The effect of emergency department crowding on clinically oriented outcomes

Determining the Association Between Emergency Department Crowding and Debriefing After Pediatric Trauma Resuscitations

BACKGROUND

Debriefing in the pediatric emergency department (PED) is an invaluable tool to improve team well-being, communication, and performance. Despite evidence, surveys have reported heavy workload as a barrier to debriefing leading to missed opportunities for improvement in an already busy ED. The study aims to determine the association between the incidence of debriefing after pediatric trauma resuscitations and PED crowding.

METHODS

A total of 491 Trauma One activations in Riley Children's Hospital Pediatric Emergency Department that presented between April 2018 to December 2019 were included in the study. Debriefing documentations, patient demographics, time and date of presentation, mechanism of injury, injury severity score, disposition from PED, and length of stay (LOS) were collected and analyzed. The National Emergency Department Overcrowding Scale score at arrival, Average LOS, total PED census, total PED waiting room census, and rates of left without being seen were compared between groups.

RESULTS

Of 491 Trauma One activations presented to our PED, 50 (10%) trauma evaluations had documented debriefing. The National Emergency Department Overcrowding Scale score at presentation was significantly lower in those with debriefing versus without debriefing. In addition, the PED hourly census, waiting room census, average LOS, and left without being seen were also significantly lower in the group with debriefing. In addition, trauma cases with debriefing had a higher proportion of patients with profound injuries and discharges to the morgue.

CONCLUSIONS

Pediatric emergency department crowding is a significant barrier to debriefing after trauma resuscitations. However, profound injuries and traumatic pediatric deaths remain the strongest predictors in conducting debriefing regardless of PED crowding status.

Criticality and clinical department prediction of ED patients using machine learning based on heterogeneous medical data

PROBLEM

Emergency triage faces multiple challenges, including limited medical resources and inadequate manual triage nurses, which cause incorrect triage, overcrowding in the emergency department (ED), and long patient waiting time.

OBJECTIVE

This paper aims to propose and validate an accurate and efficient artificial intelligence-based method for effectively ED triage and alleviating the pressure on medical resources.

METHODS

We propose two novel machine learning models, TransNet and TextRNN, for predicting patient severity levels and clinical departments using heterogeneous medical data in ED triage. Our models employ a parallel structure for feature extraction and incorporate an attention mechanism to extract essential information from the fused features, enabling accurate predictions. The models analyze the triage data (2020-2022) from the ED of Beijing University People's Hospital, incorporating variables (demographics, triage vital signs, and chief complaints) to identify patient severity levels and clinical departments. We performed data cleaning, categorization, and encoding first. Then, we divided the available data into a training set (56%), a validation set (24%), and a test set (20%) by random sampling. Finally, our models underwent 5-fold cross-validation and were compared with other state-of-the-art models.

RESULTS

We comprehensively evaluated the proposed models against various Recurrent Neural Networks (RNN), Convolutional Neural Networks (CNN), Traditional Machine Learning (TML), and Transformer-based (TF) models, achieving excellent performance in predicting triage outcomes. Specifically, TextRNN achieved a prediction success rate of 86.23% [85.86-86.70] for severity levels and 94.30% [94.00-94.46] for clinical departments among 161,198 ED visits. Moreover, TransNet demonstrated higher sensitivities of 84.08% and 90.05% for severity levels and clinical departments, respectively, with specificities of 76.48% and 95.16%. The accuracy of our model is 0.87%, 0.18%, 4.29%, and 1.96%, higher than that of the above four family models on average. Furthermore, our method significantly reduced under-triage by 12.06% and over-triage by 17.92% compared to manual triage.

CONCLUSIONS

Experimental results demonstrated that the proposed models fuse heterogeneous medical data in the triage process, successfully predicting patients' triage outcomes. Our models can improve triage efficiency, reduce the under/over-triage rate, and provide physicians with valuable decision-making support.

Artificial intelligence for predicting acute appendicitis: a systematic review

BACKGROUND

Paediatric appendicitis may be challenging to diagnose, and outcomes difficult to predict. While diagnostic and prognostic scores exist, artificial intelligence (AI) may be able to assist with these tasks.

METHOD

A systematic review was conducted aiming to evaluate the currently available evidence regarding the use of AI in the diagnosis and prognostication of paediatric appendicitis. In accordance with the PRISMA guidelines, the databases PubMed, EMBASE, and Cochrane Library were searched. This review was prospectively registered on PROSPERO.

RESULTS

Ten studies met inclusion criteria. All studies described the derivation and validation of AI models, and none described evaluation of the implementation of these models. Commonly used input parameters included varying combinations of demographic, clinical, laboratory, and imaging characteristics. While multiple studies used histopathological examination as the ground truth for a diagnosis of appendicitis, less robust techniques, such as the use of ICD10 codes, were also employed. Commonly used algorithms have included random forest models and artificial neural networks. High levels of model performance have been described for diagnosis of appendicitis and, to a lesser extent, subtypes of appendicitis (such as complicated versus uncomplicated appendicitis). Most studies did not provide all measures of model performance required to assess clinical usability.

CONCLUSIONS

The available evidence suggests the creation of prediction models for diagnosis and classification of appendicitis using AI techniques, is being increasingly explored. However, further implementation studies are required to demonstrate benefit in system or patient-centred outcomes with model deployment and to progress these models to the stage of clinical usability.

Identifying models of care to improve outcomes for older people with urgent care needs: a mixed methods approach to develop a system dynamics model

Background

We aimed to understand urgent and emergency care pathways for older people and develop a decision support tool using a mixed methods study design.

Objective(s), study design, settings and participants

Work package 1 identified best practice through a review of reviews, patient, carer and professional interviews. Work package 2 involved qualitative case studies of selected urgent and emergency care pathways in the Yorkshire and Humber region. Work package 3 analysed linked databases describing urgent and emergency care pathways identifying patient, provider and pathway factors that explain differences in outcomes and costs. Work package 4 developed a system dynamics tool to compare emergency interventions.

