Passenger Flows in Underground Railway Stations and Platforms, MTI Report 12-43

Urban rail systems are designed to carry large volumes of people into and out of major activity centers. As a result, the stations at these major activity centers are often crowded with boarding and alighting passengers, resulting in passenger inconvenience, delays, and at times danger. This study examines the planning and analysis of station passenger queuing and flows to offer rail transit station designers and transit system operators guidance on how to best accommodate and manage their rail passengers. The objectives of the study are to: 1) Understand the particular infrastructural, operational, behavioral, and spatial factors that affect and may constrain passenger queuing and flows in different types of rail transit stations; 2) Identify, compare, and evaluate practices for efficient, expedient, and safe passenger flows in different types of station environments and during typical (rush hour) and atypical (evacuations, station maintenance/ refurbishment) situations; and 3) Compile short-, medium-, and long-term recommendations for optimizing passenger flows in different station environments

Main features and control strategies to reduce overcrowding in emergency departments. A systematic review of the literature

Purpose: Overcrowding is a problem that affects emergency departments (ED) all over the world; it occurs due to a disproportion between user demand and the physical, human and structural resources available. Essential prerequisites to assessing and managing the phenomenon are its accurate measurement and an understanding of its impact. The objective of this systematic review is to identify the characteristics of the problem, analyzing the proposed strategies aimed at improving patient flow, delay in services provided and overcrowding of emergency departments. Methods: To achieve our objectives, a manual computerized search was performed in the bibliographic databases using as keywords “Emergency Department”, “Overcrowding”, “Emergency Room”, “Emergency Service”, “Emergency Unit”“,Emergency Ward”, “Emergency Outpatient Unit”, “Emergency Hospital”, “Crowding”, “Mass Gathering”, “Management” and “Comprehensive Health Care”. Two independent reviewers analyzed abstracts, titles and full text articles for admissibility, according to the selected inclusion and exclusion criteria. Results: The process lead to include 19 articles. It was possible to group the solutions proposed in five categories: work organization, investment in primary care, creation of new dedicated professional figures, work and structural modifications and implementation of predictive simulation models using mathematical algorithms. Conclusion: The most effective measures to guarantee an improvement in the flow of patients are represented by both improving the efficiency of human resources and by developing predictive mathematical models, regardless of the type of hospital and its location. Considering the complexity of EDs and the multiple characteristics of overcrowding and that the causes of crowding are different and site-specific, a careful examination of the specifics of each ED is necessary to identify improving fields

Predictive Analytics in Practice: A Novel Simulation Application for Addressing Patient Flow Challenges in Today's Emergency Departments

Abstract Objectives: To develop a flexible software application that uses predictive analytics to enable emergency department (ED) decision-makers in virtually any environment to predict the effects of operational interventions and enhance continual process improvement efforts. To demonstrate the ability of the application's core simulation model to recreate and predict sitespecific patient flow in two very different EDs: a large academic center and a freestanding ED. To describe how the application was used by a freestanding ED medical director to match ED resources to patient demand. Methods: The application was developed through a public-private partnership between University of Florida Health and Roundtable Analytics, Inc., supported by a National Science Foundation Small Business Technology Transfer (STTR) grant. The core simulation technology was designed to be quickly adaptable to any ED using data routinely collected by most electronic health record systems. To demonstrate model accuracy, Monte Carlo studies were performed to predict the effects of management interventions in two distinct ED settings. At one ED, the medical director conducted simulation studies to evaluate the sustainability of the current staffing strategy and inform his decision to implement specific interventions that better match ED resources to patient demand. After implementation of one intervention, the fidelity of the model's predictions was evaluated. Results: A flexible, cloud-based software application enabling ED decision-makers to predict the effects of operational decisions was developed and deployed at two qualitatively distinct EDs. The application accurately recreated each ED's throughput and faithfully predicted the effects of specific management interventions. At one site, the application was used to identify when increasing arrivals will dictate that the current staffing strategy will be less effective than an alternative strategy. As actual arrivals approached this point, decision-makers used the application to simulate a variety different interventions; this directly informed their decision to implement a new strategy. The observed outcomes resulting from this intervention fell within the range of predictions from the model. Conclusion: This application overcomes technical barriers that have made simulation modeling inaccessible to key decision-makers in emergency departments. Using this technology, ED managers with no programming experience can conduct customized simulation studies regardless of their ED's volume and complexity. In two very different case studies, the fidelity of the application was established and the application was shown to have a direct positive effect on patient flow. The effective use of simulation modeling promises to replace inefficient trial-anderror approaches and become a useful and accessible tool for healthcare managers challenged to make operational decisions in environments of increasingly scarce resources

