Search Results (30)

Objectives: The aim of this study was to analyse no-show patterns in healthcare appointments, identify associated factors, and explore key determinants influencing non-attendance. Study Design: This was a retrospective observational study. Methods: We analysed 120,405 healthcare appointments from 2022–2023 in Turin, Northern Italy. Data included demographics, appointment characteristics, and attendance records. Logistic regression identified significant predictors of no-shows, adjusting for confounders. Results: A 5.1% (n = 6198) no-show percentage was observed. Younger patients (<18 years) and adults (18–65 years) had significantly higher odds of missing appointments than elderly patients (>65 years) (OR = 2.32, 95% CI: 2.17–2.47; OR = 2.46, 95% CI: 2.20–2.74; p < 0.001). First-time visits had a higher no-show risk compared to follow-up visits and diagnostics (OR = 1.11, 95% CI: 1.04–1.18; p < 0.001). Each additional day of waiting increased the likelihood of no-show by 1% (OR = 1.01, 95% CI: 1.01–1.01; p < 0.001). Conclusions: No-show percentages are influenced by demographic and service-related factors. Strategies targeting younger patients, longer waiting times, and non-urgent appointments could reduce no-show percentages. Full article

Background/Objectives: Missed outpatient appointments contribute to care discontinuity and emergency department (ED) overcrowding. This study investigated the association between missed appointments and ED visits, identifying predictors such as patient characteristics, distance from the hospital, and waiting time. Methods: A retrospective analysis was conducted using a dataset of 749,450 scheduled outpatient appointments from adult patients (aged ≥ 18 years). Patients under 18 were excluded. We identified missed appointments and assessed their association with ED visits occurring in the same period. Descriptive statistics, non-parametric tests, and logistic and linear regression models were applied to examine predictors such as age, sex, distance from the hospital, waiting time, the type of service, and medical specialty. Results: The overall no-show rate was 3.85%. Among patients with missed appointments, 37.3% also visited the ED. An older age (OR = 1.007; p = 0.006) and the male gender (OR = 1.498; p < 0.001) were significant predictors of having a scheduled appointment before an ED visit. No significant associations were found for distance or specialty branch. Conclusions: Missed appointments are associated with ED utilization. Predictive factors can inform targeted interventions, such as via improved scheduling systems and personalized reminders. Distance alone may not be a barrier, but system-level solutions are needed to address no-show rates and optimize healthcare resource use. Full article

Patient no-shows significantly disrupt pediatric healthcare delivery, highlighting the necessity for precise predictive models, especially during the dynamic shifts caused by the SARS-CoV-2 pandemic. In outpatient settings, these no-shows result in medical resource underutilization, increased healthcare costs, reduced access to care, and decreased clinic efficiency and increased provider workload. The objective is to develop a predictive model for patient no-shows using data-driven techniques. We analyzed five years of historical data retrieved from both a scheduling system and electronic health records from a general pediatrics clinic within the WVU Health systems. This dataset includes 209,408 visits from 2015 to 2018, 82,925 visits in 2019, and 58,820 visits in 2020, spanning both the pre-pandemic and pandemic periods. The data include variables such as patient demographics, appointment details, timing, hospital characteristics, appointment types, and environmental factors. Our XGBoost model demonstrated robust predictive capabilities, notably outperforming traditional “no-show rate” metrics. Precision and recall metrics for all features were 0.82 and 0.88, respectively. Receiver Operator Characteristic (ROC) analysis yielded AUCs of 0.90 for all features and 0.88 for the top five predictors when evaluated on the 2019 cohort. Furthermore, model generalization across racial/ethnic groups was also observed. Evaluation on 2020 telehealth data reaffirmed model efficacy (AUC: 0.90), with consistent top predictive features. Our study presents a sophisticated predictive model for pediatric no-show rates, offering insights into nuanced factors influencing attendance behavior. The model’s adaptability to evolving healthcare delivery models, including telehealth, underscores its potential for enhancing clinical practice and resource allocation. Full article

