Search Results (150)

Patient unpunctuality substantially complicates appointment scheduling in integrated telemedicine–traditional outpatient systems. The current research frequently ignores behavioral distinctions between telemedicine patients and outpatients, while neglecting to measure the intangible burden on physicians from service mode switches. To address these gaps, this study incorporates patient heterogeneity and introduces two novel cost metrics. Specifically, we implement penalties for service-mode switching and penalties for consecutive telemedicine sessions. We develop a Stochastic Mixed-Integer Programming (SMIP) model. This stochastic model is transformed into a deterministic Mixed-Integer Linear Programming (MILP) formulation via Sample Average Approximation (SAA). Linearization techniques enhance computational efficiency. In numerical experiments, the dual-penalty model yields balanced schedules with moderate patient mix, reducing physician overtime by 62.5% and service mode switches by 55% compared to baseline approaches. Sensitivity analysis confirms that narrowing outpatient unpunctuality ranges significantly reduces patient waiting and overtime, while raising telemedicine patient proportions bolsters system stability at the cost of increased physician idle time. These insights offer actionable guidance for healthcare institutions managing integrated online–offline services. Full article

Background: The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes and fostering collaboration between stakeholders such as vessel operators, logistics providers, and terminal managers is critical to mitigating these inefficiencies. Methods: This study proposes a multi-agent, multi-objective coordination model that synchronizes vessel berth allocation with truck appointment scheduling. A solution method combining prioritized planning with a neighborhood search heuristic is introduced to explore Pareto-optimal trade-offs. The performance of this approach is benchmarked against well-established multi-objective evolutionary algorithms (MOEAs), including NSGA-II and SPEA2. Results: Numerical experiments demonstrate that the proposed method generates a greater number of Pareto-optimal solutions and achieves higher hypervolume indicators compared to MOEAs. These results show improved balance among objectives such as minimizing vessel waiting times, reducing truck congestion, and optimizing terminal resource usage. Conclusions: By integrating berth allocation and truck scheduling through a transparent, multi-agent approach, this work provides decision-makers with better tools to evaluate trade-offs in port terminal operations. The proposed strategy supports more efficient, fair, and informed coordination in complex multimodal environments. Full article

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 ± 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 ± 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study. Full article

Background: With advancements in Generative Artificial Intelligence, various industries have made substantial efforts to integrate this technology to enhance the efficiency and effectiveness of existing processes or identify potential weaknesses. Context, however, remains a crucial factor in leveraging intelligence, especially in high-stakes sectors such as healthcare, where contextual understanding can lead to life-changing outcomes. Objective: This research aims to develop a practical medical multi-agent system framework capable of automating appointment scheduling and triage classification, thus improving operational efficiency in healthcare settings. Methods: We present MedScrubCrew, a multi-agent framework integrating established technologies: Gale-Shapley stable matching algorithm for optimal patient-provider allocation, knowledge graphs for semantic compatibility profiling, and specialized large language model-based agents. The framework is designed to emulate the collaborative decision making processes typical of medical teams. Results: Our evaluation demonstrates that combining these components within a cohesive multi-agent architecture substantially enhances operational efficiency, task completeness, and contextual relevance in healthcare scheduling workflows. Conclusions:MedScrubCrew provides a practical, implementable blueprint for healthcare automation, addressing significant inefficiencies in real-world appointment scheduling and patient triage scenarios. 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

