Two emergency departments, 6000km apart: Differences in patient flow and staff perceptions about crowding

Current patterns of care at adult emergency department in Ethiopian tertiary university hospital

BACKGROUND

The complexity and demands of emergency healthcare service are continuously increasing, and it is important to regularly track the patterns of care at the emergency department (ED).

METHODOLOGY

A retrospective study was conducted at the ED of the University of Gondar Comprehensive Specialized Hospital (UoGCSH) from April 1 to June 30, 2021. Ethical approval was obtained from the Emergency and Critical Care Directorate of UoGCSH. Data was collected from the emergency registry and descriptive analysis was performed.

RESULTS

A total of 5232 patients have visited and triaged at the ED. All patients who visited the ED have received triage service within 5 min of arrival. The average length of stay at the ED was 3 days. About 79.1% of patients have stayed at the ED beyond 24 h, and the unavailability of beds at admission areas was responsible for 62% of delays. Mortality rate at the ED was 1.4%, and male to female ratio of death was 1.2 to 1. Shock (all types combined), pneumonia with/without COVID-19, and poisoning were the leading causes of death at the ED which were responsible for 32.5%, 15.5%, and 12.7% of deaths respectively.

CONCLUSIONS

Triage has been done within the recommended time after patient arrival. However, many patients were staying at the ED for an unacceptably prolonged time. Unavailability of beds at the admission areas, waiting long for senior clinicians' decisions, delays in investigation results, and lack of medical equipment were the causes of delayed discharge from the ED. Shock, pneumonia, and poisoning were the leading causes of death. Healthcare administrators should address the lack of medical resources, and clinicians should provide timely clinical decision and investigation results.

Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining

Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time.

Transfer boarding delays care more in low-volume rural emergency departments: A cohort study

PURPOSE

Emergency department (ED) crowding is increasing and is associated with adverse patient outcomes. The objective of this study was to measure the relative impact of ED boarding on timeliness of early ED care for new patient arrivals, with a focus on the differential impact in low-volume rural hospitals.

METHODS

A retrospective cohort of all patients presenting to a Veterans Health Administration (VHA) ED between 2011 and 2014. The primary exposure was the number of patients in the ED at the time of ED registration, stratified by disposition (admit, discharge, or transfer) and mental health diagnosis. The primary outcome was time-to-provider evaluation, and secondary outcomes included time-to-EKG, time-to-laboratory testing, time-to-radiography, and total ED length-of-stay. Rurality was measured using the Rural-Urban Commuting Areas.

FINDINGS

A total of 5,912,368 patients were included from all 123 VHA EDs. Adjusting for acuity, new patients had longer time-to-provider when more patients were in the ED, and patients awaiting transfer for nonmental health conditions impacted time-to-provider for new patients (16.6 min delays, 95% CI: 12.3-20.7 min) more than other patient types. Rural patients saw a greater impact of crowding on care timeliness than nonrural patients (additional 5.3 min in time-to-provider per additional patient in ED, 95% CI: 4.3-6.4), and the impact of additional patients in all categories was most pronounced in the lowest-volume EDs.

CONCLUSIONS

Patients seen in EDs with more crowding have small, but additive, delays in early elements of ED care, and transferring patients with nonmental health diagnoses from rural facilities were associated with the greatest impact.

Emergency severity level-3 patient flow based on point-of-care testing improves patient outcomes

BACKGROUND

Overcrowding of the Emergency Department is rapidly becoming a global challenge and a major source of concern for emergency physicians. The desire to improve Emergency Department throughput requires novel approaches to patient flow.

MATERIALS AND METHODS

We conducted a prospective and cluster-randomized study, to evaluate the impact in patient outcomes of a new patient flow based on Point-of-Care Testing (POCT). A total of 380 Emergency Severity Level-3 patients were enrolled and studied in two different groups, interventional arm (laboratory analyses performed on POCT analyzers implemented in the Emergency Department) or control arm (central laboratory). The primary outcome was the Emergency Department length of stay. Secondary outcome included the time to first medical intervention, the laboratory turnaround time and the time to disposition decision. Readmission within the 7 days after discharge was also calculated.

RESULTS

Length of stay significantly decreased by 88.50 min (from 247.00 to 158.50), time to disposition decision by 89.00 min (from 192.00 to 103.00) and laboratory turnaround time by 67.11 min (from 89.84 to 22.73) in the POCT group. No increase in readmission was found.

CONCLUSION

Our strategy based on POCT represents a good approach to optimize patient flow in the Emergency Department and it should be seen as a starting point for further studies focusing on improving throughput.

Care management instruments used by nurses in the emergency hospital services

OBJECTIVE

To describe the instruments used by nurses for the management of care in face of the demands of the emergency hospital service.

METHOD

This is a qualitative study, with triangulation of data from interviews, focus groups, and documents, conducted with nurses from an Emergency Hospital Service in a state in southern Brazil. Data were subjected to thematic content analysis.

RESULTS

Seventeen nurses participated in the study. The categories emerging from this study were view of the whole picture, definition of priorities, and physical instruments. These instruments are used by nurses to manage multiple tasks and provide adequate care to patients with different levels of complexity, in the face of an intense and unpredictable work process due to the constant demand for care.

