Split flow models, in which a physician rather than a nurse performs triage, are increasingly being used in hospital emergency departments (EDs) to improve patient flow. Before deciding whether such interventions should be adopted, it is important to understand how split flows causally impact patient flow and outcomes. We employ causal inference methodology to estimate average causal effects of a split flow model on time to be roomed, time to disposition after being roomed, admission decisions, and ED revisits at a large tertiary teaching hospital that uses a split flow model during certain hours each day. We propose a regression discontinuity (RD) design to identify average causal effects, which we formalize with causal diagrams. Using electronic health records data (n = 21,570), we estimate that split flow increases average time to be roomed by about 4.6 minutes (95% CI: [2.9,6.2] minutes) but decreases average time to disposition by 14.4 minutes (95% CI: [4.1,24.7] minutes), leading to an overall reduction in length of stay. Split flow is also found to decrease admission rates by 5.9% (95% CI: [2.3%, 9.4%]) but not at the expense of a significant change in revisit rates. Lastly, we find that the split flow model is especially effective at reducing length of stay during low congestion levels, which mediation analysis partly attributes to early task initiation by the physician assigned to triage.
We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Our feature-rich modelling allows for dynamic updating and refinement of waiting time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. Aspects relating to communicating forecast uncertainty to patients, and implementing this methodology in practice, are also discussed.
Frequent users of emergency departments (ED) pose a significant challenge to hospital emergency services. Despite a wealth of studies in this field, it is hardly understood, what medical conditions lead to frequent attendance. We examine (1) what ambulatory care sensitive conditions (ACSC) are linked to frequent use, (2) how frequent users can be clustered into subgroups with respect to their diagnoses, acuity and admittance, and (3) whether frequent use is related to higher acuity or admission rate. We identified several ACSC that highly increase the risk for heavy ED use, extracted four majo…
In this paper we present a new methodology to model patient transitions and length of stay in the emergency department using a series of conditional Coxian phase-type distributions, with covariates. We reformulate the Coxian models (standard Coxian, Coxian with multiple absorbing states, joint Coxian, and conditional Coxian) to take into account heterogeneity in patient characteristics such as arrival mode, time of admission and age. The approach differs from previous research in that it reduces the computational time, and it allows the inclusion of patient covariate information directly into …
Emergency department (ED) care for frail elderly patients is associated with an increased use of resources due to their complex medical needs and frequently difficult psycho-social situation. To better target their needs with specially trained staff, it is vital to determine the times during which these particular patients present to the ED. Recent research was inconclusive regarding this question and the applied methods were limited to coarse time windows. Moreover, there is little research on time variation of frail ED patients' case complexity. This study examines differences in arrival rat…
We estimate average responses curves for treatment time in the Emergency Department (ED). Extending treatment time is considered a promising solution for improving admission decisions. Providing empirical support for this solution, however, is difficult because this intervention (treatment) is a continuous time-to-event; is strongly influenced by unmeasured patient health needs and is jointly determined with the admission decision (admit vs. discharge); and may be only modifiable up to a shift in the realized time. We formalize the admission process as a directed acyclic graph and show that av…
Emergency department (ED) crowding has been an increasing problem worldwide. Prior research has identified factors that contribute to ED crowding. However, the relationships between these remain incompletely understood. This study's objective was to analyse the effects of initiating a local protocol to alleviate crowding situations at the expense of increasing returning patients through the development of a system dynamics (SD) simulation model. The SD study is from an academic care hospital in Boston, MA. Data sources include direct observations, semi-structured interviews, archival data from…
Few studies have linked specific sources of ambient particulate matter smaller than 2.5 $μ$m (PM2.5) and asthma. In this study, we estimated the contributions of specific sources to PM2.5 and examined their association with daily asthma hospital utilization in Cincinnati, Ohio, USA. We used Poisson regression models to estimate the daily number of asthma ED visits the day of and one, and two days following separate increases in PM2.5 and its source components, adjusting for temporal trends, holidays, temperature, and humidity. In addition, we used a model-based clustering method to group days …
Parkland Center for Clinical Innovation (PCCI)（帕克兰临床创新中心）；Parkland Community Health Plan (PCHP)（帕克兰社区健康计划）
Pediatric asthma is the most prevalent chronic childhood illness, afflicting about 6.2 million children in the United States. However, asthma could be better managed by identifying and avoiding triggers, educating about medications and proper disease management strategies. This research utilizes deep learning methodologies to predict asthma-related emergency department (ED) visit within 3 months using Medicaid claims data. We compare prediction results against traditional statistical classification model - penalized Lasso logistic regression, which we trained and have deployed since 2015. The …
Forecasting mortality associated emergency department crowding
Faculty of Medicine and Health Technology, Tampere University（坦佩雷大学医学与健康技术学院）；Emergency Department, Tampere University Hospital（坦佩雷大学医院急诊科）；Kanta-Häme Central Hospital（坎塔-海梅中心医院）；Faculty of Information Technology and Communication Sciences, Tampere University（坦佩雷大学信息技术与传播科学学院）；Centre for Vascular Surgery and Interventional Radiology, Tampere University Hospital（坦佩雷大学医院血管外科与介入放射中心）
MRC Centre for Global Infectious Disease Analysis, Imperial College London（帝国理工学院MRC全球传染病分析中心）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健国民健康服务体系信托）；Faculty of Medicine, Imperial College London（帝国理工学院医学院）；Department of Infectious Disease, Imperial College London（帝国理工学院传染病系）；Department of Mathematics, Imperial College London（帝国理工学院数学系）
There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. Using 8 years of electronic admissions data from two major acute care hospitals in London, we develop a novel ensemble methodology that combines the outcomes of the best performing time se…
We portray the evolution of the Covid-19 epidemic during the crisis of March-April 2020 in the Paris area, by analyzing the medical emergency calls received by the EMS of the four central departments of this area (Centre 15 of SAMU 75, 92, 93 and 94). Our study reveals strong dissimilarities between these departments. We show that the logarithm of each epidemic observable can be approximated by a piecewise linear function of time. This allows us to distinguish the different phases of the epidemic, and to identify the delay between sanitary measures and their influence on the load of EMS. This …