Results

A total of 18 reviews summarising 128 primary studies found that integrated social and medical care, screening and assessment, follow-up and monitoring of service outcomes were important. Forty patient/carer participants described emergency department attendances; most reported a reluctance to attend. Participants emphasised the importance of being treated with dignity, timely and accurate information provision and involvement in decision-making. Receiving care in a calm environment with attention to personal comfort and basic physical needs were key. Patient goals included diagnosis and resolution, well-planned discharge home and retaining physical function. Participants perceived many of these goals of care were not attained. A total of 21 professional participants were interviewed and 23 participated in focus groups, largely confirming the review evidence. Implementation challenges identified included the urgent and emergency care environment, organisational approaches to service development, staff skills and resources. Work package 2 involved 45 interviews and 30 hours of observation in four contrasting emergency departments. Key themes relating to implementation included: intervention-related staff: frailty mindset and behaviours resources: workforce, space, and physical environment operational influences: referral criteria, frailty assessment, operating hours, transport. context-related links with community, social and primary care organisation and management support COVID-19 pandemic. approaches to implementation service/quality improvement networks engaging staff and building relationships education about frailty evidence. The linked databases in work package 3 comprised 359,945 older people and 1,035,045 observations. The most powerful predictors of four-hour wait and transfer to hospital were age, previous attendance, out-of-hours attendance and call handler designation of urgency. Drawing upon the previous work packages and working closely with a wide range of patient and professional stakeholders, we developed an system dynamics tool that modelled five evidence-based urgent and emergency care interventions and their impact on the whole system in terms of reducing admissions, readmissions, and hospital related mortality.

Limitations

Across the reviews there was incomplete reporting of interventions. People living with severe frailty and from ethnic minorities were under-represented in the patient/carer interviews. The linked databases did not include patient reported outcomes. The system dynamics model was limited to evidence-based interventions, which could not be modelled conjointly.

Conclusions

We have reaffirmed the poor outcomes frequently experienced by many older people living with urgent care needs. We have identified interventions that could improve patient and service outcomes, as well as implementation tools and strategies to help including clinicians, service managers and commissioners improve emergency care for older people.

Future work

Future work will focus on refining the system dynamics model, specifically including patient-reported outcome measures and pre-hospital services for older people living with frailty who have urgent care needs.

Study registrations

This study is registered as PROSPERO CRD42018111461. WP 1.2: University of Leicester ethics: 17525-spc3-ls:healthsciences, WP 2: IRAS 262143, CAG 19/CAG/0194, WP 3: IRAS 215818, REC 17/YH/0024, CAG 17/CAG/0024.

Funding

This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme [project number 17/05/96 (Emergency Care for Older People)] and will be published in full in Health and Social Care Delivery Research; Vol. 11, No. 14. See the NIHR Journals Library website for further project information.

Association of emergency department crowding with inpatient outcomes

OBJECTIVE

To examine the association of higher emergency department (ED) census with inpatient outcomes on the day of discharge (inpatient length of stay, in-hospital mortality, ED revisits, and readmissions).

DATA SOURCES AND STUDY SETTING

All-payer ED and inpatient discharge data and hospital characteristics data from all non-federal, general, and acute care hospitals in the state of California from October 1, 2015 to December 31, 2017.

STUDY DESIGN

In retrospective data analysis, we examined whether ED census was associated with inpatient outcomes for all inpatients, including those not admitted through the ED. The main predictor variable was ED census on day of discharge, categorized based on hospital year and day of week. Separate linear regression models with robust SEs and hospital fixed effects examined the association of ED census on inpatient outcomes (length of stay, 3-day ED revisit, 30-day all-cause readmission, in-hospital mortality), controlling for patient and visit-level factors. We stratified analyses by whether admission was elective or unscheduled.

EXTRACTION METHODS

Inpatient discharges in non-federal, general medical hospitals with EDs.

PRINCIPAL FINDINGS

We examined 5,784,253 discharges. The adjusted model showed that, compared to when the ED was below the median, higher ED census on the day of discharge was associated with longer inpatient length of stay, lower readmissions, and higher in-hospital mortality (90th percentile for length of stay: +0.8% [95% confidence interval, CI: +0.6% to +1.1%]; readmissions: -0.59 percentage points [or -5.6%] [95% CI: -0.0071 to -0.0048]; mortality: +0.14 percentage points [or +5.4%] [95% CI: +0.0009 to +0.0018]). [Correction added on 18 November 2022, after first online publication: '[odds rato, OR -5.6%]' and '[OR +5.4%]' of the preceding sentence have been corrected to '[or -5.6%]' and '[or +5.4%]', respectively, in this version.] Results for length of stay were primarily driven by patients with elective admissions, while results for readmissions and in-hospital mortality were primarily driven by patients with unscheduled admissions.

CONCLUSIONS

This study suggests that ED crowding may affect inpatients throughout the hospital, even patients who are already admitted to the hospital.

Experience-based codesign approach to improve care in Australian emergency departments for complex consumer cohorts: the MyED project protocol, Stages 1.1-1.3

INTRODUCTION

Emergency department (ED) care must adapt to meet current and future demands. In Australia, ED quality measures (eg, prolonged length of stay, re-presentations or patient experience) are worse for older adults with multiple comorbidities, people who have a disability, those who present with a mental health condition, Indigenous Australians, and those with a culturally and linguistically diverse (CALD) background. Strengthened ED performance relies on understanding the social and systemic barriers and preferences for care of these different cohorts, and identifying viable solutions that may result in sustained improvement by service providers. A collaborative 5-year project (MyED) aims to codesign, with ED users and providers, new or adapted models of care that improve ED performance, improve patient outcomes and improve patient experience for these five cohorts.

METHODS AND ANALYSIS

Experience-based codesign using mixed methods, set in three hospitals in one health district in Australia. This protocol introduces the staged and incremental approach to the whole project, and details the first research elements: ethnographic observations at the ED care interface, interviews with providers and interviews with two patient cohorts-older adults and adults with a CALD background. We aim to sample a diverse range of participants, carefully tailoring recruitment and support.

ETHICS AND DISSEMINATION

Ethics approval has been obtained from the Western Sydney Local Health District Human Research Ethics Committee (2022/PID02749-2022/ETH02447). Prior informed written consent will be obtained from all research participants. Findings from each stage of the project will be submitted for peer-reviewed publication. Project outputs will be disseminated for implementation more widely across New South Wales, Australia.

Medico-legal risk and use of medical directives in the emergency department

PURPOSE

The medico-legal risk associated with application of medical directives in the emergency department (ED) is unknown. The objective of this study was to describe and analyze factors associated with medico-legal risk in cases involving medical directives in the ED.

