Search Results (24)

Unattended scheduled appointments (“patient no-shows” henceforth) adversely affect healthcare providers and patients’ health, disrupting the continuity of care, operational efficiency, and allocation of medical resources. Therefore, accurate predictive modeling is needed to reduce the impact of patient no-shows. Although machine learning methods, such as logistic regression, random forests, and decision trees, are widely used to predict patient no-shows, they often rely on hard decision splits and static feature importance, limiting adaptability to complex patient behaviors. To address this limitation, we propose a hybrid multi-head attention soft random forest (MHASRF) model that integrates attention mechanisms into a random forest using probabilistic soft splitting. It assigns attention weights across the trees, enabling attention on specific patient behaviors. The MHASRF model exhibited an accuracy of 88.24%, specificity of 91.21%, precision of 81.60%, recall of 82.01%, F₁-score of 81.81%, and area under the receiver operating characteristic curve of 94.07%, demonstrating high and balanced performance across metrics. It could also identify key predictors of patient no-shows at two feature-importance levels (tree and attention mechanism), providing deeper insights into patient no-shows. Thus, the proposed MHASRF model is a robust, adaptable, and interpretable method for predicting patient no-shows that can help healthcare providers optimize resources. Full article

The management of operating rooms (ORs) is one of the most studied topics in operations research applied to healthcare. In particular, scheduling elective surgeries in a pediatric and teaching hospital can be a challenge because disruptions occur frequently. The aim of our research was to create a mathematical programming model to schedule day hospital (DH) patients, considering possible disruptions and defining how to best manage the rescheduling process. Our study originates from a collaboration between a high-volume pediatric surgery department and operations research practitioners. The possible disruptions we consider are emergencies and same-day cancellations of planned hospital operations. Elective DH surgeries are scheduled considering the waiting list and the patients’ clinical priorities, generating a nominal schedule. This schedule is optimized in conjunction with a series of back-up schedules to guarantee that OR activity immediately recovers in case of a disruption. An ILP-based approach to the problem is proposed. We enumerate a representative subset of the possible emergency and no-show scenarios, and for each of them a back-up plan is designed. The approach reschedules patients, minimizing disruptions with respect to the nominal schedule, and applies an as-soon-as-possible policy in case of emergencies to ensure that all patients receive timely care. The approach is shown to be effective in managing disruptions, ensuring that the waiting list is managed properly, with a balanced mix of urgent and less urgent patients. It provides an effective solution for scheduling patients in a pediatric hospital, considering the unique features of such facilities. Full article

Introduction: Gestational diabetes (GD) screening remains limited in many low- and middle-income countries (LMICs) due to resource constraints, limited training, and low community awareness. Although community-centered approaches may improve access to screening in rural and Indigenous settings, the implementation processes through which such approaches are designed and operationalized are rarely documented. Methods: This study presents a community-based implementation process report describing the development, adaptation, and early implementation of a GD screening program in rural Guatemala, guided by the Exploration, Preparation, Implementation, and Sustainment (EPIS) implementation science framework. Using a participatory approach, international screening guidelines were systematically adapted to the local context through iterative protocol refinement, structured stakeholder engagement, and ongoing feedback from community health educators and partner institutions. Aggregate program data were used descriptively to characterize early screening uptake and feasibility. Results: Key implementation challenges included patient no-shows, community skepticism, and difficulties among health educators in interpreting screening procedures. Iterative adaptations were introduced to simplify protocols, reduce loss to follow-up, and strengthen community engagement. Over time, the program expanded from point-of-care screening to more comprehensive prenatal services and increased collaboration with the Ministry of Health and local community outlets. A total of 103 Indigenous Mayan Tz’utujil women were screened (mean age: 26.9 years; range: 15–46), of whom, 12 were diagnosed with GD. Conclusions: This implementation process report demonstrates the scientific value of systematically documenting real-world adaptation, feasibility, and stakeholder engagement when introducing GD screening in rural Indigenous LMIC settings. The implementation lessons described may inform similar maternal health initiatives in comparable contexts. Full article