CONCLUSION

The instruments used by nurses in their work process are mainly skills and attitudes developed as a coping strategy at an intense and complex work environment.

Areas of delay related to prolonged length of stay in an emergency department of an academic hospital in South Africa

Introduction

Globally, length of stay of patients in emergency departments remains a challenge. Remaining in the emergency department for >12 h increases health care costs, morbidity and mortality rates and leads to crowding and lower patient satisfaction.The aim of this research was to describe the areas of delay related to prolonged length of stay in the emergency department of an academic hospital.

Methods

A quantitative retrospective study was done. The Input-Throughput-Output model was used to identify the areas of patients' journey through the emergency department. The possible areas of delay where then described. Using systematic sampling, a total of 100 patient files managed in an emergency department of an academic hospital in South Africa were audited over a period of 3 months. Descriptive statistics and regression analysis was used to analyse data.

Results

The mean length of stay of patients in the emergency department was 73 h 49 min. The length of stay per phase was: input (3 h 17 min), throughput (16 h 25 min) and output (54 h 7 min). A strong significant relationship found between the length of stay and the time taken between disposition decision (throughput phase) disposition decision to admission or discharge of patients from the ED (output phase) (p < 0.05).

Conclusion

The output phase was identified as the longest area of delay in this study, with the time taken between disposition decision to admission or discharge of patients from the ED (patients waiting for inpatient beds) as the main significant area of delay.

Machine learning for developing a prediction model of hospital admission of emergency department patients: Hype or hope?

OBJECTIVE

Early identification of emergency department (ED) patients who need hospitalization is essential for quality of care and patient safety. We aimed to compare machine learning (ML) models predicting the hospitalization of ED patients and conventional regression techniques at three points in time after ED registration.

METHODS

We analyzed consecutive ED patients of three hospitals using the Netherlands Emergency Department Evaluation Database (NEED). We developed prediction models for hospitalization using an increasing number of data available at triage, ∼30 min (including vital signs) and ∼2 h (including laboratory tests) after ED registration, using ML (random forest, gradient boosted decision trees, deep neural networks) and multivariable logistic regression analysis (including spline transformations for continuous predictors). Demographics, urgency, presenting complaints, disease severity and proxies for comorbidity, and complexity were used as covariates. We compared the performance using the area under the ROC curve in independent validation sets from each hospital.

RESULTS

We included 172,104 ED patients of whom 66,782 (39 %) were hospitalized. The AUC of the multivariable logistic regression model was 0.82 (0.78-0.86) at triage, 0.84 (0.81-0.86) at ∼30 min and 0.83 (0.75-0.92) after ∼2 h. The best performing ML model over time was the gradient boosted decision trees model with an AUC of 0.84 (0.77-0.88) at triage, 0.86 (0.82-0.89) at ∼30 min and 0.86 (0.74-0.93) after ∼2 h.

CONCLUSIONS

Our study showed that machine learning models had an excellent but similar predictive performance as the logistic regression model for predicting hospital admission. In comparison to the 30-min model, the 2-h model did not show a performance improvement. After further validation, these prediction models could support management decisions by real-time feedback to medical personal.

Delayed flow is a risk to patient safety: A mixed method analysis of emergency department patient flow

INTRODUCTION

Increasing emergency department (ED) demand and crowding has heightened focus on the need for better understanding of patient flow.

AIM

This study aimed to identify input, throughput and output factors contributing to ED patient flow bottlenecks and extended ED length of stay (EDLOS).

METHOD

Concurrent nested mixed method study based on retrospective analysis of attendance data, patient flow observational data and a focus group in an Australian regional ED.

RESULTS

Analysis of 89 013 ED presentations identified increased EDLOS, particularly for patients requiring admission. Mapping of 382 patient journeys identified delays in time to triage assessment (0-39 mins) and extended waiting room stays (0-348 mins). High proportions of patients received care outside ED cubicles. Four qualitative themes emerged: coping under pressure, compromising care and safety, makeshift spaces, and makeshift roles.

CONCLUSION

Three key findings emerged: i) hidden waits such as extended triage-queuing occur during the input phase; ii) makeshift spaces are frequently used to assess and treat patients during times of crowding; and iii) access block has an adverse effect on output flow. Data suggests arrival numbers may not be a key predictor of EDLOS. This research contributes to our understanding of ED crowding and patient flow, informing service delivery and planning.

[Emergency overcrowding and hospital organization: Causes and solutions]

Emergency Department (ED) overcrowding is a silent killer. Thus, several studies in different countries have described an increase in mortality, a decrease in the quality of care and prolonged hospital stays associated with ED overcrowding. Causes are multiple: input and in particular lack of access to lab test and imaging for general practitioners, throughput and unnecessary or time-consuming tasks, and output, in particular the availability of hospital beds for unscheduled patients. The main cause of overcrowding is waiting time for available beds in hospital wards, also known as boarding. Solutions to resolve the boarding problem are mostly organisational and require the cooperation of all department and administrative levels through efficient bed management. Elderly and polypathological patients wait longer time in ED. Internal Medicine, is the ideal specialty for these complex patients who require time for observation and evaluation. A strong partnership between the ED and the internal medicine department could help to reduce ED overcrowding by improving care pathways.