METHODS

We conducted a descriptive analysis of closed medico-legal cases [hospital complaints, regulatory authority (i.e., College) complaints, and civil legal actions] involving emergency physicians in Canadian EDs involving medical directives (alternate terms including "standing order", "nursing initiated", "nurse initiated", "nursing order", "triage initiated", "triage ordered", "directive", "ED protocol", and "ED's protocol"). We used data from closed cases involving the Canadian Medical Protective Association from January 2016 until December 2021. We abstracted descriptive factors of the cases and used a framework for contributing factors classification.

RESULTS

From 2016 until 2021, 43,332 cases were closed and 1957 involved emergency physicians for which there was medico-legal information available for analysis. In all, 28 involved emergency physicians and medical directives. Situational awareness, team communication, and issues with clinical decision-making were the most important factors contributing to harm and medico-legal risk. Peer experts were critical of physicians not reviewing all results available for patients when initiated through a directive, misinterpreting test results, a less than thorough initial assessment, and of failing to reassess patients or re-order investigations when indicated.

CONCLUSION

Our findings suggest that the medico-legal risk exposure from the use of medical directives in the ED is low. Emergency departments may consider implementing systems to support adherence to medical directive policies, ensure physicians are alerted when medical directives are completed in a timely fashion, and leverage tools to notify the healthcare team when results have not been reviewed.

Emergency department crowding: an overview of reviews describing measures causes, and harms

Crowding in Emergency Departments (EDs) has emerged as a global public health crisis. Current literature has identified causes and the potential harms of crowding in recent years. The way crowding is measured has also been the source of emerging literature and debate. We aimed to synthesize the current literature of the causes, harms, and measures of crowding in emergency departments around the world. The review is guided by the current PRIOR statement, and involved Pubmed, Medline, and Embase searches for eligible systematic reviews. A risk of bias and quality assessment were performed for each review, and the results were synthesized into a narrative overview. A total of 13 systematic reviews were identified, each targeting the measures, causes, and harms of crowding in global emergency departments. Key among the results is that the measures of crowding were heterogeneous, even in geographically proximate areas, and that temporal measures are being utilized more frequently. It was identified that many measures are associated with crowding, and the literature would benefit from standardization of these metrics to promote improvement efforts and the generalization of research conclusions. The major causes of crowding were grouped into patient, staff, and system-level factors; with the most important factor identified as outpatient boarding. The harms of crowding, impacting patients, healthcare staff, and healthcare spending, highlight the importance of addressing crowding. This overview was intended to synthesize the current literature on crowding for relevant stakeholders, to assist with advocacy and solution-based decision making.

History, ECG, Risk Factors (HER) Scoring for Cardiac Risk Stratification in Patients <45 Years of Age Presenting With Chest Pain

Background Chest pain is a common chief complaint of patients presenting to the emergency department. Acute coronary syndrome (ACS) is found to be the etiology of this symptom in a minority of these patient encounters. This study aimed to determine the utility of using the History, ECG, Risk Factors (HER) components of the History, ECG, Age, Risk Factors, Troponin (HEART) score in ruling out 30-day Major Adverse Cardiac Event (MACE), ACS, ventricular tachycardia, and ventricular fibrillation in patients aged less than 45. Additionally, the utility of this score in ruling out a positive troponin was investigated as well. Methodology This is a retrospective chart review study that examined a consecutive cohort of 7,724 patients presenting with chest pain to the 11 emergency departments of a single healthcare system over a two-year period (January 2019 to December 2020). HER scores of 0 to 1 were categorized as negative (-) and scores of two or greater were categorized as positive (+). Sensitivity, specificity, and predictive values were calculated for the relationship between HER score positivity and primary cardiac disease and troponin results. Results Test characteristics of HER scoring for significant primary cardiac disease in patients between 18 and 45 years of age presenting with undifferentiated chest pain were sensitivity of 88.0 (CI = 80.0-94.0), specificity of 72.6 (CI = 71.8-73.8), positive predictive value of 3.1 (CI = 2.4-3.9), and negative predictive value of 99.8 (CI = 99.7-99.9). Furthermore, an HER score >1 was neither sensitive nor specific in predicting a positive troponin (sensitivity = 80, CI = 71.9-86; specificity = 71.3, CI = 70.3-72.3). However, the negative predictive value of an HER score of 0-1 was 99.5 (CI = 99.3-99.7) and the positive predictive value was 4.7 (CI = 3.9-5.7). Conclusions According to this study, when evaluating young patients who are deemed to have a subjectively non-highly suspicious history, who have minimal risk factors, and who have an ECG without significant ST deviation, troponin testing is low yield in the risk stratification of patients under the age of 45 for serious primary cardiac disease.

Utilizing Triage Data for Medical Imaging Studies in the Emergency Department

The use of radiological images is widespread in the emergency department (ED) as physicians commonly rely on them during initial evaluations to confirm diagnoses, contributing to prolonged waiting times. This study aimed to determine the relationship between commonly gathered triage data and the need for radiological imaging. Data were collected from electronic charts that contained routinely collected hospital data at the time of triage in the King Abdulaziz Medical City (KAMC) in Riyadh ED. The binary logistic regression results demonstrated a statistically significant relationship between age and radiological imaging ordered in the ED. Each one-unit increase in age corresponded to a 0.983-fold increase in the likelihood of ordering radiological imaging (odds ratio: 0.983, 95% confidence interval: 0.972-0.995, p = 0.004). In contrast, hypertension, diabetes, and heart failure were independent predictors of the need for radiological imaging in the ED (p >0.05). Patient data that are immediately available during ED triage can be used to predict the need for radiological imaging during ED visits. Such models can identify patients who may require radiological imaging during ED visits and expedite patient disposition.

The impact of COVID-19 on emergency department boarding and in-hospital mortality

INTRODUCTION

The COVID-19 pandemic has been challenging for healthcare systems in the United States and globally. Understanding how the COVID-19 pandemic has impacted emergency departments (EDs) and patient outcomes in a large integrated healthcare system may help prepare for future pandemics. Our primary objective was to evaluate if there were changes to ED boarding and in-hospital mortality before and during the COVID-19 pandemic.

METHODS

This was a retrospective cohort study of all patients ages 18 and over who presented to one of 17 EDs (11 hospital-based; 6 freestanding) within our healthcare system. The study timeframe was March 1, 2019- February 29, 2020 (pre-pandemic) vs. March 1, 2020-August 31, 2021 (during the pandemic). Categorical variables are described using frequencies and percentages, and p-values were obtained from Pearson chi-squared or Fisher's exact tests where appropriate. In addition, multiple regression analysis was used to compare ED boarding and in-hospital mortality pre-pandemic vs. during the pandemic.

