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This paper presents the development of a dynamical tropical algebra-based model of a vaccination center, which can be used to control and optimize the admission of users during center's operation. In addition, an analysis of closed-loop control methods designed to maximize the system performance in terms of service rate and minimize users' waiting time, while observing occupancy constraints due to social distancing protocols recommended by sanitary authorities due to Covid epidemic, is presented. © 2023, Brazilian Society for Automatics--SBA.
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Over fourteen million people suffer from neuromuscular diseases in the UK such as strokes, spinal cord injuries, and Parkinson's disease etc. That means at least one in six people in the UK are living with one or more neurological conditions. In order for patients to return to normal life sooner, a rigorous rehabilitation process is needed. In hospitals, physiotherapists and neurological experts prescribe specific neurorehabilitation exercises. In most cases, patients need to schedule an appointment to receive treatment in a hospital or to have physiotherapists visit them at home. The number of neuromuscular patients has increased, resulting in longer hospital waiting times. In particular, during COVID-19, patients were not allowed to visit hospitals or have physiotherapists visit them due to government restrictions. Online guides for personalised and custom rehabilitation therapy for joint spasticity and stiffness are also not available. This paper reports the development of an IoT-based prototype system that monitors and records joint movements using sensory footwear (consisting of FSR and IMU sensors) and Kinect sensors. In addition, a prototype web portal is also being developed to record performance data during exercises at home and interact with clinicians remotely. A pilot study has been conducted with six healthy individuals and test results show that there is a strong correlation between Kinect data and FSR data in terms of coordination between joint movements. © 2022 IEEE.
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Background and Objectives: Triage systems help provide the right care at the right time for patients presenting to emergency departments (EDs). Triage systems are generally used to subdivide patients into three to five categories according to the system used, and their performance must be carefully monitored to ensure the best care for patients. Materials and Methods: We examined ED accesses in the context of 4-level (4LT) and 5-level triage systems (5LT), implemented from 1 January 2014 to 31 December 2020. This study assessed the effects of a 5LT on wait times and under-triage (UT) and over-triage (OT). We also examined how 5LT and 4LT systems reflected actual patient acuity by correlating triage codes with severity codes at discharge. Other outcomes included the impact of crowding indices and 5LT system function during the COVID-19 pandemic in the study populations. Results: We evaluated 423,257 ED presentations. Visits to the ED by more fragile and seriously ill individuals increased, with a progressive increase in crowding. The length of stay (LOS), exit block, boarding, and processing times increased, reflecting a net raise in throughput and output factors, with a consequent lengthening of wait times. The decreased UT trend was observed after implementing the 5LT system. Conversely, a slight rise in OT was reported, although this did not affect the medium-high-intensity care area. Conclusions: Introducing a 5LT improved ED performance and patient care.
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COVID-19 , Waiting Lists , Humans , Triage , Pandemics , Length of Stay , Emergency Service, HospitalABSTRACT
Task scheduling scenarios require the system designers to have complete information about the resources and their capabilities, along with the tasks and their application-specific requirements. An effective task-to-resource mapping strategy will maximize resource utilization under constraints, while minimizing the task waiting time, which will in-turn maximize the task execution efficiency. In this work, a two-level reinforcement learning algorithm for task scheduling is proposed. The algorithm utilizes a deep-intensive learning stage to generate a deployable strategy for task-to-resource mapping. This mapping is re-evaluated at specific execution breakpoints, and the strategy is re-evaluated based on the incremental learning from these breakpoints. In order to perform incremental learning, real-time parametric checking is done on the resources and the tasks;and a new strategy is devised during execution. The mean task waiting time is reduced by 20% when compared with standard algorithms like Dynamic and Integrated Resource Scheduling, Improved Differential Evolution, and Q-learning-based Improved Differential Evolution;while the resource utilization is improved by more than 15%. The algorithm is evaluated on datasets from different domains like Coronavirus disease (COVID-19) datasets of public domain, National Aeronautics and Space Administration (NASA) datasets and others. The proposed method performs consistently on all the datasets. © 2023 IETE.