Perfecting Patient Bed Flow in the Emergency Department

Emergency department (ED) crowding is a serious problem in the United States. Crowding in the ED can result in delays that may negatively affect patient outcomes and increase the cost of care. The purpose of this project was to understand strategies that can help to improve patient flow in the ED. The plan-to-do-study act model for process improvement influenced this project. Secondary data were collected for a 2-month period to determine the impact of workflow processes (patient boarding time in ED, surge capacity and workflow processes including the impact of ancillary departments) on the movement of admitted patients from the ED to the inpatient units. Descriptive statistics were used to provide numerical summaries, frequencies, and percentages for the identified variables. The findings were consistent with an increased length of stay and longer ED boarding of patients due to the workflow process. Resulting recommendations included standardized calls for report on admitted patients within 30 minutes, timely discharge of patients, collaboration with attending physicians to facilitate evaluation of patients and orders, modification of staffing roles to ensure adequate staff, and identification of staff transporters to ensure timely transport of patients to their rooms. The findings helped to inform the development of a Bed Utilization Policy. The policy has been shared with the organization with the recommendation to implement and further evaluate to help manage bed flow. Development of utilization strategies that contribute to facilitating throughput will promote positive social change by providing nurses with the tools to help prepare for and respond to unexpected increases in patient volume. Improving efficiency with flow can help to improve patient care, timeliness, and safety

Choosing Number And Scheduling Priority Of Warm-Hand Offs: A Des Model

Background: The integration of behavioral health care into primary care is being promoted as a means to treat more people with behavioral health problems where they are most likely to be seen. Clinics with traditional behavioral health services may open slots among scheduled appointments to see these warm-hand off (WHO) patients identified by primary care providers (PCPs). The effects of giving priority for behavioral health appointments to either scheduled or WHO patients and of the number of appointments left open for WHO patients are investigated in this project. Methods: A discrete event simulation model was built of a moderately integrated clinic. WHO patients arrive randomly, on average 4 per day per PCP, and wait to see behavioral health providers (BHPs) who also see scheduled patients. Simulations of four clinic sizes, with PCP to BHP ratios of 1:1, were run. Effects of queue discipline (priority is given to scheduled or WHO patients) and the number of open WHO slots (3 or 5) are analyzed. Outcomes include the percent of scheduled patients served, the percent of WHO patients served, and the percent of BHP utilization. Results: In clinics with 1 PCP and 1 BHP, for 3 and 5 open slots respectively, giving priority to WHO patients resulted in 80.6% and 81.0% of WHO patients served and 84.4% and 86.6% of scheduled patients served, however, giving priority to scheduled patients resulted in 97.8% and 98.1% of scheduled patients served, but 32.0% and 47.9% of WHO patients served. A similar pattern was seen for larger clinics, though the percent of WHO patients served increased for both 3 and 5 open slots with clinic size. Having 3 or 5 open slots led to few differences when WHO patients were given priority, but when scheduled patients were given priority, choosing 5 open slots rather than 3 open slots, increased the percent of WHO patients served by 15-20 percentage points across the clinic sizes. In either queue discipline, changing from 3 to 5 open slots reduced the percent of BHP utilization by approximately 8 percentage points for all clinic sizes. When WHO patients were given priority, the average wait time for scheduled patients increased from approximately 2-5 minutes to 13-19 minutes across clinic sizes. Conclusion: These results might suggest to some clinics attempting to integrate primary care and traditional behavioral health services to choose to give WHO patients priority. However, it is recognized that there are costs associated with not seeing both scheduled and WHO patients, and clinics making this decision will have to weigh these tradeoffs. The analysis of these results provides one framework to assist in choosing between different arrangements for integration