RESULTS

A total of 1,374,790 patient encounters were included in this study. In-hospital mortality increased by 16% during the COVID-19 Pandemic AOR 1.16(1.09-1.23, p < 0.0001). Boarding increased by 22% during the COVID-19 pandemic AOR 1.22(1.20-1.23), p < 0.0001). More patients were admitted during the COVID-19 pandemic than prior to the pandemic (26.02% v 24.97%, p < 0.0001). Initial acuity level for patients presenting to the ED increased for both high acuity (13.95% v 13.18%, p < 0.0001) and moderate acuity (60.98% v 59.95%, p < 0.0001) during the COVID-19 pandemic.

CONCLUSION

The COVID-19 pandemic led to increased ED boarding and in-hospital mortality.

Emergency department crowding negatively influences outcomes for adults presenting for chronic obstructive pulmonary disease

OBJECTIVES

Emergency department (ED) crowding leads to poor outcomes. Patients with respiratory conditions like chronic obstructive pulmonary disease (COPD) are especially vulnerable to crowding-related delays in care. We aimed to assess the associations of ED crowding metrics with outcomes for patients presenting with COPD.

METHODS

We conducted a population-based cohort study of adult patients presenting with a diagnosis of COPD to 18 high-volume EDs between 2014 and 2019 in Alberta, Canada. Administrative databases provided date and time data on key stages of the presentation including physician initial assessment and disposition decision. Crowding metrics were calculated using facility-specific median physician initial assessment and length of stay. Patient presentations were grouped by acuity and mixed-effects regression models were fit to adjust for the clustering at the facility level.

RESULTS

There were 49,085 presentations for COPD made by 25,734 patients (median age = 73 years). A 1-h increase in the physician initial assessment metric was associated with an increase in physician initial assessment for COPD patients by 23, 53, and 59 min for the high, moderate, and low acuity groups, respectively, adjusted for other predictors. For the low acuity group, this metric was associated with an increased length of stay of 73 min for admitted individuals. Similarly, an increase in the length of stay metric was also associated with an increased likelihood of being admitted for all acuity groups.

CONCLUSIONS

For patients with COPD, ED crowding results in delays in assessment increased length of stay, and increased proportion of patients admitted. These results suggest that ED crowding mitigation efforts to provide timely care for patients with COPD are urgently needed.

TRIAL REGISTRATION

N/A.

The Gap Between Daily Hospital Bed Supply and Demand: Design, Implementation, and Impact of Data-Driven Pre-Noon Discharge Targets

BACKGROUND

Hospitals have sought to increase pre-noon discharges to improve capacity, although evidence is mixed on the impact of these initiatives. Past interventions have not quantified the daily gap between morning bed supply and demand. The authors quantified this gap and applied the pre-noon data to target a pre-noon discharge initiative.

METHODS

The study was conducted at a large hospital and included adult and pediatric medical/surgical wards. The researchers calculated the difference between the average cumulative bed requests and transfers in for each hour of the day in 2018, the year prior to the intervention. In 2019 an intervention on six adult general medical and two surgical wards was implemented. Eight intervention and 14 nonintervention wards were compared to determine the change in average cumulative pre-noon discharges. The change in average hospital length of stay (LOS) and 30-day readmissions was also calculated.

RESULTS

The average daily cumulative gap by noon between bed supply and demand across all general care wards was 32.1 beds (per ward average, 1.3 beds). On intervention wards, mean pre-noon discharges increased from 4.7 to 6.7 (p < 0.0000) compared with the nonintervention wards 14.0 vs. 14.6 (p = 0.19877). On intervention wards, average LOS decreased from 6.9 to 6.4 days (p < 0.001) and readmission rates were 14.3% vs 13.9% (p = 0.3490).

CONCLUSION

The gap between daily hospital bed supply and demand can be quantified and applied to create pre-noon discharge targets. In an intervention using these targets, researchers observed an increase in morning discharges, a decrease in LOS, and no significant change in readmissions.

Mortality before and after reconfiguration of the Danish hospital-based emergency healthcare system: a nationwide interrupted time series analysis

OBJECTIVES

The study aimed to investigate how the 'natural experiment' of reconfiguring the emergency healthcare system in Denmark affected in-hospital and 30-day mortality on a national level. The reconfiguration included the centralisation of hospitals and the establishment of emergency departments with specialists present around the clock.

DESIGN

Hospital-based cohort study.

SETTING

All public hospitals in Denmark.

PARTICIPANTS

Patients with an unplanned contact from 1 January 2007 until 31 December 2016.

INTERVENTIONS

Stepped-wedge reconfiguration of the Danish emergency healthcare system.

MAIN OUTCOME MEASURES

We determined the adjusted ORs for in-hospital mortality and HRs for 30-day mortality using logistic and Cox regression analysis adjusted for sex, age, Charlson Comorbidity Index, income, education, mandatory referral and the changes in the out of hours system in the Capital Region. The main outcomes were stratified by the time of arrival. We performed subgroup analyses on selected diagnoses: myocardial infarction, stroke, pneumonia, aortic aneurysm, bowel perforation, hip fracture and major trauma.

RESULTS

We included 11 367 655 unplanned hospital contacts. The adjusted OR for overall in-hospital mortality after reconfiguration of the emergency healthcare system was 0.998 (95% CI 0.968 to 1.010; p=0.285), and the adjusted OR for 30-day mortality was 1.004 (95% CI 1.000 to 1.008; p=0.045)). Subgroup analyses showed some possible benefits of the reconfiguration such as a reduction in-hospital and 30-day mortality for myocardial infarction, stroke, aortic aneurysm and major trauma.

CONCLUSIONS

The Danish emergency care reconfiguration programme was not associated with an improvement in overall in-hospital mortality trends and was associated with a slight slowing of prior improvements in 30-day mortality trends.

