Boarding Time Research Articles

Performance of screening of contacts of COVID-19 cases in same flight

Objective: To understand the performance of 2019-nCoV nucleic acid detection in screening of contacts of COVID-19 cases in same flights and provide evidence for the effective screening of persons at high risk for the infection in domestic flights. Methods: The information of passengers who took same domestic flights with COVID-19 cases in China from April 1, 2020 to April 30, 2022 were retrospectively collected,and χ2 test was used to analyze positive nucleic acid detection rates in the passengers in different times before the onsets of the index cases, in different seat rows and in epidemic periods of different 2019-nCoV variants. Results: During the study period, a total of 433 index cases were identified among 23 548 passengers in 370 flights. Subsequently, 72 positive cases of 2019-nCoV nucleic acid were detected in the passengers, in whom 57 were accompanying persons of the index cases. Further analysis of the another 15 passengers who tested positive for the nucleic acid showed that 86.67% of them had onsets or positive detections within 3 days after the diagnosis of the index cases, and the boarding times were all within 4 days before the onsets of the index cases. The positive detection rate in the passengers who seated in first three rows before and after the index cases was 0.15% (95%CI: 0.08%-0.27%), significantly higher than in the passengers in other rows (0.04%, 95%CI: 0.02%-0.10%, P=0.007),and there was no significant difference in the positive detection rate among the passengers in each of the 3 rows before and after the index cases (P=0.577). No significant differences were found in the positive detection rate in the passengers, except the accompanying persons, among the epidemics caused by different 2019-nCoV variants (P=0.565). During the Omicron epidemic period, all the positive detections in the passengers, except the accompanying persons, were within 3 days before the onset of the index cases. Conclusions: The screening test of 2019-nCoV nucleic acid can be conducted in the passengers took the same flights within 4 days before the onsets of the index cases on board. Passengers who seated within 3 rows from the index cases can considered as the close contacts at high risk for 2019-nCoV, for whom screening should be conducted first and special managements are needed. The passengers in other rows can be classified as general risk persons for screening and management.

Predicting bus ridership based on the weather conditions using deep learning algorithms

This study proposes a new approach to predict bus ridership based on the weather conditions while accounting for additional factors such as large events, holidays, and bus cancellations. For this purpose, a deep learning algorithm - multilayer perceptron (MLP) networks - is implemented using smart-card data from the bus network in the Twente Region in the Netherlands. The prediction was carried out under three scenarios: (1) without the weather conditions (base model), (2) with the weather conditions of the same time as the boarding time into buses, and (3) with the weather conditions of an hour ahead of boarding time into buses. The results showed that the application of the MLP is very promising in forecasting bus ridership considering the meteorological parameters. The average errors were improved by 4.9% on weekdays and 2.8% on weekends as a result of including the meteorological parameters in the models. The improvements were even more prominent on weekdays with moderate to extreme weather conditions (for instance, heavy precipitation, strong wind speed, and low temperature). However, the models showed higher errors during the morning peak hours on days with heavy rainfall and strong wind speed.

Impact of Access Block Is Increasing Mortality in Emergency Departments in Nepal

Introduction:Emergency patients have to stay in the Emergency Department (ED) for hours to days to get ward admission for definite care in Nepal. Access block is a major issue in the ED of University Level Teaching Hospitals (ULTH) in Nepal. This study aimed to analyze the impact of access blocks in the EDs of Nepal.Method:Meta-analysis of different publications on ‘duration of ED Boarding’ and ‘mortality outcome of ED’ of ULTHs of Nepal till November 15, 2022Results:9.7% of ED patients were admitted to the ward. The time period from ED arrival to respective ward team consultation is 5.7 hours, consultation to ward admission is 5.6 hours, and admission to ward transfer is 8 hours. The average ED boarding time is 18.1 hours. 38% of patients arrived in ED via Ambulances. The time period from ED arrival to ward team consultation for those who need transfer to another center are 6.9 hours, consultation to admission is 5.7 hours, and admission to transfer is 8.7 hours (ED Boarding time 21.3 hours). Meta-analysis of three major ULTH’s showed mortality with respect to ED boarding time to be 17% in < 1 hour, 40.4% in 1 - 6 hours, 27.4% in 6-12 hours, 9.1% in 12-24 hours, 4% in 24-28 hours and 2.1% in >48 hours. Among them, higher age, greater mortality rate. The immediate causes of mortality comparing 2018 vs 2010 are Sepsis & Septic shock 32.2% vs 18%, Cardiac Causes 21.8% vs 14.8%, Aspiration Pneumonia 19.5% vs 14.8%, Severe Lung Diseases 12.7% vs 16.4%, Hypovolumic & Haemorrhagic Shock 9.2% vs 34.4% and Poisoning 4.6% vs 1.6%.Conclusion:Prolonged ED boarding due to Access Block is triggering increased mortality in the ED.