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This study investigated the performance of rural public bus transport services in Jordan Valley during COVID-19. Jordan Valley consists of three brigades;Southern Shouneh, Deir Alla, and Northern Shouneh. The performance measures included availability, comfort and convenience, waiting time, mobility, productivity, and safety for the external and internal bus routes. The names, number of buses, and fares for bus routes were obtained from Land Transport Regulatory Commission of Jordan (LTRC). The field survey consisted of interviews with passengers and drivers in addition to direct field observations. The average waiting time for both the minibuses and microbuses at off-peak hours was found twice and half the waiting time at peak hours. The minimum and maximum values of the average speed varied between 40 to 100 km/h for the external routes and between 30-90 km/h for the internal routes. As a productivity measure, the average operating ratio for the internal routes was found 2.09 and 1.38 for the external routes. 60% of the microbuses obliged to the stated fare in comparison to minibuses in which all of them obliged to the stated fare. It was found that 40% of the external bus routes were within the range of overall Level of Service (LOS) between C & D, 26.67% within the range of LOS between B & D, 13.33% within the range of LOS between B & C, 13.33% within the range between C & E, and 6.67% within the range between D & E. Also, it was found that 60% of internal bus routes were within the range of LOS between C & D, 20% within the range of LOS between C & E, and 20% within the range of LOS C. The developed regression models between the average perception waiting time as dependent variable and travel time as independent variable were found significant at α-level < 0.05, with r2 = 0.505 at peak periods and r2 = 0.673 at off-peak period.
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Since the outbreak of Corona Virus Disease 2019 occurred in December 2019, the reduction of population mobility has curbed the spread of the epidemic to some extent but also prolonged the waiting time for the treatment of patients with gastric cancer. Based on fully understanding the different staging characteristics of gastric cancer, clinical departments should develop reasonable out-of-hospital management strategies. On one hand, reasonable communication channels should be established to allow patients to receive adequate guidance out of the hospital. On the other hand, shared decisions with patients should be made to adjust treatment strategies, and education on viral prevention should be implemented to minimize the impact of the epidemic on tumor treatment.Copyright © 2020 by the Chinese Medical Association.
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OBJECTIVES: The recent Covid-19 pandemic has burdened the healthcare facilities, especially in the presence of limited infrastructure. We aimed at applying a queuing model to the Covid-19 screening area so as to optimize the screening services and ensuring that no patient is refused the service. METHODS: The mean arrival time of patients, number of physicians, mean screening time and queue characteristics were observed and entered in the M/M/c/K queuing model using R programming to optimize the number of physicians required in the screening area. RESULTS: Considering the mean arrival of 7 patients in 10 minutes and screening of 3 patients in 10 minutes by 1 physician, 2 physicians were assigned. At this capacity, the probability of saturation of the system was 15% with patient loss rate of 1.05 per 10 minutes. Queuing simulation with 3 physicians reduced the patient loss rate to 0.013 per 10 minutes and a saturation probability of 0.2%. However, an increase of arrival rate from 10 to 20 led to an early saturation of the system. CONCLUSION: Queuing models offer an opportunity for the healthcare providers and hospital administrators to optimize patient care services, especially in critical areas with an ever-changing situation such as the current pandemic.
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The emergency department (ED) is the most important section in every hospital. The ED behaviour is adequately complex, because the ED has several uncertain parameters such as the waiting time of patients or arrival time of patients. To deal with ED complexities, this paper presents a simulation-based optimisation-based meta-model (S-BO-BM-M) to minimise total waiting time of the arriving patients in an emergency department under COVID-19 conditions. A full-factorial design used meta-modelling approach to identify scenarios of systems to estimate an integer nonlinear programming model for the patient waiting time minimisation under COVID-19 conditions. Findings showed that the S-BO-BM-M obtains the new key resources configuration. Simulation-based optimisation meta-modelling approach in this paper is an invaluable contribution to the ED and medical managers for the redesign and evaluates of current situation ED system to reduce waiting time of patients and improve resource distribution in the ED under COVID-19 conditions to improve efficiency. Copyright © 2023 Inderscience Enterprises Ltd.
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Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimising costs, the expected completion time, population travel time, and waiting time. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade-offs between solution cost, completion time, population travel time, and waiting time. © 2023 The Operational Research Society.
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Covid-19 is more likely to spread in campus than it in other places because students live together without masks. In this case, it is necessary to take nucleic acid tests in a unified time regularly. To make nucleic acid tests efficient and convenient to manage students and the testing time, this article would apply queuing theory to design a nucleic acid tests queuing system by using the data from Sanda University in April 2022. According to the special conditions on campus, such as course schedule, students' daily activities, and campus management, students would be grouped by several management styles. The system would calculate the start time and waiting time for each group and would strive to take nucleic acid tests in an orderly manner with minimal waiting time. © 2022 IEEE.