Interprofessional teamwork before and after organizational change in a tertiary emergency department: An observational study

In healthcare settings, suboptimal interprofessional teamwork and communication contribute to unsafe care and avoidable harm. Interprofessional teamwork is essential in high-risk clinical areas such as the emergency department (ED). The aims of this study were to describe interprofessional teamwork in a hospital ED and to evaluate factors influencing interprofessional communication before and after implementation of a department-wide multifaceted intervention. Structured observations were undertaken during 2015/16 and 2019. Differences in interprofessional communication practices, teamwork, and sources of interruptions were compared before and after the intervention. The following domains were surveilled: (a) healthcare professionals (HCPs) communication initiatives, (b) HCPs' contribution to patient assessment, (c) interprofessional communication processes, and (d) team interruptions. The intervention included strategies to enable use of communication tools, changes to team structures, changes in work environment, ethical principles, and establishment of a code of professional conduct during interprofessional communication. Team interruptions significantly decreased post-intervention, and our findings suggest that organizational changes affect domains of teamwork. Statistically significant differences were observed in the initiated communication pre-intervention and contribution to patient assessment significantly increased post-intervention. Multifaceted organizational interventions can positively affect interprofessional team communication and work-flow in the ED, thus patient safety and quality of care can be improved.

The multicenter impacts of an emergency physician lead on departmental flow and provider experiences

INTRODUCTION

Emergency department (ED) flow impacts patient safety, quality of care and ED provider satisfaction. Throughput interventions have been shown to improve flow, yet few studies have reported the impact of ED physician leadership roles on patient flow and provider experiences. The study objective was to evaluate the impacts of the emergency physician lead role on ED flow metrics and provider experiences.

METHODS

Quantitative data about patient flow metrics were collected from ED information systems in two tertiary hospital EDs and analyzed to compare ED length of stay, EMS hallway length of stay, physician initial assessment time, 72-h readmission and left without being seen rates three months before and following emergency physician lead role implementation. ED flow metrics for adult patients at each site were analyzed independently using descriptive and inferential statistics, t tests and multivariable regression analysis. Qualitative data were collected via surveys from ED providers (physicians, nurses, and EMS) about their experiences working with the emergency physician leads and analyzed for themes about emergency physician leads impact.

RESULTS

The number of ED visits was relatively stable pre-post at the Peter Lougheed Centre (Lougheed) but increased pre-post at the Foothills Medical Centre (Foothills). Post-intervention at Lougheed median ED length of stay decreased by 18 min (p < 0.001) and at Foothills ED length of stay increased by 8 min (p < 0.001). EMS length of stay at Lougheed decreased by 20 min (p < 0.001), and at Foothills length of stay increased by 17 min (p < 0.001). Themes in provider feedback were that emergency physician leads (1) facilitated patient flow, (2) impacted provider workload, and (3) supported patient flow and safety with early assessments, treatments and investigations.

CONCLUSION

In this study, the emergency physician lead impacted ED flow metrics variably at different sites, but important learnings from provider experiences can guide future emergency physician lead implementation.

Racial and Ethnic Disparities in Emergency Department Wait Times for Children: Analysis of a Nationally Representative Sample

OBJECTIVE

To evaluate the association of race and ethnicity with wait times for children in US emergency departments (ED).

METHODS

We performed a cross-sectional study of ED encounters of children (<18 years) from 2014 to 2019 using a multistage survey of nonfederal US ED encounters. Our primary variable of interest was composite race and ethnicity: non-Hispanic White (NHW), non-Hispanic Black, Hispanic, and all others. Our outcome was ED wait time in minutes. We evaluated the association between race and ethnicity and wait time in Weibull regression models that sequentially added variables of acuity, demographics, hospital factors, and region/urbanicity.

RESULTS

We included 163,768,956 survey-weighted encounters. In univariable analysis, Hispanic children had a lower hazard ratio (HR) of progressing to evaluation (HR 0.84, 95% confidence interval [CI] 0.76-0.93) relative to NHW children, indicating longer ED wait times. This association persisted in serial multivariable models incorporating acuity, demographics, and hospital factors. This association was not observed when incorporating variables of hospital region and urbanicity (HR 0.91, 95% CI 0.83-1.00). In subgroup analysis, Hispanic ethnicity was associated with longer wait times in pediatric EDs (HR 0.76, 95% CI 0.63-0.92), non-metropolitan EDs (HR 0.75, 95% CI 0.64-0.89), and the Midwest region (HR 0.77, 95% CI 0.69-0.87). No differences in wait times were observed for children of Black race or other races.

CONCLUSIONS

Hispanic children experienced longer ED wait times across serial multivariable models, with significant differences limited to pediatric, metropolitan, and Midwest EDs. These results highlight the presence of disparities in access to prompt emergency care for children.

Machine Learning-Based Prediction of Hospital Admission Among Children in an Emergency Care Center

OBJECTIVES

Machine learning-based prediction of hospital admissions may have the potential to optimize patient disposition and improve clinical outcomes by minimizing both undertriage and overtriage in crowded emergency care. We developed and validated the predictive abilities of machine learning-based predictions of hospital admissions in a pediatric emergency care center.

METHODS

A prognostic study was performed using retrospectively collected data of children younger than 16 years who visited a single pediatric emergency care center in Osaka, Japan, between August 1, 2016, and October 15, 2019. Generally, the center treated walk-in children and did not treat trauma injuries. The main outcome was hospital admission as determined by the physician. The 83 potential predictors available at presentation were selected from the following categories: demographic characteristics, triage level, physiological parameters, and symptoms. To identify predictive abilities for hospital admission, maximize the area under the precision-recall curve, and address imbalanced outcome classes, we developed the following models for the preperiod training cohort (67% of the samples) and also used them in the 1-year postperiod validation cohort (33% of the samples): (1) logistic regression, (2) support vector machine, (3) random forest, and (4) extreme gradient boosting.

RESULTS

Among 88,283 children who were enrolled, the median age was 3.9 years, with 47,931 (54.3%) boys and 1985 (2.2%) requiring hospital admission. Among the models, extreme gradient boosting achieved the highest predictive abilities (eg, area under the precision-recall curve, 0.26; 95% confidence interval, 0.25-0.27; area under the receiver operating characteristic curve, 0.86; 95% confidence interval, 0.84-0.88; sensitivity, 0.77; and specificity, 0.82). With an optimal threshold, the positive and negative likelihood ratios were 4.22, and 0.28, respectively.

CONCLUSIONS

Machine learning-based prediction of hospital admissions may support physicians' decision-making for hospital admissions. However, further improvements are required before implementing these models in real clinical settings.