The implementation of the truck appointment system (TAS) in various ports shows that it can effectively reduce congestion and enhance resource utilization. However, uncertain factors such as traffic and weather conditions usually prevent the external trucks from arriving at the port on time according to the appointed period for container pickup and delivery operations. Comprehensively considering the significant factors associated with truck appointment no-shows, this paper proposes a deep learning model that integrates the long short-term memory (LSTM) network with the transformer architecture based on the cascade structure, namely the LSTM-Transformer model, for actual truck arrival predictions at the container terminal using TAS. The LSTM-Transformer model combines the advantages of LSTM in processing time dependencies and the high efficiency of the transformer in parsing complex data contexts, innovatively addressing the limitations of traditional models when faced with complex data. The experiments executed on two datasets from a container terminal in Tianjin Port, China, demonstrate superior performance for the LSTM-Transformer model over various popular machine learning models such as random forest, XGBoost, LSTM, transformer, and GRU-Transformer. The root mean square error (RMSE) values for the LSTM-Transformer model on two datasets are 0.0352 and 0.0379, and the average improvements are 23.40% and 18.43%, respectively. The results of sensitivity analysis show that possessing advanced knowledge of truck appointments, weather, traffic, and truck no-shows will improve the accuracy of model predictions. Accurate forecasting of actual truck arrivals with the LSTM-Transformer model can significantly enhance the efficiency of container terminal operational planning. Full article

Parcel delivery networks have grown rapidly during the last few years due to the intensive evolution of online marketplaces. We address the issue of managing the operation of a network’s pick-up point, including the selection of the warehouse’s capacity and the policy for accepting orders for delivery. The existence of the time lag between order placing and delivery to the pick-up point is accounted for via modeling the order’s processing as the service in the dual tandem queueing system. Distinguishing features of this tandem queue are the account of possible irregularity in order generation via consideration of the versatile Markov arrival process and the possibilities of batch transfer of the orders to the pick-up point, group withdrawal of orders there, and client no-show. To reduce the probability of an order rejection at the pick-up point due to the overflow of the warehouse, a threshold strategy of order admission at the first stage on a tandem is proposed. Under the fixed value of the threshold, tandem operation is described by the continuous-time multidimensional Markov chain with a block lower Hessenberg structure for the generator. Stationary performance measures of the tandem system are calculated. Numerical results highlight the dependence of these measures on the capacity of the warehouse and the admission threshold. The possibility of the use of the results for managerial goals is demonstrated. In particular, the results can be used for the optimal selection of the capacity of a warehouse and the policy of suspending order admission. Full article

The shipping market is unpredictable and volatile due to some uncontrollable factors such as epidemic, conflicts and natural disasters. There is always an imperfect match between the supply capacity of liner companies and the actual demand of the market, which leads to a waste of slot resources and/or unsatisfied customer demand. Furthermore, the trade off between empty container transportation and laden container transportation is the crucial problem of strategic importance for liner companies. To deal with the above problem, this paper aims to develop a new solution to the collaborative optimization problem of container slot allocation and empty container repositioning by exploring the resource allocation, storage, and repositioning methods collaboratively. An online booking platform is introduced in this paper, and no-shows and customer preferences are considered in the analysis. An innovative integer programming model is established based on an online booking mode and a delivery-postponed strategy. A new branch-and-cut algorithm is then proposed to solve the problem. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model and algorithm. The experimental results show that collaborative optimization can remarkably enhance the revenue of liner companies along with increasing the utilization of slot resources. Full article

Background: Patient no-show behavior is a critical factor complicating hospital resource optimization and causing waste. The inefficiency caused by patients’ no-shows and the resulting increased operating costs negatively affect the hospitals’ financial structure and service quality. For this reason, health managers must make accurate predictions about whether patients will attend an appointment and plan the appointment system within the framework of these predictions. This research aims to optimize the hospital appointment system by making accurate predictions regarding the no-show behavior of the patients, based on recorded data. Methods: An artificial intelligence-based appointment system has been developed according to patients’ demographics and past behavior patterns. The forecast results and realized performance results were compared. The artificial intelligence we have developed continuously improves appointment assignments by learning from past and current data. Results: According to the findings, the artificial intelligence-based appointment system increased the rate of patients attending appointments by 10% per month. Likewise, the hospital capacity utilization rate increased by 6%. Conclusions: Findings from the study confirmed that no-show risks could be managed in the appointment process through artificial intelligence. This artificial intelligence-based design for appointment systems significantly decreases hospital costs and improves service quality performance. Full article