One-way-street revisited: Streamlined admission of critically-ill trauma patients

INTRODUCTION

Emergency department (ED) crowding is associated with increased mortality and delays in care. We developed a rapid admission pathway targeting critically-ill trauma patients in the ED. This study investigates the sustainability of the pathway, as well as its effectiveness in times of increased ED crowding.

MATERIALS & METHODS

This was a retrospective cohort study assessing the admission of critically-ill trauma patients with and without the use of a rapid admission pathway from 2013 to 2018. We accessed demographic and clinical data from trauma registry data and ED capacity logs. Statistical analyses included univariate and multivariate testing.

RESULTS

A total of 1700 patients were included. Of this cohort, 434 patients were admitted using the rapid admission pathway, whereas 1266 were admitted using the traditional pathway. In bivariate analysis, mean ED LOS was 1.54 h (95% Confidence Interval [CI]: 1.41, 1.66) with the rapid pathway, compared with 5.88 h (95% CI: 5.64, 6.12) with the traditional pathway (p < 0.01). We found no statistically significant relationship between rapid admission pathway use and survival to hospital discharge. During times of increased crowding, rapid pathway use continued to be associated with reduction in ED LOS (p < 0.01). The reduction in ED LOS was sustained when comparing initial results (2013-2014) to recent data (2015-2018).

CONCLUSION

This study found that a streamlined process to admit critically-ill trauma patients is sustainable and associated with reduction in ED LOS. As ED crowding remains pervasive, these findings support restructured care processes to limit prolonged ED boarding times for critically-ill patients.

Prediction of emergency department patient disposition decision for proactive resource allocation for admission

We investigate the capability of information from electronic health records of an emergency department (ED) to predict patient disposition decisions for reducing "boarding" delays through the proactive initiation of admission processes (e.g., inpatient bed requests, transport, etc.). We model the process of ED disposition decision prediction as a hierarchical multiclass classification while dealing with the progressive accrual of clinical information throughout the ED caregiving process. Multinomial logistic regression as well as machine learning models are built for carrying out the predictions. Utilizing results from just the first set of ED laboratory tests along with other prior information gathered for each patient (2.5 h ahead of the actual disposition decision on average), our model predicts disposition decisions with positive predictive values of 55.4%, 45.1%, 56.9%, and 47.5%, while controlling false positive rates (1.4%, 1.0%, 4.3%, and 1.4%), with AUC values of 0.97, 0.95, 0.89, and 0.84 for the four admission (minor) classes, i.e., intensive care unit (3.6% of the testing samples), telemetry unit (2.2%), general practice unit (11.9%), and observation unit (6.6%) classes, respectively. Moreover, patients destined to intensive care unit present a more drastic increment in prediction quality at triage than others. Disposition decision classification models can provide more actionable information than a binary admission vs. discharge prediction model for the proactive initiation of admission processes for ED patients. Observing the distinct trajectories of information accrual and prediction quality evolvement for ED patients destined to different types of units, proactive coordination strategies should be tailored accordingly for each destination unit.

Emergency department crowding: A systematic review of causes, consequences and solutions

BACKGROUND

Emergency department crowding is a major global healthcare issue. There is much debate as to the causes of the phenomenon, leading to difficulties in developing successful, targeted solutions.

AIM

The aim of this systematic review was to critically analyse and summarise the findings of peer-reviewed research studies investigating the causes and consequences of, and solutions to, emergency department crowding.

METHOD

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A structured search of four databases (Medline, CINAHL, EMBASE and Web of Science) was undertaken to identify peer-reviewed research publications aimed at investigating the causes or consequences of, or solutions to, emergency department crowding, published between January 2000 and June 2018. Two reviewers used validated critical appraisal tools to independently assess the quality of the studies. The study protocol was registered with the International prospective register of systematic reviews (PROSPERO 2017: CRD42017073439).

RESULTS

From 4,131 identified studies and 162 full text reviews, 102 studies met the inclusion criteria. The majority were retrospective cohort studies, with the greatest proportion (51%) trialling or modelling potential solutions to emergency department crowding. Fourteen studies examined causes and 40 investigated consequences. Two studies looked at both causes and consequences, and two investigated causes and solutions.

CONCLUSIONS

The negative consequences of ED crowding are well established, including poorer patient outcomes and the inability of staff to adhere to guideline-recommended treatment. This review identified a mismatch between causes and solutions. The majority of identified causes related to the number and type of people attending ED and timely discharge from ED, while reported solutions focused on efficient patient flow within the ED. Solutions aimed at the introduction of whole-of-system initiatives to meet timed patient disposition targets, as well as extended hours of primary care, demonstrated promising outcomes. While the review identified increased presentations by the elderly with complex and chronic conditions as an emerging and widespread driver of crowding, more research is required to isolate the precise local factors leading to ED crowding, with system-wide solutions tailored to address identified causes.