소셜미디어에 나타난 공항 바이오인증 서비스에 대한 일반인의 인식: 텍스트마이닝 기법 활용

The importance of security in the aviation sector has continued to be emphasized since the September 11 attacks. And recently, the development of science and technology has introduced a new technology called bio-metric by combining airport security and service fields. This study confirmed the perception of the general public using air travel related to airport bio-metric. In the process, text mining techniques were used instead of using questionnaires, which were widely used in previous studies. Main keywords were collected from Naver, Daum, and Google using the Textom 4.0 program that can analyze Korean. The collected words were frequency analysis, TF-IDF analysis, centrality analysis, and CONCOR analysis. The results of the CONCOR analysis are as follows. Group 1: Airport bio-metric activated after COVID-19, Group 2: Airport that can register bio-metric, Group 3: Shortening boarding procedures and times, Group 4: Korean air Self-boarding, Group 5: Airport-linked services, Group 6: Mobile driver"s license available at the airport, Group 8: Resident registration card that can be issued on smartphones. Based on these results, this study confirmed the general public"s perception related to airport bio-metric and suggested implications for academia and industry.

Sepsis mortality and ICU length of stay after the implementation of an intensive care team in the emergency department.

Emergency department patient boarding is associated with hospital mortality and increased hospital length of stay. The objective of thepresent study is to describe the impact of deploying an Intensive Care team in the ED and its association with sepsis mortality and ICU length of stay. Patients admitted to ICU through theED with an ICD-10 CM diagnosis of sepsis were included. Preintervention and postintervention phases included 4 and 15months, respectively. Sepsis time zero, SEP-1 compliance, and lag time from time zero to antibiotic administration were compared. Outcomes of interest were mortality and ICU LOS. 1021 septic patients were included. Sixty-six percent fulfilled compliance with 3h SEP-1 bundle. Lag time from time zero to antibiotic administration was 75min. Multivariate analysis showed no association between ICU team in the ED and hospital mortality (Log OR 0.94, CI 0.67-1.34; p = 0.73). The ICU team in the ED was associated with prolonged ICU LOS (Log OR 1.21, CI 1.13-1.30; p < 0.01). Septic shock and ED boarding time were associated with prolonged ICU LOS. Compliance with SEP-1 bundle was associated with its reduction. Implementation of an ICU team in the ED for the treatment of septic patients during high volume hospitalizations is not associated with a reduction of mortality or ICU LOS.

A fully digital coincidence Doppler broadening spectrometer based on FPGA

In this article, we implemented a fully digital Coincidence Doppler Broadening (CDB) spectrometer consisting of two HPGe detectors, a fully digital processing and acquisition board, and a dedicated software system. The Field Programmable Gate Array (FPGA) on the board extracts energy and time stamp of digitized pulses from HPGe detectors, and communicates with the dedicated software by which the single channel spectrums and 2D coincidence spectrum were produced. We designed an instruction set dedicated to the CDB Spectrometer and the controller module in FPGA so that the FPGA subsystem is fully controllable and configurable. The trapezoidal pulse shaping algorithm was implemented in FPGA whose arguments are all configurable through the dedicated software by the instruction set. We examined the long-term stability of the spectrometer by 511 keV peak position of both channels. The 2D Gaussian fitting is performed on coincidence spectrum by the software to correct the peak drift of both channels. The results show that this method effectively improves the Doppler-broadened spectrum, by which the peak-to-background ratio increases from 7.0 × 105 to 1.9 × 106.