Any country’s healthcare system is vital for its progress, quality of life, and long-term viability. During the pandemic, many developed countries encountered challenges of differing degrees in the administration of their healthcare systems. The overloading of healthcare services is common, leading to prolonged waiting times for medical services. Thus, the wastage of hospital resources must be taken seriously. In this paper, we examine the problem of no-shows and cancellations in outpatient clinics. By examining the literature and drawing from practical industry experience, we uncover the operational procedures of these clinics. We then suggest a robust optimization strategy for overbooking, incorporating both a conventional overbooking model and a resilient system approach. The proposed model seeks to address the substantial uncertainties in parameters encountered during the pandemic. Taking into account risk aversion, we develop an optimal overbooking policy that considers the associated costs. The primary contribution lies in introducing an alternative approach to manage the uncertainty of no-shows and cancellations through the utilization of an overbooking technique. Full article

Hospitals that are considered non-profit take into consideration not to make any losses other than seeking profit. A model that ensures that hospital price policies are variable due to hospital revenues depending on patients with appointments is presented in this study. A dynamic pricing approach is presented to prevent patients who have an appointment but do not show up to the hospital from causing financial loss to the hospital. The research leverages three distinct machine learning (ML) algorithms, namely Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB), to analyze the appointment status of 1073 patients across nine different departments in a hospital. A mathematical formula has been developed to apply the penalty fee to evaluate the reappointment situations of the same patients in the first 100 days and the gaps in the appointment system, considering the estimated patient appointment statuses. Average penalty cost rates were calculated based on the ML algorithms used to determine the penalty costs patients will face if they do not show up, such as 22.87% for RF, 19.47% for GB, and 14.28% for AB. As a result, this study provides essential criteria that can help hospital management better understand the potential financial impact of patients missing appointments and can be considered when choosing between these algorithms. Full article

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related deaths in the world. Patients with early-stage HCC are treated with liver-directed therapies to bridge or downstage for liver transplantation (LT). In this study, the impact of HCC care delay on HCC progression among early-stage patients was investigated. Early-stage HCC patients undergoing their first cycle of liver-directed therapy (LDT) for bridge/downstaging to LT between 04/2016 and 04/2022 were retrospectively analyzed. Baseline variables were analyzed for risk of disease progression and time to progression (TTP). HCC care delay was determined by the number of rescheduled appointments related to HCC care. The study cohort consisted of 316 patients who received first-cycle LDT. The HCC care no-show rate was associated with TTP (p = 0.004), while the overall no-show rate was not (p = 0.242). The HCC care no-show rate and HCC care delay were further expanded as no-show rates and rescheduled appointments for imaging, laboratory, and office visits, respectively. More than 60% of patients experienced HCC care delay for imaging and laboratory appointments compared to just 8% for office visits. Multivariate analysis revealed that HCC-specific no-show rates and HCC care delay for imaging (p < 0.001) were both independently associated with TTP, highlighting the importance of minimizing delays in early-stage HCC imaging surveillance to reduce disease progression risk. Full article

Healthcare institutions generally use an appointment system. However, patients often need to receive medical services unexpectedly. If they visit a clinic without an appointment, they may have to wait for a long time, as their priority is low. In this study, we investigated whether the clinic registration system can be improved by separating the queues and resources for different types of patients. From our simulation results, we found that under a certain setup, the separation policy does not effectively reduce the walk-ins’ waiting time, nor improve the service. The study gives valuable managerial insights into the factors affecting patients’ waiting times. As the number of random walk-ins is relatively higher, the service times are longer, and the no-show rate of appointments is lower, separation may reduce the waiting time of walk-in patients. Full article

