percentiles and capacity utilization. The complexities of this problem include the problem size, time-varying demand with seasonality and trend, demand exceeding supply with no rejection of requests and the planning horizon considered. A goal programming model is formulated for the deterministic problem with service level objective assuming perfect information or forecasts. From the optimal properties, a simple scheduling rule with local exchange is developed. To forecast demand with possible updating during the rolling horizon, a forecasting approach incorporating linear trend for annual demand and logistic regression for patient class proportion is adopted. It is integrated with the scheduling heuristic in simulation to develop the dynamic access time rules. In the experiments designed based on three high-demand specialist outpatient clinics, demand scenarios are created to mimic different seasonal effects in a one-year horizon. Results showed improved access times for semi-urgent class and stable class patients with the largest 10–50% access times over a set of reported statistics.

Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics

We study the effects of rescheduling on no-show behavior in an outpatient appointment system for both new and follow-up patients. Previous literature has primarily focused on new patients and investigated the role of waiting time on no-show probability. We offer a more nuanced understanding of this costly phenomenon. Using comprehensive clinical data, we demonstrate that for follow-up patients, their no-show probability decreases by 10.9 percentage points if their appointments were rescheduled at their own request, but increases by 6.2 percentage points if they were rescheduled by the clinic. New patients, in contrast, are less sensitive to who initiates rescheduling. Their no-show probability decreases by 2.3 percentage points if their appointments were rescheduled at their own request, and increases by 3.2 percentage points—but is statistically insignificant at the 10% level—if they were rescheduled by the clinic. New patients are more concerned about waiting time compared with follow-up patients. For patients whose appointments were not rescheduled, new patients’ no-show probability decreases by 1.3 percentage points if their waiting time is reduced by one week, but the waiting time has a small and statistically insignificant effect on follow-up patients’ no-show probability. Using data-driven simulation, we conduct counterfactual investigation of the impact of allowing active rescheduling on the performance of appointment systems. In particular, allowing the flexibility of patient rescheduling can reduce the overall no-show rate and increase system utilization, but at a cost of increased wait time for new patients. If patients are able to reschedule at least one week in advance, new patients’ wait time is largely reduced, whereas the no-show rate remains the same; this is equivalent to the effect of a 5% increase in the clinic’s capacity.

Using Electronic Health Records Data to Evaluate the Impact of Information Technology on Improving Health Equity: Evidence from China

This study evaluates the impact of health information technology in accessing medical resources and identifies its role in improving health equity. We used 262, 771 records from the electronic medical records and outpatient appointment systems of three clinics for logistic regression to analyze the impact of information technology on patients' access to medical care. We interviewed a few health professionals to gauge their reactions and to validate and understand our quantitative results. The proportion of inpatients affected by information technology is low, accounting for only 16.7% (N = 43, 870). The difference between rural and urban groups is statistically significant, and rural households are more susceptible to information technology. In addition, distance has a significant positive effect. We demonstrate an inverted U-shaped relationship between severity of disease and the impact of information technology. Moreover, our interview results are consistent with our quantitative results. Quantitative and interview results suggest that health information technology plays a positive role in accessing medical care for patients with rural household and those in remote areas. Meanwhile, this effect is complex for patients with different severities of illnesses. Governments and managers should vigorously promote health information technology for healthcare delivery in the future and focus their attention on patients with serious diseases.

Do dietetic patients in a regional area attend a drop-in diabetes outpatient clinic? Proof-of-concept observational study

Background/AimsDrop-in clinics may be an alternative patient-centred approach to traditional appointment systems. However patient uptake in Allied Health settings is unknown. Given the limited literature, this observational prospective project tested whether patients with diabetes would present to a drop-in clinic, and whether the types and volume of patients would change due to introduction of a drop-in clinic. MethodsAlongside a referral-based booked individual appointment service (standard care (SC)), a drop-in clinic was introduced allowing patients to present without appointment. Patient data was collected from medical chart and outpatient appointment systems over 30 months. High category patient criteria included HbA1c>7.5%. Data was compared between drop-in and SC groups using chi-squared and ANOVA tests. ResultsOf 150 eligible patients, more drop-in patients (n = 76) presented over 15 months than SC patients booked in the 15 months before (n = 41) or 15 months after (n = 33) the drop-in clinic commenced. Drop-ins were 12 years older and less likely to have Type 1 Diabetes Mellitus (T1DM) than SC patients (p < 0.001), however the proportion of high category patients was similar across groups (54%, p = 0.731). SC patients were similar before and after drop-in clinic commencement (51%F, baseline HbA1c 9.5% ± 2.2, 34% clinic non-attendance, P = 0.159–0.671). ConclusionsPatients attended a drop-in diabetes outpatient clinic. This included high category patients. The weekday drop-in service may appeal to older patients with Type 2 Diabetes Mellitus, but not to younger patients or patients with T1DM. The types, volume, and attendance rates of SC patients was similar before and after commencement of the drop-in clinic.