Hospital Boarding Crises: The Impact of Urgent vs. Prevention Responses on Length of Stay

Healthcare policy makers use wait-time metrics to encourage hospital managers to improve patient experience. In 2002, Massachusetts mandated that hospital managers develop processes to respond to boarding crises, which occur when emergency department (ED) patients experience long waits for inpatient beds. Performance improvement theory suggests that patients would be better served by preventing boarding crises rather than responding urgently after they occur. To empirically test this theory, we use data from a Massachusetts hospital that has two physician-based processes related to boarding and patient flow. First, to comply with the state mandate, the hospital developed processes to identify when the hospital is in a boarding crisis, a code yellow (CY), and subsequently request that physicians prioritize patient discharge (urgent response). Second, physicians can use predischarge orders, optional written communication about discharge barriers, to avoid discharge delays for patients approaching discharge (prevention response). Our data supports the existence of a trade-off between these two responses. Counter to our hypothesis, the state-mandated urgent response does not have any impact on length of stay (LOS). We also find that a CY has no impact on ED hourly occupancy, marginally decreases ED wait times, and increases boarding time. The prevention response is associated with a 26% reduction in LOS. Furthermore, we find that the urgent response reduces the likelihood of physicians’ ability to use the prevention response by 27.3%. We conclude that the state policy has unintended negative consequences that stymie hospital efforts to create longer term improvement. This paper was accepted by Stefan Scholtes, healthcare management. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4724 .

ATA2023Annual Conference &amp; ExpoMarch 4–6, 2023San Antonio, Texas

ATA2023Annual Conference &amp; ExpoMarch 4–6, 2023San Antonio, Texas

PGDRT: Prediction Demand Based on Graph Convolutional Network for Regional Demand-Responsive Transport

To provide an efficient demand-responsive transport (DRT) service, we established a model for predicting regional movement demand that reflects spatiotemporal characteristics. DRT facilitates the movement of restricted passengers. However, passengers with restrictions are highly dependent on transportation services, and there are large fluctuations in travel demand based on the region, time, and intermittent demand constraints. Without regional demand predictions, the gaps between the desired boarding times of passengers and the actual boarding times are significantly increased, resulting in inefficient transportation services with minimal movement and maximum costs. Therefore, it is necessary to establish a regional demand generation prediction model that reflects temporal features for efficient demand response service operations. In this study, a graph convolutional network model that performs demand prediction using spatial and temporal information was developed. The proposed model considers a region’s unique characteristics and the influence between regions through spatial information, such as the proximity between regions, convenience of transportation, and functional similarity. In addition, three types of temporal characteristics—adjacent visual characteristics, periodic characteristics, and representative characteristics—were defined to reflect past demand patterns. With the proposed demand forecasting model, measures can be taken, such as having empty vehicles move to areas where demand is expected or encouraging adjustment of the vehicle’s rest time to avoid congestion. Thus, fast and efficient transportation satisfying the movement demand of passengers with restrictions can be achieved, resulting in sustainable transportation.

Hospital Boarding Crises: The Impact of Urgent Versus Prevention Responses on Length of Stay

Healthcare policymakers use wait-time metrics to encourage hospital managers to improve patient experience. In 2002, Massachusetts mandated that hospital managers develop processes to respond to boarding crises, which occur when emergency department (ED) patients experience long waits for inpatient beds. Performance improvement theory suggests that patients would be better served by preventing boarding crises rather than responding urgently after they occur. To empirically test this theory, we use data from a Massachusetts hospital that has two physician-based processes related to boarding and patient flow. First, to comply with the state mandate, the hospital developed processes to identify when the hospital is in a boarding crisis, a code yellow (CY), and subsequently request that physicians prioritize patient discharge (urgent response). Second, physicians can use pre-discharge orders (PDO), optional written communication about discharge barriers, to avoid discharge delays for patients approaching discharge (prevention response). Our data support the existence of a tradeoff between these two responses. Counter to our hypothesis, the state-mandated urgent response does not have any impact on LOS. We also find that a CY has no impact on ED hourly occupancy, marginally decreases ED wait times, and increases boarding time. The prevention response is associated with a 26% reduction in LOS. Furthermore, we find that the urgent response reduces the likelihood of physicians’ ability to use the prevention response by 27.3%. We conclude that the state policy has unintended negative consequences that stymie hospital efforts to create longer-term improvement.