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BACKGROUND: Waiting for a long time to make payments in outpatient wards and long queues of insured patients at the checkout window are common in many hospitals across China. To alleviate the problem of long queues for payment, many hospitals in China have established various mobile apps that those without health insurance can use. However, medically insured outpatients are still required to pay manually at the checkout window. Therefore, it is urgent to use information technology to innovate and optimize the outpatient service process, implement mobile payment for medically insured outpatients, and shorten the waiting time for outpatients, especially in the context of the COVID-19 epidemic. Furthermore, smartphone-based mobile payment for outpatients with health insurance could be superior to on-site cashier billing. OBJECTIVE: This study aimed to investigate the impact of smartphone-based mobile payment in relation to different aspects, such as waiting time, satisfaction with patients' waiting time, payment experience, the proportion of those dissatisfied with payment, total outpatient satisfaction, and outpatient volume, and compare mobile payment with on-site payment. METHODS: This was a historically controlled study. This study analyzed the outpatients' waiting time to make a medical insurance payment, their satisfaction with the waiting time and payment experience, the proportion of those dissatisfied with payment, and the outpatient volume of patients at Guangzhou Women and Children's Medical Center 1 year before and after the implementation of mobile payment for medical insurance in January 2021. An independent sample 2-tailed t test was used to compare waiting time, satisfaction with waiting time, and overall satisfaction. Paired sample 2-tailed t test was used to compare monthly outpatient visits. The chi-square test was used to compare the percentages of patients dissatisfied with payment. RESULTS: After the implementation of mobile payment for medical insurance outpatients, the patients' payment waiting time was significantly shortened (mean 45.28, SD 10.35 min vs mean 1.02, SD 0.25 min; t9014=53.396; P<.001), and satisfaction with waiting time and payment experience were significantly improved (mean 82.08, SD 3.17 vs mean 90.36, SD 3.45; t9014=-118.65; P<.001). Dissatisfaction with payment significantly decreased (10.27%, SD 2.18% vs 1.19% vs SD 0.30%; P<.001). The total satisfaction of outpatients significantly improved (mean 86.91, SD 3.23 vs mean 89.98, SD 3.31; t9014=-44.57; P<.001), and the outpatient volume increased (248,105.58, SD 89,280.76 vs 303,194.75, SD 53,773.12; t11=2.414; P=.03). Furthermore, payment efficiency improved, and the number of the on-site cashiers substantially decreased. CONCLUSIONS: Mobile payment for health insurance significantly shortened patients' payment waiting time; improved patient satisfaction on waiting time and payment experience and overall satisfaction; reduced the proportion of patients who were dissatisfied with payment and the cashier at the hospital; and increased monthly outpatient volume. This approach was effective and thus worthy of promoting.
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In this paper we investigate the public transport trip frequency variations, as well as the reasons that led to the shift away from public transport means, due to the COVID-19 pandemic. We studied relevant data from the Moovit platform, and we compared operational and trip frequency characteristics of public transport systems before and after the outbreak of the pandemic in 87 cities worldwide. On average, waiting times at public transport stops/stations increased while trip distances decreased, apparently due to the mobility restriction and social distancing measures implemented in 2020. Most of the Moovit users who said that they abandoned public transport in 2020 were found in Italy and Greece. We developed linear regression analysis models to investigate (among the 35 variables examined in the study) the relationship between public transport abandonment rates and socioeconomic factors, quality of service characteristics, and indicators of pandemic's spread. Empirical findings show that public transport dropout rates are positively correlated with the COVID-19 death toll figures, the cleanliness of public transport vehicles and facilities, as well as with the income inequality (GINI) index of the population, and thus reconfirm previous research findings. In addition, the waiting time at stops/stations and the number of transfers required for commute trips appeared to be the most critical public transport trip segments, which significantly determine the discontinuation of public transport use under pandemic circumstances. Our research findings indicate specific aspects of public transport services, which require tailored adjustments in order to recover ridership in the post-pandemic period.
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Online popular restaurants are those that are widely concerned by the society and sought after by the public through we media platform or internet marketing. Online comment is the product of the information age. The daily life of Internet users is to exchange information, express views and communicate with others through major Internet platforms. The outbreak of COVID-19 in 2020 has hit the catering industry in China. According to the statistics of the existing literature, it is found that there are few studies on online popular restaurants, and the research methods are relatively simple and traditional. The research on online comments of online popular restaurants can explore the emotional tendency of consumers, find the problems existing in online popular restaurants and put forward corresponding development suggestions. This paper uses Python technology to obtain the comments of 30 popular restaurants in Dalian on the public comment website, and puts forward corresponding opinions and suggestions on the operation of online popular restaurants through data mining. It is concluded that consumers care about the following aspects in the consumption process: taste, service, decoration style, waiting time in line. In this regard, we put forward the following suggestions: improve the taste of food, constantly push through the old and bring forth the new, and the primary task for the sustainable development of the restaurant is to ensure the taste;Improve service quality and create a high-quality service culture;Create a unique decoration style and resolutely resist and crack down on piracy;Reduce waiting time or provide better service during waiting time. © 2022 SPIE.