This study examines the influence of various individual demographic and risk factors on the use of unscheduled healthcare (emergency and inpatient visits) among pediatric outpatients with asthma over three retrospective timeframes (12, 18, and 24 months) at an academic health center. Out of a total of 410 children who visited an academic medical center for asthma outpatient care between 2019 and 2020, 105 (26%) were users of unscheduled healthcare for childhood asthma over the prior 12 months, 131 (32%) over the prior 18 months, and 147 (36%) over the prior 24 months. multiple logistic regression (MLR) analysis of the effect of individual risk factors revealed that asthma severity, age of child, and clinic no-shows were statistically significant predictors of unscheduled healthcare use for childhood asthma. Children with higher levels of asthma severity were significantly more likely to use unscheduled healthcare (compared to children with lower levels of asthma severity) across all three timeframes. Likewise, children with three to four clinic no-shows were significantly more likely to use unscheduled healthcare compared to children with zero clinic no-shows in the short term (12 and 18 months). In contrast, older children were significantly less likely to use unscheduled healthcare use compared to younger children in the longer term (24 months). By virtue of its scope and design, this study provides a foundation for addressing a need identified in the literature for short- and long-term strategies for improving supported self-management and reducing unscheduled healthcare use for childhood asthma at the patient, provider, and organizational levels, e.g., (1) implementing telehealth services for asthma outpatient care to reduce clinic no-shows across all levels of asthma severity in the short term; (2) developing a provider–patient partnership to enable patient-centered asthma control among younger children with higher asthma severity in the long term; and (3) identifying hospital–community linkages to address social risk factors influencing clinic no-shows and unscheduled healthcare use among younger children with higher asthma severity in the long term. Full article

No-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients’ behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient’s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient’s scheduling. Full article

The Korean government implemented a 52-h workweek policy for employees’ welfare. Consequently, companies face workforce availability reduction with the same number of employees. That is, labor-dependent companies suffer from workforce shortage. To handle the workforce shortage, they increase irregular employees who are paid relatively less. However, the problem of ‘no-show’, due to the stochastic characteristics of irregular employee’s absence, happens. Therefore, this study aims to propose a staff scheduling strategy considering irregular employee absence and a new labor policy by using linear programming. By deriving a deterministic staff schedule through system parameters derived from the features and rules of an actual company in the numerical experiment, the practicality and applicability of the developed mathematical model are proven. Furthermore, through sensitivity analysis and simulation considering the stochastic characteristics of absences, various proactive cases are provided. Through the proactive cases, the influence of the change of the average percent of irregular employees’ absences on the total labor costs and staff schedules and the expected number who would not come to work could be given when assuming the application in practice. This finding can help decision-makers prepare precautious measures, such as assigning extra employees in case of an irregular employee’s absence. Full article

Background: Early research showed weight gain in children during the COVID-19 pandemic. Objective: To compare changes in BMI z-score of children with overweight and obesity in a personalised lifestyle intervention before and during the pandemic. Methods: Changes in BMI z-score half a year (T6) and twelve months (T12) after the first lockdown were included for 71 children in the ‘2020 during COVID’ group and compared to 48 children in the ‘2019 before COVID’ group, using a marginal model for repeated measures (model 1). Model 2 corrected for lifestyle intervention characteristics, and model 3 corrected additionally for family characteristics. Results: The mean difference in BMI z-score change was significantly different at T12 (+0.07 in 2020 versus −0.09 in 2019, p = 0.022). Model 3 showed significant differences in BMI z-score change at both T6 (+0.15, p = 0.024) and T12 (+0.18, p = 0.016). This model also defined ‘having a mother with obesity’ (+0.13, p = 0.019) and the frequency of no-show consultations (+0.41 per missed consultation per month, p = 0.025) as related factors. Conclusions: Lifestyle intervention in children with overweight and obesity is less successful in decreasing BMI z-score during the COVID-pandemic. Identified risk factors for less success could contribute to identifying children with higher risks for, and possibly prevent, BMI z-score increase. Full article