Challenges faced in the peer review system in open access journals

Challenges faced in the peer review system in open access journals

Let the fast passengers wait: Boarding an airplane takes shorter time when passengers with the most bin luggage enter first

Airlines usually organize the passenger queue by letting certain groups of passengers enter the airplane in a specific order. The total boarding time of such airplane boarding policies can be estimated and compared by a Lorentzian metric based on the one used in Einstein’s theory of relativity. The metric accounts for aisle-clearing times that depend on the passengers’ queue positions, in particular when passengers in the back of the queue with increasing probability will have to wait for already seated aisle or middle seat passengers to rise up and let the others pass to a seat closer to the window. We provide closed-form expressions for the asymptotic total boarding time when the number of passengers is large, and prove that the best queue ordering with low congestion is according to decreasing luggage-handling time. The effect of seat interference amplifies the previously shown superiority of slow first vs. random boarding and fast first. That this ranking of policies also holds for realistic congestion is illustrated by both analytical methods and simulations, and parameters are taken from empirical data. However, the result is non-trivial, as the ranking shifts for unrealistically high congestion. Based on the analytical results, we demonstrate that the slow-first policy can be improved by dividing the passengers into more than two groups based on their number of bin luggage items, and let the slowest groups with the most luggage items enter the queue first.

The effect of increased emergency department demand on throughput times and disposition status for pediatric psychiatric patients

ObjectivesEmergency department (ED) crowding has been shown to increase throughput measures of length of stay (LOS), wait time, and boarding time. Psychiatric utilization of the ED has increased, particularly among younger patients. This investigation quantifies the effect of ED demand on throughput times and discharge disposition for pediatric psychiatric patients in the ED. MethodsUsing electronic medical record data from 1,151,396 ED visits in eight North Carolina EDs from January 1, 2018, through December 31, 2020, we identified 14,092 pediatric psychiatric visits. Measures of ED daily demand rates included overall occupancy as well as daily proportion of non-psychiatric pediatric patients, adult psychiatric patients, and pediatric psychiatric patients. Controlling for patient-level factors such as age, sex, race, insurance, and triage acuity, we used linear regression to predict throughput times and logistic regression to predict disposition status. We estimated effects of ED demand by academic versus community hospital status due to ED and inpatient resource differences. ResultsMost ED demand measures had insignificant or only very small associations with throughput measures for pediatric psychiatric patients. Notable exceptions were that a one percentage point increase in the proportion of non-psychiatric pediatric ED visits increased boarding times at community sites by 1.06 hours (95% CI: 0.20–1.92), while a one percentage point increase in the proportion of pediatric psychiatric ED visits increased LOS by 3.64 hours (95% CI: 2.04–5.23) at the academic site. We found that ED demand had a minimal effect on disposition status, with small increases in demand rates favoring <1 percentage point increases in the likelihood of discharge. Instead, patient-level factors played a much stronger role in predicting discharge disposition. ConclusionsED demand has a meaningful effect on throughput times, but a minimal effect on disposition status. Further research is needed to validate these findings across other state and healthcare systems.

A partially flexible routing strategy for assigning emergency department patients to inpatient wards

Increasing flexibility in assigning emergency department patients to inpatient wards shortens the boarding time, which is known as an important driver for emergency department overcrowding crisis, whereas high levels of flexibility damages quality of care, adversely affects staff satisfaction, and complicates the implementation. We propose a partially flexible routing strategy to balance the trade-offs between positive and negative aspects of flexibility. We consider a partially flexible two-class queue with two heterogeneous server pools as our base model and analyze it by adopting a Matrix Analytic approach and derive a closed-form expression for the performance measures of this system by utilizing probability-generating functions. Using the base model, we develop a partially flexible routing algorithm that forms a bipartite graph that is both connected and cyclic. Connectivity and cyclicity alongside other properties of this algorithm offer comparable boarding times, a high level of quality of care, simplicity in practice, and staff satisfaction. By considering a fully flexible structure, where all patients can be assigned to all inpatient wards, as a benchmark, our simulation study results show that the performance of our proposed policy is similar to a fully flexible design in reducing boarding time under various system settings.

The Impact of Bus Door Crowding on Operations and Safety

This study examines how bus design factors influence door crowding and quantifies how door crowding relates to operational performance and passenger safety. Results are based on data collected for 2,807 stops in Dhaka, Bangladesh. Door crowding is affected by multiple bus design factors, including door placement, aisle length, presence of a front seating area, and service type. Increases in door crowding are associated with longer marginal boarding times and an increased number of unsafe boarding and alighting movements that occur when the bus has not come to a complete stop. Results underscore the importance of educating conductors on the dangers associated with door crowding.

