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The effect of overcrowding in emergency departments on the admission rate according to the emergency triage level.

ABSTRACT: Overcrowding in emergency departments is a serious public health issue. Recent studies have reported that overcrowding in emergency departments affects not only the quality of emergency care but also clinical decisions about admission. However, no studies have examined the characteristics of the patient groups whose admission rate is influenced by such overcrowding. This retrospective cohort study was conducted in a single emergency department between January 1 and December 31, 2018. Patients over 19 years old were enrolled and divided into three groups according to the degree of overcrowding-high, low, and non-based on the total number of patients in the emergency department. An emergency triage tool (the Korean Triage and Acuity Scale) was used, which categorizes patients into five different levels. We analyzed whether the degree of change in the admission rate according to the extent of overcrowding differed for each triage group. There were 73,776 patients in this study. In the analysis of all patient groups, the admission rate increased as the degree of overcrowding rose (the adjusted odds ratio for admission was 1.281 (1.225-1.339) in the high overcrowding group versus the non-overcrowding group). The analysis of the patients in each triage level showed an increase in the admission rate associated with the overcrowding, which was greater in the patient groups with a lower triage level (adjusted odds ratios for admission in the high overcrowding group versus non-overcrowding group: Korean Triage and Acuity Scale level 3 = 1.215 [1.120-1.317], level 4 = 1.294 [1.211-1.382], and level 5 = 1.954 [1.614-2.365]).

SUBMITTER: Jung HM

PROVIDER: S-EPMC7888587 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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The effect of overcrowding in emergency departments on the admission rate according to the emergency triage level.

Jung Hae Min HM Kim Min Joung MJ Kim Ji Hoon JH Park Yoo Seok YS Chung Hyun Soo HS Chung Sung Phil SP Lee Ji Hwan JH

PloS one 20210217 2

Overcrowding in emergency departments is a serious public health issue. Recent studies have reported that overcrowding in emergency departments affects not only the quality of emergency care but also clinical decisions about admission. However, no studies have examined the characteristics of the patient groups whose admission rate is influenced by such overcrowding. This retrospective cohort study was conducted in a single emergency department between January 1 and December 31, 2018. Patients ov ...[more]

PMID: 33596264

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Project description:BackgroundMedication lists prepared in the emergency department (ED) form the basis for diagnosing and treating patients during hospitalization. Since incomplete medication information may lead to patient harm, it is crucial to obtain a correct and complete medication list at hospital admission. In this cross-sectional retrospective study we wanted to explore medication information completeness in admission notes from Norwegian EDs and investigate which factors were associated with level of completeness.MethodsMedication information was assessed for completeness by applying five evaluation criteria; generic name, formulation, dose, frequency, and indication for use. A medication completeness score in percent was calculated per medication, per admission note and per criterion. Quantile regression analysis was applied to investigate which variables were associated with medication information completeness.ResultsAdmission notes for patients admitted between October 2018 and September 2019 and using at least one medication were included. A total of 1,080 admission notes, containing 8,604 medication orders, were assessed. The individual medications had a mean medication completeness score of 88.1% (SD 16.4), while admission notes had a mean medication completeness score of 86.3% (SD 16.2). Over 90% of all individual medications had information about generic name, formulation, dose and frequency stated, while indication for use was only present in 60%. The use of an electronic tool to prepare medication information had a significantly strong positive association with completeness. Hospital visit within the last 30 days, the patient's living situation, number of medications in use, and which hospital the patient was admitted to, were also associated with information completeness.ConclusionsMedication information completeness in admission notes was high, but potential for improvement regarding documentation of indication for use was identified. Applying an electronic tool when preparing admission notes in EDs seems crucial to safeguard completeness of medication information.

Project description:ObjectiveTo predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.MethodsThis retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.ResultsA total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).ConclusionMachine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

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Project description:BackgroundOvercrowding in emergency departments is a worldwide problem. A systematic literature review was undertaken to scientifically explore which interventions improve patient flow in emergency departments.MethodsA systematic literature search for flow processes in emergency departments was followed by assessment of relevance and methodological quality of each individual study fulfilling the inclusion criteria. Studies were excluded if they did not present data on waiting time, length of stay, patients leaving the emergency department without being seen or other flow parameters based on a nonselected material of patients. Only studies with a control group, either in a randomized controlled trial or in an observational study with historical controls, were included. For each intervention, the level of scientific evidence was rated according to the GRADE system, launched by a WHO-supported working group.ResultsThe interventions were grouped into streaming, fast track, team triage, point-of-care testing (performing laboratory analysis in the emergency department), and nurse-requested x-ray. Thirty-three studies, including over 800,000 patients in total, were included. Scientific evidence on the effect of fast track on waiting time, length of stay, and left without being seen was moderately strong. The effect of team triage on left without being seen was relatively strong, but the evidence for all other interventions was limited or insufficient.ConclusionsIntroducing fast track for patients with less severe symptoms results in shorter waiting time, shorter length of stay, and fewer patients leaving without being seen. Team triage, with a physician in the team, will probably result in shorter waiting time and shorter length of stay and most likely in fewer patients leaving without being seen. There is only limited scientific evidence that streaming of patients into different tracks, performing laboratory analysis in the emergency department or having nurses to request certain x-rays results in shorter waiting time and length of stay.

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Dataset Information

The effect of overcrowding in emergency departments on the admission rate according to the emergency triage level.

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The effect of overcrowding in emergency departments on the admission rate according to the emergency triage level.

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