Due to increased container traffic, the problems of congestion at terminal gates generate serious air pollution and decrease terminal efficiency. To address this issue, many terminals are implementing a truck appointment system (TAS) based on several concepts. Our work addresses gate congestion at a container terminal. A conceptual model was developed to identify system components and interactions, analyzing container flow from both static and dynamic perspectives. A truck appointment system (TAS) was modeled to optimize waiting times using a non-stationary approach. Compared to existing methods, our TAS introduces a more adaptive scheduling mechanism that dynamically adjusts to fluctuating truck arrivals, reducing peak congestion and improving resource utilization. Unlike traditional static appointment systems, our approach helps reduce truckers’ dissatisfaction caused by the deviation between the preferred time and the assigned one, leading to smoother operations. Various genetic algorithms were tested, with a hybrid genetic–tabu search approach yielding better results by improving solution stability and reducing computational time. The model was applied and adapted to the Port of Casablanca using real-world data. The results clearly highlight a significant potential to enhance sustainability, with an annual reduction of 785 tons of CO₂ emissions from a total of 1281 tons. Regarding trucker dissatisfaction, measured by the percentage of trucks rescheduled from their preferred times, only 7.8% of arrivals were affected. This improvement, coupled with a 62% decrease in the maximum queue length, further promotes efficient and sustainable operations. Full article

Background: This study evaluates one of the five regions of the state of Rio de Janeiro, Brazil, as part of a broader research project examining users’ perceptions of the Unified Health System (SUS), which has already generated publications in previous phases. The aim was to assess users’ perceptions of the SUS regarding access to and the quality of public health services, including pharmaceutical services, in the Metropolitan Region of Rio de Janeiro State. Method: A cross-sectional study was conducted between January and August 2024 with 200 participants, using a 66-item survey addressing access to and the quality of SUS services, appointment scheduling, medication acquisition, and the pharmacist’s role. Associations between variables were investigated using the Pearson Chi-Square Test in R software. Results: Frequent SUS users rated access as very good/good (p = 0.002) and overall quality as very good/good (p = 0.045). Reported challenges included the need for improved infrastructure (48.5%), better professional qualifications (30.6%), and easier access to medicines (16.8%). Higher ratings were given by those who used the SUS more frequently, and, in general, there was a tendency for participants with lower socioeconomic conditions to provide more favorable assessments of access to public health services (p = 0.024). Conclusions: A universal health system should cover diverse regions with unique needs. However, 49.4% of participants stated they never received information on how to store their medicines, and 42.3% reported never encountering a pharmacist in public pharmacies. Further ongoing studies assessing user perceptions are essential to ensure users play a central role in health decision-making, contributing to the system’s strengthening and improvement. Full article

Background/Objectives: Nonattendance at healthcare appointments remains a major challenge, particularly in chronic diseases like diabetes. Dietary therapy is essential in diabetes care, yet nonattendance at dietitian appointments persists. This study aimed to identify key drivers of nonattendance at dietitian appointments, explore prior experiences with dietary counseling, and determine factors motivating attendance. Methods: A mixed-methods approach was used in this quality improvement project, drawing on multiple data sources to explore nonattendance at dietitian appointments. This included combining a retrospective analysis of clinical and attendance data from patient records at a Danish outpatient diabetes clinic with semi-structured interviews with 25 individuals who had recently missed a dietitian appointment. Quantitative and qualitative data were analyzed separately and then integrated to characterize overall nonattendance patterns. Interview data were analyzed using systematic text condensation. Results: Individuals who missed dietitian appointments were also more likely to miss other healthcare appointments. Vulnerable individuals (i.e., those with complex health conditions or mental health issues) were more likely to miss appointments. Four principal barriers to attendance were identified: administrative, digital, and logistical challenges; competing health concerns; personal priorities; and unclear referral communication and patient involvement. Conclusions: Improving attendance at dietitian appointments requires a multifaceted approach. Key recommendations include optimizing scheduling practices, implementing digital reminders, offering continuity of care and virtual consultation options. Referring clinicians should improve patient communication by clearly explaining the purpose of the dietitian consultation and involving patients in shared decision-making prior to referral. Dietitians should collaborate with patients to establish realistic, personalized goals to foster engagement in their diabetes management. Full article