35.3 Bringing Inpatient Psychiatry to the Emergency Room

The boarding times of children and adolescents in the emergency room requiring a higher level of care due to the lack of available inpatient beds is worsening. There was an upward trend in boarding times before the pandemic, but the boarding crisis has worsened to the point of youth waiting almost 3 times as long in emergency rooms now. This presentation will highlight how boarding in the emergency room now requires initiating treatment and provision of care that is beyond what would be expected in the emergency room setting, in order to help mitigate the detrimental effects of this crisis.

137 Does Boarding Time in the Emergency Department Contribute to Mortality Among Those Who Are Mechanically Ventilated?

With emergency department (ED) crowding, poor patient flow, and overwhelmed hospital systems, ED boarding is common. Boarding time (BT) has previously been associated with numerous negative patient outcomes, including increased in-hospital mortality, hospital length of stay, and decreased ventilator free days. There is little data on the association between BT and mortality among those who receive mechanical ventilation (MV) in the ED. We hypothesized that BT was associated with increased mortality and decreased hospital free days (HFD) in subjects who received MV in the ED.

Hybrid Scheduling Model Based on Fare Incentives for Peak Time Interval of the Metro: The Harbin Metro System

Considering the operation efficiency of the metro system, the boarding queuing problem is a complex and intractable dilemma caused by the security and ticket checking process in urban metro stations, especially for the mass-transit metro system in China. In this study, a novel scheme of fare differentials based on demand control and congestion management for reducing congestion in the metro boarding process was proposed. In addition, we extended Vickrey’s point-queue model into a boarding congestion model incorporating the bidesired departure time considering the security and ticket checking process in stations as a continuum bottleneck. The train-run and departure time choosing behaviors of passengers under different schedule gaps of the adjacent train runs are explored when the boarding system achieves user equilibrium in the proposed model. Then, we examined the demand regulatory mechanisms of fare incentives (fare differentials) in reducing the queuing boarding time in metro stations when the optimization of the schedule gaps is ineffective in the pattern of mass-scale travel demand. The analytical solutions of these two optimal methods (schedule gaps and fare differentials) for boarding congestion management are presented. After comparing the two congestion-reduced methods, the fare incentive rule has a better regulation effect on the smoothing of travel demand. The results of the sensitivity analysis using numerical simulations reveal the regulatory mechanism of fare differentials in reducing the queuing time and increasing the incremental revenue. (1) The demand threshold is only related to the boarding capacity and schedule gaps (i.e., the greater the boarding capacity and schedule gaps, the greater the passenger capacity of the metro station). (2) The effect of fare incentives in reducing the boarding congestion is better if the lower fare is implemented in the later train runs. (3) A lower fare differential between two adjacent metro runs can be used to regulate the proportion of staggered passengers in the queuing line to reduce the crowd gathering in the metro station hall when travel demand is high, meanwhile, a higher fare differential between two adjacent metro shuttles can increase incremental revenue effectively. (4) The measure of fare differentials causes worse results in both the reduction of the queuing time and the increase of the incremental revenue when the metro travel demand is lower than the demand threshold. This conclusion is consistent with the pattern of reality and experience. Therefore, the definition and judgment conditions of demand thresholds, introduced in this study, can provide theoretical guidance when implementing fare incentive policies in aviation and metro networks.

Predicting Need for Hospital Beds to Reduce Emergency Department Boarding.

Introduction Accurately determining the supply and demand of hospital beds for new admissions can help prevent adverse patient outcomes. Quantitative analysis of modern electronic medical record data can help predict supply and demand for unoccupied staffed hospital beds (SEDs) and aid in eliminating human approximations, standardizing daily work through concrete and objective data. The purpose of this study was to reduce variability and human error in predicting the number of SEDs needed. Methods In this study,the authors analyzed bed calculator data from a medium-sized, suburban medical center to evaluate the efficacy of a unique bed calculator prediction tool to determine the need for SEDs. The calculator aggregates multiple key reference factors available through the bed calculator system into a cohesive linear regression model. Results Compared with human estimation, the authors found that the bed calculator is able to predict the number of SEDs needed more effectively. That being said, there was no significant difference in the average boarding times pre- and postintervention, indicating that the bed calculator did not result in decreased boarding times for patients. Discussion These findings establish the efficacy of the bed calculator and its ability to align bed supply and demand. Because patient boarding times depend on the system's patient flow management, future studies should focus on how to improve various streams of communication and coordination.

Psychiatric care in the emergency department: Converting boarding time to treatment time.

The authors declare no potential conflict of interest.