Clinical narrative-aware deep neural network for emergency department critical outcome prediction

Since early identification of potential critical patients in the Emergency Department (ED) can lower mortality and morbidity, this study seeks to develop a machine learning model capable of predicting possible critical outcomes based on the history and vital signs routinely collected at triage. We compare emergency physicians and the predictive performance of the machine learning model. Predictors including patients' chief complaints, present illness, past medical history, vital signs, and demographic data of adult patients (aged ≥ 18 years) visiting the ED at Shuang-Ho Hospital in New Taipei City, Taiwan, are extracted from the hospital's electronic health records. Critical outcomes are defined as in-hospital cardiac arrest (IHCA) or intensive care unit (ICU) admission. A clinical narrative-aware deep neural network was developed to handle the text-intensive data and standardized numerical data, which is compared against other machine learning models. After this, emergency physicians were asked to predict possible clinical outcomes of thirty visits that were extracted randomly from our dataset, and their results were further compared to our machine learning model. A total of 4,308 (2.5 %) out of the 171,275 adult visits to the ED included in this study resulted in critical outcomes. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) of our proposed prediction model is 0.874 and 0.207, respectively, which not only outperforms the other machine learning models, but even has better sensitivity (0.95 vs 0.41) and accuracy (0.90 vs 0.67) as compared to the emergency physicians. This model is sensitive and accurate in predicting critical outcomes and highlights the potential to use predictive analytics to support post-triage decision-making.

Prospective External Validation of a Commercial Model Predicting the Likelihood of Inpatient Admission From the Emergency Department

STUDY OBJECTIVE

Early notification of admissions from the emergency department (ED) may allow hospitals to plan for inpatient bed demand. This study aimed to assess Epic's ED Likelihood to Occupy an Inpatient Bed predictive model and its application in improving hospital bed planning workflows.

METHODS

All ED adult (18 years and older) visits from September 2021 to August 2022 at a large regional health care system were included. The primary outcome was inpatient admission. The predictive model is a random forest algorithm that uses demographic and clinical features. The model was implemented prospectively, with scores generated every 15 minutes. The area under the receiver operator curves (AUROC) and precision-recall curves (AUPRC) were calculated using the maximum score prior to the outcome and for each prediction independently. Test characteristics and lead time were calculated over a range of model score thresholds.

RESULTS

Over 11 months, 329,194 encounters were evaluated, with an incidence of inpatient admission of 25.4%. The encounter-level AUROC was 0.849 (95% confidence interval [CI], 0.848 to 0.851), and the AUPRC was 0.643 (95% CI, 0.640 to 0.647). With a prediction horizon of 6 hours, the AUROC was 0.758 (95% CI, 0.758 to 0.759,) and the AUPRC was 0.470 (95% CI, 0.469 to 0.471). At a predictive model threshold of 40, the sensitivity was 0.49, the positive predictive value was 0.65, and the median lead-time warning was 127 minutes before the inpatient bed request.

CONCLUSION

The Epic ED Likelihood to Occupy an Inpatient Bed model may improve hospital bed planning workflows. Further study is needed to determine its operational effect.

Predictors of Urgent Dialysis Following Ambulance Transport to the Emergency Department in Patients Treated With Maintenance Hemodialysis

Background

Patients receiving maintenance hemodialysis frequently require ambulance transport to the emergency department (ambulance-ED transport). Identifying predictors of outcomes after ambulance-ED transport, especially the need for timely dialysis, is important to health care providers.

Objective

The purpose of this study was to derive a risk-prediction model for urgent dialysis after ambulance-ED transport.

Design

Observational cohort study.

Setting and Patients

All ambulance-ED transports among incident and prevalent patients receiving maintenance hemodialysis affiliated with a regional dialysis program (catchment area of approximately 750 000 individuals) from 2014 to 2018.

Measurements

Patients' vital signs (systolic blood pressure, oxygen saturation, respiratory rate, and heart rate) at the time of paramedic transport and time since last dialysis were utilized as predictors for the outcome of interest. The primary outcome was urgent dialysis (defined as dialysis in a monitored setting within 24 hours of ED arrival or dialysis within 24 hours with the first ED patient blood potassium level >6.5 mmol/L) for an unscheduled indication. Secondary outcomes included, hospitalization, hospital length of stay, and in-hospital mortality.

Methods

A logistic regression model to predict outcomes of urgent dialysis. Discrimination and calibration were assessed using the C-statistic and Hosmer-Lemeshow test.

Results

Among 878 ED visits, 63 (7.2%) required urgent dialysis. Hypoxemia (odds ratio [OR]: 4.04, 95% confidence interval [CI]: 1.75-9.33) and time from last dialysis of 24 to 48 hours (OR: 3.43, 95% CI: 1.05-11.9) and >48 hours (OR: 9.22, 95% CI: 3.37-25.23) were strongly associated with urgent dialysis. A risk-prediction model incorporating patients' vital signs and time from last dialysis had good discrimination (C-statistic 0.8217) and calibration (Hosmer-Lemeshow goodness of fit P value .8899). Urgent dialysis patients were more likely to be hospitalized (63% vs 34%), but there were no differences in inpatient mortality or length of stay.

Limitations

Missing data, requires external validation.

Conclusion

We derived a risk-prediction model for urgent dialysis that may better guide appropriate transport and care for patients requiring ambulance-ED transport.

Early prediction of hospital admission of emergency department patients

OBJECTIVE

The early prediction of hospital admission is important to ED patient management. Using available electronic data, we aimed to develop a predictive model for hospital admission.

METHODS

We analysed all presentations to the ED of a tertiary referral centre over 7 years. To our knowledge, our data set of nearly 600 000 presentations is the largest reported. Using demographic, clinical, socioeconomic, triage, vital signs, pathology data and keywords in electronic notes, we trained a machine learning (ML) model with presentations from 2015 to 2020 and evaluated it on a held-out data set from 2021 to mid-2022. We assessed electronic medical records (EMRs) data at patient arrival (baseline), 30, 60, 120 and 240 min after ED presentation.

RESULTS

The training data set included 424 354 data points and the validation data set 53 403. We developed and trained a binary classifier to predict inpatient admission. On a held-out test data set of 121 258 data points, we predicted admission with 86% accuracy within 30 min of ED presentation with 94% discrimination. All models for different time points from ED presentation produced an area under the receiver operating characteristic curve (AUC) ≥0.93 for admission overall, with sensitivity/specificity/F1-scores of 0.83/0.90/0.84 for any inpatient admission at 30 min after presentation and 0.81/0.92/0.84 at baseline. The models retained lower but still high AUC levels when separated for short stay units or inpatient admissions.

CONCLUSION

We combined available electronic data and ML technology to achieve excellent predictive performance for subsequent hospital admission. Such prediction may assist with patient flow.