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First, this paper analyzes the congestion of container ports at home and abroad under the current epidemic situation, then takes Yantian port of Shenzhen as the key research object to analyze the waiting time at anchorage and the stopping time at berth of the main container ports in China based on the data statistics. It studies the anchorage demand of ships with random and fluctuating arrival based on queuing theory. It gives the relationship between the stopping time of ship at berth and the maximum waiting time of the arriving ship, and reveals that the increase of ship stopping time and uneven arrival at the port are the main factors causing the current container port congestion, and puts forward some countermeasures to alleviate the port congestion. © 2022 SPIE
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Background and Objectives: Medical imaging is a key element in the clinical workup of patients with suspected oncological disease. In Hungary, due to the high number of patients, waiting lists for Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were created some years ago. The Municipality of Budapest and Semmelweis University signed a cooperation agreement with an extra budget in 2020 (HBP: Healthy Budapest Program) to reduce the waiting lists for these patients. The aim of our study was to analyze the impact of the first experiences with the HBP. Material and Methods: The study database included all the CT/MRI examinations conducted at Semmelweis University with a referral diagnosis of suspected oncological disease within the first 13 months of the HBP (6804 cases). In our retrospective, two-armed, comparative clinical study, different components of the waiting times in the oncology diagnostics pathway were analyzed. Using propensity score matching, we compared the data of the HBP-funded patients (n = 450) to those of the patients with regular care provided by the National Health Insurance Fund (NHIF) (n = 450). Results: In the HBP-funded vs. the NHIF-funded patients, the time interval from the first suspicion of oncological disease to the request for imaging examinations was on average 15.2 days shorter (16.1 vs. 31.3 days), and the mean waiting time for the CT/MRI examination was reduced by 13.0 days (4.2 vs. 17.2 days, respectively). In addition, the imaging medical records were prepared on average 1.7 days faster for the HBP-funded patients than for the NHIF-funded patients (3.4 vs. 5.1 days, respectively). No further shortening of the different time intervals during the subsequent oncology diagnostic pathway (histological investigation and multidisciplinary team decision) or in the starting of specific oncological therapy (surgery, irradiation, and chemotherapy) was observed in the HBP-funded vs. the NHIF-funded patients. We identified a moderately strong negative correlation (r = -0.5736, p = 0.0350) between the CT/MR scans requested and the active COVID-19 case rates during the pandemic waves. Conclusion: The waiting lists for diagnostic CT/MR imaging can be effectively shortened with a targeted project, but a more comprehensive intervention is needed to shorten the time from the radiological diagnosis, through the decisions of the oncoteam, to the start of the oncological treatment.
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COVID-19 , Waiting Lists , Humans , Retrospective Studies , Hungary , COVID-19/diagnostic imaging , Tomography, X-Ray Computed , Magnetic Resonance Imaging/methods , COVID-19 TestingABSTRACT
The COVID-19 pandemic has disrupted the normal operations of countries around the world, which applied different containment and mitigation policies, such as mask-wearing, social distancing, quarantine, and lockdowns, to limit the spread of the virus. More recent mitigation efforts include vaccination strategies, since various vaccines have been authorized for emergency use for the prevention of COVID-19. In fact, vaccination is one of the best proactive mitigation strategies against the virus spread. Mass vaccination strategies have been undertaken by multiple research and development teams in the past when the public needed to be vaccinated on a large scale due to a pandemic, such as the seasonal flu or H1N1. Drive through vaccination, in particular, is more convenient and safer than walk-in vaccinations in clinics due the nature of the contagious virus. In this paper, we present the implementation of a discrete event simulation model of a drive through clinic for mass vaccinations of patients, while prioritizing the senior population. The simulation output is examined in terms of average waiting time in the queue to get vaccinated, number of patients getting vaccinated per week, and utilization of the medical resources. The results are expected to provide insights into the allocation of medical resources across lanes and prioritization strategies for the senior population to achieve higher vaccination rates, while reducing the waiting time in queue. © 2021 The Authors.