Assessing the Online Outpatient Booking System

Background: Appointment scheduling is considered as one of the important aspects of patient flow management. Today, the advent of Internet into the realm of health care has paved the way for employing an internet-based booking system for visiting physicians. Therefore, this study aimed at assessing the online outpatient booking system in Iranian hospitals. Methods: The present study was an applied descriptive study. In this study, all outpatient appointment booking websites in Iranian hospitals were evaluated. The methods of outpatients booking in the hospitals (N = 598) were determined based on information available at their active websites. Finally, 31 active online booking systems were identified and investigated using a localized checklist, including 59 items in 6 sections. Results: The results showed that only 13.03% of Iranian hospitals had an active online booking system. All the online booking systems had a weak quality based on the total score percentage (17.10%). The highest score percentages were related to sections of online security features (45%) and details of health insurance (42%), while the clinical data section received the lowest score (7.25%). The most frequent item was the patient’s name in the patient demographics section (27 websites). Conclusions: The outpatient appointment system in Iranian hospitals is done more traditionally through walk-ins to the health centers. The online outpatient booking systems have poor quality. Hence, codification and communication of national standards for designing hospital websites, periodical evaluation of appointment booking websites by ministry of health, and survey of the users of this method can enhance the qualitative level of these websites.

Optimizing outpatient appointment system using machine learning algorithms and scheduling rules: A prescriptive analytics framework

In the US, the demand for outpatient services is expected to increase, while the supply of physicians to provide the care is projected to decrease. Besides, inefficiencies in the appointment system (AS) and patient no-shows (patients who do not arrive for scheduled appointments) reduce provider productivity, timely access to care, and cost the U.S. healthcare system more than $150 billion a year. To handle increasing demand and compensate for patient no-shows, outpatient clinics tend to overbook appointments. The current scheduling practice at most clinics and majority of the scheduling rules proposed in the literature assume all patients are equally likely to miss an appointment. Further, most scheduling rules in the literature do not make use of the available data, such as electronic health records, when scheduling patients. This paper proposes a prescriptive analytics framework to improve the performance of an AS with respect to patient satisfaction (measured using average patient waiting time and number of patients unable to get an appointment for the day under consideration) and resource utilization (measured using average resource idle time, overflow time and overtime). In the proposed framework, patient-related data from various sources are used to develop predictive models that identify the risk of a patient no-show. Different scheduling rules, that leverage the patient-specific no-show risk is then proposed. A case study, with real data from a Family Medicine Clinic in Pennsylvania, is used to show the feasibility of the proposed framework. The effectiveness of the proposed scheduling rules is evaluated by benchmarking it with three rules adapted from the literature. The results indicate that the proposed scheduling rules consistently outperform the benchmark rules for all the clinic settings tested. Further, the proposed framework is generic and can be adopted by any outpatient clinic characterized by occurrences of no-shows and appointment-based customer arrivals.

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.

Effects of Rescheduling on Patient No-Show Behavior in Outpatient Clinics

We study patients' no-show behavior in an outpatient appointment system for both new and follow-up patients, and explicitly consider rescheduling. Previous studies have either focused on appointments for new patients or treated rescheduled appointments as new ones. Findings from most of these studies reveal that no-show probability increases significantly with waiting time. We offer a more nuanced understanding of this costly phenomenon. Using clinical data, we demonstrate that the effect of waiting time on no-show behavior is largely weakened if the appointment was rescheduled. No-show behavior is also significantly affected by the rescheduling process, and patients of different types respond to rescheduling in notably different ways. For follow-up patients, no-show probability decreases when the appointment was rescheduled at the patient's request and increases when it was rescheduled by the clinic. New patients, in contrast, care more about whether the rescheduled appointment is sooner or later than the original appointment, rather than who initiated rescheduling. To the extent that rescheduling has a significant impact on no-show behavior, our findings offer managers a deeper understanding of patient no-show behavior and could be helpful in formulating effective policies to improve attendance rates.

Optimal Outpatient Appointment System with Uncertain Parameters Using Adaptive-Penalty Genetic Algorithm

The optimal number of doctors and appointment interval for an outpatient appointment system in a class of individual block/fixed interval are determined using an adaptive-penalty Genetic Algorithm. The length of service time for doctor consultation, the time required for the laboratory tests, and the time deviating from the appointment time are modelled by random variables. No-show patients are also included in the system. Using the adaptive penalty scheme, optimization constraints are automatically and numerically handled. The solution methodology is readily applicable to other appointment systems. The study has a significant implication from the viewpoint of economic and risk management of health care service.