Introduction: The rapid growth of mobile health (mHealth) applications has revolutionized healthcare delivery worldwide. These digital tools encompass a broad array of functionalities, including telemedicine, appointment scheduling, medication management, and health data tracking, all of which contribute to enhanced healthcare accessibility, increased patient engagement, and improved operational efficiency. However, despite their increasing prominence, the design, deployment, and use of mHealth applications continue to face several challenges, such as usability issues and overall sustained adoption. Objectives: This study aims to evaluate mHealth applications in Saudi Arabia, focusing on their design characteristics, usability features, and current feature gaps. Method: A total of 21 mHealth applications were selected and analyzed using a thematic analysis approach. The apps were selected based on usage popularity in the Saudi market and relevance to national digital health strategies. Data were drawn from publicly available app store information, official app documentation, and expert evaluations. Results: The findings reveal that while mHealth applications excel in areas such as telemedicine, appointment booking, and health education, there are notable gaps in features such as behavior modification, patient monitoring, and health management. Conclusions: This study contributes to the growing body of research on mHealth by offering grounded insights into the functional landscape of digital health tools in Saudi Arabia. It also outlines practical recommendations to enhance usability, feature diversity, and alignment with evolving healthcare needs in Saudi Arabia and beyond. Full article

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

In 2022, scientific societies agreed on a document with recommendations for a comprehensive diagnosis of viral hepatitis (B, C, and D). The aim was to evaluate the situation in Spain regarding the comprehensive diagnosis of viral hepatitis in a single blood draw before it is recommended. A panel of experts prepared a structured survey directed at hospitals (public or private with teaching accreditation) with ≥200 beds (sent 20 October 2022, closed 1 December 2022). The response rate was 61% (79/129; 52 hospitals with >500 beds). Among the participating hospitals, all could perform tests for HBsAg, anti-HCV, and HIV serology; 94% could perform PCR testing for HCV, 63% could test for anti-HDV, and 28% could test for HDV-RNA (67% [53/79] outsourced this testing). Point-of-care (POC) testing availability was low (24%), with 84% of these tests being supervised by the reference microbiological laboratory and the results being registered in the patients’ medical history. Ninety percent of the centers carried out the diagnosis in a single step (99% HCV, 70% HBV, 48% HDV, and 44% HBV-HDV). In addition, 77% used some communication strategy when an active infection was encountered (100% HCV, 49% HBV, and 31% HDV). Only 20% had an automated system for scheduling a specialist physician appointment. Most hospitals had the means for a comprehensive diagnosis of viral hepatitis in a single sample, but <50% could test for HBV/HDV. Alerts for continuity of care were available for HCV, but not HBV or HDV. POC device implementation is important for decentralized testing. Full article

Objectives: This study aim to determine the characteristics, roles, responsibilities, and challenges of informal caregivers for adult patients in Saudi Arabia. Methods: Adult patients who have informal caregivers were invited to participate in a cross-sectional study. The inclusion criteria were patients who were 18 years old or older and permanent Saudi residents. A self-administered online questionnaire was used to identify patients’ demographics, roles, responsibilities, and care challenges. Data collection lasted four months. Percentages, means, and standard deviations were reported in the analysis. Results: The study included 276 participants, mostly female (68.8%), with a mean age of 55.21 years (SD = 20.3). Over half were married (56.2%) and not employed (81.9%). Common chronic diseases were diabetes and hypertension, with 55.8% using up to five medications. Caregivers were mainly sons or daughters (62%) living with the patient (84.1%). The top caregiver tasks were escorting patients to appointments (63.4%), scheduling doctor appointments (60.1%), and tracking medication refills (59.4%). Common challenges included caregivers lacking time (45.3%), inconsistent care (35.9%), financial constraints (27.5%), and caregivers missing doses (27.9%). The top not encountered challenges were inappropriate medication storage (78.3%), communication barriers (74.3%), improper disposal of injections (72.5%), medication management errors (71.4%), and lack of empathy (70.3%). Conclusion: This study highlights the vital role of informal caregivers in managing chronic illnesses in Saudi Arabia. Informal caregivers face challenges such as time constraints and financial limitations. The findings emphasize the need for better support systems, including training programs and improved access to healthcare resources, to enhance care quality for patients. Full article