Quality improvement initiative reduces overcrowding on labour and delivery unit in a university hospital in Haiti

BACKGROUND

Following the first COVID-19 peak in 2020, came the seasonal childbirth peak at Hôpital Universitaire de Mirebalais (HUM). This peak is associated with overcrowding on the labour and delivery (L&D) ward. Lack of sufficient bed-space for sick neonates in the neonatal ICU at HUM, has led to overcrowding and lengthy stays of sick newborns on L&D. These conditions contribute to the subsequent lack of bed-space for newly postpartum mothers and potentially decreases quality of care for both new mothers and neonates.

METHODS

A Maternity Task Force was created by hospital leadership to address these urgent needs. The team's objective was to eliminate mothers and newborns laying on the floor in L&D. The Six-Sigma/DMAIC quality improvement methodology was used as the problem was urgent, demanded rapid results and centred around the process of patient flow in the institution. Process flow chart and Ishikawa diagrams were used to identify the root causes of the issues.

RESULTS

An average of 22% of postpartum women did not have a bed preintervention and 0% of postpartum women were laying on the floor post intervention. An average of 33% of newborns received paediatric care on the maternity ward pre-intervention compared with an average of 17% postintervention. The team did not achieve its objective for this second indicator, which was to have less than 10% of sick newborns on the maternity ward receiving paediatric care.

CONCLUSION

HUM hospital leadership took the vital decision to form the Maternity Task Force to make changes, which consequently led to a sustainable positive and lasting impact on the lives of new mothers and their babies at the institution. The objective of 0 postpartum mothers and newborns on the ground was achieved and fewer newborns receive intensive paediatric care on the maternity ward as a result of our interventions.

Patients' perspectives on care, communication, and teamwork in the emergency department

BACKGROUND

The work of healthcare professionals (HCPs) in the emergency department (ED) involves effective communication and efficient teamwork, which may be perceived differently by patients and HCPs. Therefore, it is important to explore patient perspectives of information exchange and clinical assessment.

AIM

To evaluate experiences of care, communication, and teamwork from ED patients' perspectives.

METHODS

Semi-structured interviews were conducted with 17 patients who were assessed in a Swedish ED during Spring 2021. Thematic analysis was used.

RESULTS

Participants' experiences reflected the complex environment of the ED. Findings emphasize the importance of information exchange in relation to a caring approach. Three themes emerged: the need for a caring approach by HCPs towards patients'; the need for dialogue between patient and HCPs; and the need for information on ED environment constraints.

CONCLUSIONS

Patients felt comforted when they experienced a caring empathic approach from the HCPs. For example, patients valued an individual holistic approach rather than feeling that they were being objectified by their medical conditions. This was important in coping with the anxiety caused by a stressful ED environment. There is a critical need for effective exchange of information between patients and HCPs.

Association between Covid-19 surge and emergency department patient flow and experience

BACKGROUND

Preparations for Covid-19 in the Netherlands included hospital reconfigurations to increase capacity for the expected surge at the emergency department (ED). We describe patients' ED length of stay (LOS), crowding and experiences of patients with respiratory complaints during the first Covid-19 peak.

METHODS

Retrospective analysis of demand, ED LOS, crowding, and a patient experience survey during a 12-week period in 2020 and similar periods in 2018 and 2019. Crowding levels were calculated using the National ED OverCrowding Scale.

RESULTS

The number of patients with respiratory complaints increased significantly, while total ED numbers were unchanged. Although presentation during the Covid-19 peak and needing hospital admission were associated with a longer ED LOS in patients with respiratory complaints, significantly less crowding occurred compared with the 2018 and 2019 periods. Increased ED LOS was associated with lower patient experience scores.

CONCLUSION

Advanced warning and its associated preparation within the hospital and the community prevented significant delays in ED throughput during the first Covid-19 peak.

Engineering a Clinical Microsystem to Decrease Workplace Violence for Medically and Psychiatrically Concurrently Decompensated Patients

BACKGROUND

Hospitalized medical patients with concurrently decompensated psychiatric and medical conditions experience worse clinical outcomes. Health care providers caring for this patient population are at increased risk of workplace violence. The authors sought to understand the effects of a clinical microsystem specifically designed to care for patients too psychiatrically ill for medical units and too medically ill for psychiatry units.

METHODS

The research team performed a quality improvement study in which a medicine-psychiatry co-managed clinical microsystem incorporating high performance teamwork principles was engineered in an urban academic medical center to improve patient and staff safety, as well as operational outcomes. Poisson regression was performed to determine differences between workplace violence events, falls, 30-day emergency department (ED) revisits, and hospital readmissions, comparing the baseline period to the intervention period.

RESULTS

There were 321 patients discharged in the baseline period and 310 during the intervention period. Workplace violence events decreased by 65.6% (incidence rate ratio [IRR] 0.34, 95% confidence interval [CI] 0.20-0.57, p < 0.001) after implementation of the clinical microsystem when compared to the baseline period. The rate of ED utilization at 30 days postdischarge also decreased from 30.6% at baseline to 21.0% postintervention (adjusted odds ratio [aOR] 0.60, 95% CI 0.42-0.87, p = 0.006). No differences were detected in falls and 30-day readmissions.

CONCLUSION

For patients with concurrently decompensated medical and psychiatric conditions, the incidence of workplace violence and postdischarge ED utilization can be improved by creating a clinical microsystem that integrates changes to both the physical environment and teamwork processes.

Shorter laboratory turnaround time is associated with shorter emergency department length of stay: a retrospective cohort study

BACKGROUND

A longer emergency department length of stay (EDLOS) is associated with poor outcomes. Shortening EDLOS is difficult, due to its multifactorial nature. A potential way to improve EDLOS is through shorter turnaround times for diagnostic testing. This study aimed to investigate whether a shorter laboratory turnaround time (TAT) and time to testing (TTT) were associated with a shorter EDLOS.

METHODS

A retrospective cohort study was performed, including all visits to the emergency department (ED) of an academic teaching hospital from 2017 to 2020 during which a standardized panel of laboratory tests had been ordered. TTT was calculated as the time from arrival in the ED to the ordering of laboratory testing. TAT was calculated as the time from test ordering to the reporting of the results, and was divided into a clinical and a laboratory stage. The outcome was EDLOS in minutes. The effect of TTT and TAT on EDLOS was estimated through a linear regression model.