Here’s something we prepared earlier: development, use and reuse of a configurable, inter-disciplinary approach for tackling overcrowding in NHS hospitals

Overcrowding affects hospital emergency departments (ED) worldwide. Most OR studies addressing overcrowding develop bespoke models to explore potential improvements but ignore the organisational context in which they would be implemented, and few influence practice. There is interest in whether reusable models, for ED crowding and in healthcare generally, could have more impact. We developed a configurable approach for tackling ED overcrowding. A reusable queuing model for exploring drivers of ED performance was augmented by a qualitative approach for exploring the implementation context and a generic framework for assessing the likely compatibility of interventions with a given organisation. At the hospital where the approach was developed it directly informed strategy. We describe reuse of the approach at three hospitals. One project was completed and well-received by hospital management, two were terminated partway when data problems surfaced. The primary contribution of this work is its novelty in considering, alongside quantitative modelling, evidence-based interventions to overcrowding and qualitative assessment of a hospital’s aptitude and capability to adopt different interventions. A secondary contribution is to further the debate on model reuse, particularly by introducing more complex, modelling-centred approaches that acknowledge how models must relate to tangible interventions with reasonable prospects of being adopted locally

Exploring overcrowding trends in an inner city emergence department in the UK before and during COVID-19 epidemic

BACKGROUND: The COVID-19 pandemic and the associated lockdowns have caused significant disruptions across society, including changes in the number of emergency department (ED) visits. This study aims to investigate the impact of three pre-COVID-19 interventions and of the COVID-19 UK-epidemic and the first UK national lockdown on overcrowding within University College London Hospital Emergency Department (UCLH ED). The three interventions: target the influx of patients at ED (A), reduce the pressure on in-patients' beds (B) and improve ED processes to improve the flow of patents out from ED (C). METHODS: We collected overcrowding metrics (daily attendances, the proportion of people leaving within 4 h of arrival (four-hours target) and the reduction in overall waiting time) during 01/04/2017-31/05/2020. We then performed three different analyses, considering three different timeframes. The first analysis used data 01/04/2017-31/12-2019 to calculate changes over a period of 6 months before and after the start of interventions A-C. The second and third analyses focused on evaluating the impact of the COVID-19 epidemic, comparing the first 10 months in 2020 and 2019, and of the first national lockdown (23/03/2020-31/05/2020). RESULTS: Pre-COVID-19 all interventions led to small reductions in waiting time (17%, p < 0.001 for A and C; an 9%, p = 0.322 for B) but also to a small decrease in the number of patients leaving within 4 h of arrival (6.6,7.4,6.2% respectively A-C,p < 0.001). In presence of the COVID-19 pandemic, attendance and waiting time were reduced (40% and 8%; p < 0.001), and the number of people leaving within 4 h of arrival was increased (6%,p < 0.001). During the first lockdown, there was 65% reduction in attendance, 22% reduction in waiting time and 8% increase in number of people leaving within 4 h of arrival (p < 0.001). Crucially, when the lockdown was lifted, there was an increase (6.5%,p < 0.001) in the percentage of people leaving within 4 h, together with a larger (12.5%,p < 0.001) decrease in waiting time. This occurred despite the increase of 49.6%(p < 0.001) in attendance after lockdown ended. CONCLUSIONS: The mixed results pre-COVID-19 (significant improvements in waiting time with some interventions but not improvement in the four-hours target), may be due to indirect impacts of these interventions, where increasing pressure on one part of the ED system affected other parts. This underlines the need for multifaceted interventions and a system-wide approach to improve the pathway of flow through the ED system is necessary. During 2020 and in presence of the COVID-19 epidemic, a shift in public behaviour with anxiety over attending hospitals and higher use of virtual consultations, led to notable drop in UCLH ED attendance and consequential curbing of overcrowding. Importantly, once the lockdown was lifted, although there was an increase in arrivals at UCLH ED, overcrowding metrics were reduced. Thus, the combination of shifted public behaviour and the restructuring changes during COVID-19 epidemic, maybe be able to curb future ED overcrowding, but longer timeframe analysis is required to confirm this