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Supply Chain Management (SCM) has grown in the last years due to the changing and evolutionary environment. The SCM has become fundamental for gaining financial and social, among others, benefits. Currently, the traditional SCM mechanisms have some areas of improvement, such as the transparency and lack of information, long waiting times for information retrieval and data fidelity. The past months our daily lives have been changing in a drastic way due to COVID-19 and some industries were affected but with this change there has been some opportunities to improve. In this article we describe the main concepts about the blockchain technology, the smart contracts, and their main use. We talk about different areas that these technologies could help to improve in different ways of their internal and external processes. Our main discussion is the description and improvement in the performance inside a supply chain, how it could be possible to speed up the information sharing, how a smart contract could be applied to a consumer-supplier relationship in the healthcare supply chain and the benefits that it would bring to it due to the quick times that this area demands because of the nature of their transactions and operations that are critical to maintain the needs of the industry. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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We consider the dynamic scheduling of skip-stop patterns in public transportation. Operators of fixed-line public transportation services seek to reduce their vehicle running times by skipping stops that will not impact significantly the waiting times of passengers. This can result in an improved trade-off between vehicle running times and passenger waiting times allowing to slightly increase the travel times of passengers in order to reduce the operational costs. Although there exist several decision support models for dynamic stop-skipping, these models do not consider the impact of skipped stops to in-vehicle crowding. That is, apart from the increased passenger waiting times, a skipped stop might result in an increased amount of passengers boarding the next trip of the line resulting in overcrowding. To rectify this, we propose a mixed-integer nonlinear model that incorporates the objective of avoiding in-vehicle overcrowding when making stop-skipping decisions. This is particularly relevant in situations where passengers are no longer able to find a seat or they have to maintain social distancing inside the vehicle because of a pandemic. Because the stop-skipping problem is NP-Hard, we introduce a number of valid inequalities that tighten its solution space and we demonstrate the performance of our model in benchmark problem instances. © 2022 IEEE.
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We aimed to determine the pattern of delay and its effect on the short-term outcomes of total gastrectomy before and during the coronavirus disease 2019 (COVID-19) pandemic. Overlaid line graphs were used to visualize the dynamic changes in the severity of the pandemic, number of gastric cancer patients, and waiting time for a total gastrectomy. We observed a slightly longer waiting time during the pandemic (median: 28.00 days, interquartile range: 22.00-34.75) than before the pandemic (median: 25.00 days, interquartile range: 18.00-34.00; p = 0.0071). Moreover, we study the effect of delayed surgery (waiting time > 30 days) on short-term outcomes using postoperative complications, extreme value of laboratory results, and postoperative stay. In patients who had longer waiting times, we did not observe worse short-term complication rates (grade II-IV: 15% vs. 19%, p = 0.27; grade III-IV: 7.3% vs. 9.2%, p = 0.51, the short waiting group vs. the prolonged waiting group) or a higher risk of a longer POD (univariable: OR 1.09, 95% CI 0.80-1.49, p = 0.59; multivariable: OR 1.10, 95% CI 0.78-1.55, p = 0.59). Patients in the short waiting group, rather than in the delayed surgery group, had an increased risk of bleeding in analyses of laboratory results (plasma prothrombin activity, hemoglobin, and hematocrit). A slightly prolonged preoperative waiting time during COVID-19 pandemic might not influence the short-term outcomes of patients who underwent total gastrectomy.
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Background: The higher demand for surgical services during the advancement of the COVID-19 pandemic has resulted from the need for a pre-admission negative result, the need for extra resources, and a shortage of skilled expertise. This quality improvement project aimed to reduce the in-hospital preoperative waiting time of elective cases to less than 24 hours. Methods: The study was conducted in a tertiary care center. Following the collection of baseline data, we formed a multidisciplinary team to analyze the root causes and intervention ideas of delay using fishbone and driver diagrams, respectively. We prioritize key drivers and implemented several low-cost interventions using Plan-Do-Study-Act (PDSA) model. We monitored the average in-hospital preoperative waiting time of patients. Results: Overall, in-hospital preoperative waiting time for elective cases has been reduced from a baseline of 4.89 days to 1.32 days on average by the end of 10 months of initiating the project. Similarly, monthly elective case cancellation rate due to COVID-19-related reason has been reduced from baseline 62.5% of the total cancellation to 0%. Due to this, the average monthly inpatient bed utilization has increased from 2.21 patients per month during pre-COVID-19 period to 5.9 patients per month in each bed of the surgical ward by the end of the project. Conclusion: The implementation of a quality improvement project can optimize operation theatre efficiency, inpatient bed utilization, and reduce the surgical backlog. Meticulous and rigorous effort has to be laid down to do root cause analysis, generate feasible change ideas, and continuous follow-up, and testing of multiple PDSA cycles is required to impact an improvement and sustain it in the long run. The emergence of COVID-19 pandemic could be used as an opportunity to reduce the length of stay in the hospital.