Background/Objectives: Children with Down syndrome (DS) have distinct oral care needs and challenges, yet research on their care experiences, exploring caregiver and provider perspectives, is limited. Therefore, this study aimed to describe the barriers and facilitators to oral care for children with DS, as reported by caregivers and dental professionals. Methods: In this qualitative inquiry, semi-structured questions were used to elicit narratives describing oral care experiences from one caregiver focus group (n = 5), individual caregiver interviews (n = 9), and individual dentist interviews (n = 8). The transcripts were coded and thematically analyzed. Results: Three themes emerged in both groups. The first theme, Access, described the challenges in locating a dentist willing and knowledgeable about how to treat children with DS, and the variability in experiences between different contexts (i.e., community-based vs. specialty clinics). The second theme, Pre-visit Preparation, noted the potential impact of dental trauma on dental visits and recommended the use of preparation strategies, such as desensitization appointments, strategic scheduling, and visual or verbal scripts or social stories, to introduce dental encounters. The final theme, Dental Encounters, dealt with the importance of communication and interpersonal connection, as well as concerns about and support for active/passive immobilization techniques and pharmacological intervention. Sensory strategies for auditory, tactile, and vestibular input were discussed, in addition to distraction techniques, the timing and pacing of dental encounters, and parental presence/absence. Conclusions: Tailoring dental care around the unique sensory and behavioral needs of children with DS and building effective partnerships between children, parents, and dentists were emphasized for optimizing the dental care experiences of children with DS. Full article

Background/Objectives: Appointment planning in physical therapy (PT) is crucial for optimizing patient outcomes and resource efficiency, yet determinants of these plans and deviations from them remain underexplored. This study aimed to explore how physical therapists in Saudi Arabia determine appointment numbers, their preferred planning methods, and the prevalence and contributing factors of deviations from planned appointments. Methods: A cross-sectional observational study was conducted using an electronic questionnaire distributed to PTs practicing in outpatient departments and homecare settings across Saudi Arabia. Descriptive statistics were used to summarize therapists’ methods, preferences, and the prevalence of and potential reasons for deviations. Results: A total of 434 responses were collected. Most therapists (66%) relied on their evaluation to determine the number of appointments, and this was their preferred method (76%). However, 50% reported patients usually requiring more appointments than initially planned, and 14% did not complete all the planned appointments. Faster-than-expected progress (61%) and slower-than-expected progress (58%) were the primary reasons for deviations. Conclusions: Despite most therapists determining the number of appointments based on their evaluation, the majority reported usual deviations from planned appointments, highlighting a gap in appointment planning. Future research should investigate the impact of deviations on patient outcomes and healthcare costs. Strategies to reduce deviations, such as improving adherence to clinical practice guidelines (CPGs), are warranted. Full article

Background/Objectives: Long waiting times for outpatient care remain a global challenge for public health systems. In Portugal, the National Health Service (NHS) ensures universal access to medical treatment, aiming to promote equity in healthcare. However, persistent delays in outpatient speciality appointments hinder this objective. Methods: This study proposes a prioritization-scheduling approach that integrates a mathematical model with a heuristic method to enhance accessibility in NHS hospitals. By optimizing the available capacity of hospitals within each geographic area, the model efficiently sequences patient appointments across different facilities, prioritizing those who have waited the longest. The approach was tested using simulated instances based on real NHS hospital data. Results: Results indicate that the model effectively integrates hospital resources within a region and efficiently allocates specialist appointments, significantly reducing waiting times. Conclusions: This research introduces a promising strategy that, when incorporated into a decision support system, can serve as a valuable tool for healthcare management. Full article