Designing outpatient appointment systems with patient characteristics: a case study

Appointment system design based on patient characteristics has become a recent issue. This is investigated in a multi-facility system with the presence of second consultation using the discrete event simulation methodology. Different appointment systems are simulated, combining appointment rules and patient sequences with the adjustment of appointment intervals based on patient characteristics. The results show that the interval adjustment made on patient classifications is more successful than sequencing patients without making a time adjustment. The study identifies some efficient appointment systems for a given situation in relation to the trade-off between patient waiting time and physician idle time, while emphasising the importance of not excluding a second consultation in designing appointment systems.

Follow-up care in cancer: adjusting for referral targets and extending choice.

Over recent years, several initiatives have impacted on the referral of patients to secondary care, most notably targets for urgent referral in suspected cancer and the patient choice agenda. At the same time, improved long-term survival in cancer has increased numbers attending follow-up, doubts about the effectiveness of specialist follow-up have emerged, and alternative models of follow-up have been tested. The aim of the study was to explore patient and carer perspectives on the flexibility and responsiveness of cancer services. This article focuses on findings relating to referral, subsequent outpatient appointments and cessation of outpatient follow-up. Issues were explored in a qualitative study using face-to-face interviews with a purposive sample of 54 people affected by cancer. Data were analysed concurrently with data collection, using qualitative analysis software. The study gave rise to a number of salient themes. Links were identified between three of these: choice and responsiveness during referral; the flexibility and responsiveness of outpatient appointment systems; and negotiating cessation of follow-up. It appeared that policy on urgent referrals might be adversely affecting practice relating to appointment systems and the continuance of follow-up. Hospital-based cancer follow-up is being given decreasing priority because of doubts about effectiveness and a target-driven focus on referral. This is impacting on patients, who may value outpatient follow-up as a 'safety net' but have difficulties in obtaining appointments, and may be discharged without negotiation or adequate support. For these reasons, new forms of flexible/responsive aftercare are urgently needed.

Multiobjective study to implement outpatient appointment system at Hedi Chaker Hospital

The outpatient department at Hedi Chaker Hospital in Sfax, Tunisia, is currently using a block scheduling appointment whereby all patients are required to come at the beginning of the session. This brings about a tremendous waste of their time, creates congestion and puts further pressure on the medical staff. This paper evaluates a number of outpatient scheduling schemes based on the most recent research findings. Each model uses an appointment system scheme for scheduling with a well-performing appointment rule crossed with a sequencing rule. Environmental factors, such as the probability of no-shows and the probability of walk-ins, are included in the simulation of each of the different scenarios. Key measures of performance are determined for each of the different scenarios: waiting time in queues for each class of patients and resource utilisation. A Goal Programming Model is developed to choose the most appropriate schedule that optimizes the measures. Management preferences are incorporated to obtain a solution in line with their satisfaction.

Adaptive Appointment Systems with Patient Preferences

Patients' satisfaction with an appointment system when they attempt to book a nonurgent appointment is affected by their ability to book with a doctor of choice and to book an appointment at a convenient time of day. For medical conditions requiring urgent attention, patients want quick access to a familiar physician. For such instances, it is important for clinics to have open slots that allow same-day (urgent) access. A major challenge when designing outpatient appointment systems is the difficulty of matching randomly arriving patients' booking requests with physicians' available slots in a manner that maximizes patients' satisfaction as well as clinics' revenues. What makes this problem difficult is that booking preferences are not tracked, may differ from one patient to another, and may change over time. This paper describes a framework for the design of the next generation of appointment systems that dynamically learn and update patients' preferences and use this information to improve booking decisions. Analytical results leading to a partial characterization of an optimal booking policy are presented. Examples show that heuristic decision rules, based on this characterization, perform well and reveal insights about trade-offs among a variety of performance metrics important to clinic managers.

Improving out-patient appointment systems.

The Patient's Charter has recently set the standard for hospital out-patient clinics that patients will "be given a specific appointment time and will be seen within 30 minutes of that time". Describes the successful application of an operational research/operations management approach to out-patient appointment systems at the Royal Lancaster Infirmary. Draws the conclusion from a comparison of this and other studies that many hospitals face very similar problems with similar solutions; but that localized studies may often be required to achieve these solutions.

The outpatient appointment system: Design of a simulation study

This paper describes the model-construction of a simulation study. The purpose of this study was to produce a general method for determining a suitable appointment system for the clinics in the outpatient department of a hospital. The original model contained 11 variables. Investigation of the influence of each variable on patient's wainting-time and doctor's idle-time showed that a considerable reduction in the number of variables could be achieved. Only 5 variables were finally left in the stimulation. The use of the results of this study in a real-life clinic situation is discussed elsewhere.

Centralized Outpatient Appointment Systems: Delivering Ambulatory Care More Efficiently in Multi-Specialty Clinics

Centralized Outpatient Appointment Systems: Delivering Ambulatory Care More Efficiently in Multi-Specialty Clinics