Objective: The aim of this study was to systematically map evidence to inform best practices for sexual and reproductive healthcare delivered via telehealth (TeleSRH) in United States-based Title X-funded clinics. Methods: We searched three databases (2017–2025) for studies evaluating effectiveness, harms, patient and provider experiences, barriers/facilitators, and engagement strategies encompassing TeleSRH for sexually transmitted infections (STIs), contraceptive care/family planning (CC/FP), and sexual wellness, in countries with a human development index of ≥0.8. Results: From 5963 references and 436 articles, we included 142 eligible publications. TeleSRH use declined since the COVID-19 pandemic’s peak but remains higher than pre-pandemic. Evidence comes mostly from poor-quality studies. TeleSRH increases access and adherence to STI prevention (e.g., pre-exposure prophylaxis for HIV). Tele-follow-up may safely facilitate HIV care continuity. For CC/FP, TeleSRH is comparable to in-person care for patient satisfaction and uptake; patients are less likely to select long-acting reversible contraception but post-initiation tele-follow-up may increase its continuation rates. Vasectomy completion rates may be similar between pre-procedural counseling via telehealth versus in-person. TeleSRH’s potential benefits might include reduced travel time, wait times, no-show rates, and clinic human resource burden (via tele-triaging) and increased preventative screening rates for STIs and non-communicable diseases, prescription refill rates, ability to receive confidential care in preferred settings, and rural/marginalized community outreach. Implementation challenges span technological and capital constraints, provider availability, staff capability building, restrictive policies, language incompatibility, and patient mistrust. Supplementing synchronous TeleSRH with asynchronous communication (e.g., mobile application) may improve continued patient engagement. Conclusions: Preventive, diagnostic, and therapeutic TeleSRH can be effective, with high patient acceptability; however, effectiveness and adoption hinge on contextual factors outlined in this review. Full article

The aim of this study was to analyze the effectiveness of implementing an automated appointment confirmation system in a mental health clinic and to assess its impact on patient attendance, which may indirectly support the patient recovery process. The study was conducted at a mental health outpatient clinic in Gdańsk, Poland, and focused on medical appointments across three affiliated outpatient units. Data from 2019 and 2023 were compared, focusing particularly on the rate of missed appointments (relationship between number of visits that did not take place and total number of visits that were scheduled in the software), form return rates (the relationship between the number of forms returned by patients and the total number sent), and patient opinions regarding the usability of the new system. The results showed a significant reduction in no-show rates—from 18.55% to 7.01%—confirming the high effectiveness of the automated system. The form return rate reached 55.41%, with the highest engagement observed among individuals aged 35–44. Patient evaluation of the system was highly positive—over 93% found it intuitive and meeting their expectations. A proprietary software solution developed in Python, alongside databases and Microsoft Office Access/Excel tools, was used for data collection and analysis. The study demonstrated that a comprehensive approach, combining automated reminders with the ability for quick patient response and telephone support, is an effective tool for improving the accessibility and quality of healthcare services. The analysis also considered limitations related to digital barriers and identified directions for further research, including studies on how patient abstention from appointments affects their recovery process. Full article

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

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

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

In this study, we aim to identify predictors of a no-show in neurology clinics at our institution. We conducted a retrospective review of neurology clinics from July 2013 through September 2018. We compared odds ratio of patients who missed appointments (no-show) to those who were present at appointments (show) in terms of age, lead-time, subspecialty, race, gender, quarter of the year, insurance type, and distance from hospital. There were 60,012 (84%) show and 11,166 (16%) no-show patients. With each day increase in lead time, odds of no-show increased by a factor of 1.0019 (p < 0.0001). Odds of no-show were higher in younger (p ≤ 0.0001, OR = 0.49) compared to older (age ≥ 60) patients and in women (p < 0.001, OR = 1.1352) compared to men. They were higher in Black/African American (p < 0.0001, OR = 1.4712) and lower in Asian (p = 0.03, OR = 0.6871) and American Indian/Alaskan Native (p = 0.055, OR = 0.6318) as compared to White/Caucasian. Patients with Medicare (p < 0.0001, OR = 1.5127) and Medicaid (p < 0.0001, OR = 1.3354) had higher odds of no-show compared to other insurance. Young age, female, Black/African American, long lead time to clinic appointments, Medicaid/Medicare insurance, and certain subspecialties (resident and stroke clinics) are associated with high odds of no show. Possible suggested interventions include better communication and flexible appointments for the high-risk groups as well as utilizing telemedicine. Full article

The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient’s profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient’s no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law. Full article