RESULTS

In total, 23,718 ED visits were included in the analysis. Median EDLOS was 199.0 minutes (interquartile range [IQR] 146.0-268.0). Median TTT was 7.0 minutes (IQR 2.0-12.0) and median TAT was 51.1 minutes (IQR 41.1-65.0). Both TTT and TAT were positively associated with EDLOS. The laboratory stage comprised a median of 69% (IQR 59-78%) of total TAT.

CONCLUSION

Longer TTT and TAT are independently associated with longer EDLOS. As the laboratory stage predominantly determines TAT, it provides a promising target for interventions to reduce EDLOS and ED crowding.

Impact of the 24-hour time target policy for emergency departments in South Korea: a mixed method study in a single medical center

BACKGROUND

In South Korea, after the spread of the Middle East Respiratory Syndrome epidemic was aggravated by long stays in crowded emergency departments (EDs), a 24-hour target policy for EDs was introduced to prevent crowding and reduce patients' length of stay (LOS). The policy requires at least 95% of all patients to be admitted, discharged or transferred from an ED within 24 hours of arrival. This study analyzes the effects of the 24-hour target policy on ED LOS and compliance rates and describes the consequences of the policy.

METHODS

A mixed-methods approach was applied to a retrospective observational study of ED visits combined with a survey of medical professionals. The primary measure was ED LOS, and the secondary measure was policy compliance rate which refers to the proportion of patient visits with a LOS shorter than 24 hours. Patient flow, quality of care, patient safety, staff workload, and staff satisfaction were also investigated through surveys. Mann-Whitney U and χ2 tests were used to compare variables before and after the introduction of the policy.

RESULTS

The median ED LOS increased from 3.9 hours (interquartile range [IQR] = 2.1-7.6) to 4.5 hours (IQR = 2.5-8.5) after the policy was introduced. This was likely influenced by the average monthly number of patients, which greatly increased from 4819 (SD = 340) to 5870 (SD = 462) during the same period. The proportion of patients with ED LOS greater than 24 hours remained below5% only after 6 months of policy implementation, but the number of patients whose disposition was decided at 23 hours increased by 4.84 times. Survey results suggested that patient flow and quality of care improved slightly, while the workload of medical staff worsened.

CONCLUSIONS

After implementing the 24-hour target policy, the proportion of patients whose ED LOS exceeded 24 hours decreased, even though the median ED LOS increased. However, the unintended consequences of the policy were observed such as increased medical professional workload and abrupt expulsion of patients before 24 hours.

Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study

BACKGROUND

The burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews.

OBJECTIVES

The goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers.

METHODS

We evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms.

RESULTS

The overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73).

CONCLUSION

The data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.

Overcrowding analysis in emergency department through indexes: a single center study

Introduction

Overcrowding in the Emergency Department (ED) is one of the major issues that must be addressed in order to improve the services provided in emergency circumstances and to optimize their quality. As a result, in order to help the patients and professionals engaged, hospital organizations must implement remedial and preventative measures. Overcrowding has a number of consequences, including inadequate treatment and longer hospital stays; as a result, mortality and the average duration of stay in critical care units both rise. In the literature, a number of indicators have been used to measure ED congestion. EDWIN, NEDOCS and READI scales are considered the most efficient ones, each of which is based on different parameters regarding the patient management in the ED.

Methods

In this work, EDWIN Index and NEDOCS Index have been calculated every hour for a month period from February 9th to March 9th, 2020 and for a month period from March 10th to April 9th, 2020. The choice of the period is related to the date of the establishment of the lockdown in Italy due to the spread of Coronavirus; in fact on 9 March 2020 the Italian government issued the first decree regarding the urgent provisions in relation to the COVID-19 emergency. Besides, the Pearson correlation coefficient has been used to evaluate how much the EDWIN and NEDOCS indexes are linearly dependent.

Results

EDWIN index follows a trend consistent with the situation of the first lockdown period in Italy, defined by extreme limitations imposed by Covid-19 pandemic. The 8:00–20:00 time frame was the most congested, with peak values between 8:00 and 12:00. on the contrary, in NEDOCS index doesn’t show a trend similar to the EDWIN one, resulting less reliable. The Pearson correlation coefficient between the two scales is 0,317.

Conclusion

In this study, the EDWIN Index and the NEDOCS Index were compared and correlated in order to assess their efficacy, applying them to the case study of the Emergency Department of “San Giovanni di Dio e Ruggi d’Aragona” University Hospital during the Covid-19 pandemic. The EDWIN scale turned out to be the most realistic model in relation to the actual crowding of the ED subject of our study. Besides, the two scales didn’t show a significant correlation value.

Do All Adult Psychiatric Patients Need Routine Laboratory Evaluation and an Electrocardiogram?

BACKGROUND

Acute psychiatric presentations account for a significant number of emergency department (ED) visits. These patients require assessment by the emergency physician and often need further evaluation by a psychiatrist, who may request routine laboratory evaluation and an electrocardiogram (ECG).

CLINICAL QUESTION

Do all adult psychiatric patients need routine laboratory evaluation and an ECG?

EVIDENCE REVIEW

Studies retrieved included 2 prospective, observational studies and 7 retrospective studies. These studies evaluate the utility of laboratory analysis in all patients presenting a psychiatric complaint and its impact on patient management and disposition.

CONCLUSION

Based upon the available literature, routine laboratory analysis and ECG for all patients presenting with a psychiatric complaint are not recommended. Clinicians should consider the individual patient, clinical situation, and comorbidities when deciding to obtain further studies such as laboratory analysis. © 2022 Elsevier Inc.

[The way to routine data from 16 emergency departments for cross-sectoral health services research : Experiences, challenges and solution approaches from the extraction of pseudonymous data for the INDEED project]

BACKGROUND

In Germany there is currently no health reporting on cross-sectoral care patterns in the context of an emergency department care treatment. The INDEED project (Utilization and trans-sectoral patterns of care for patients admitted to emergency departments in Germany) collects routine data from 16 emergency departments, which are later merged with outpatient billing data from 2014 to 2017 on an individual level.

AIM

The methodological challenges in planning of the internal merging of routine clinical and administrative data from emergency departments in Germany up to the final data extraction are presented together with possible solution approaches.

METHODS

Data were selected in an iterative process according to the research questions, medical relevance, and assumed data availability. After a preparatory phase to clarify formalities (including data protection, ethics), review test data and correct if necessary, the encrypted and pseudonymous data extraction was performed.

RESULTS

Data from the 16 cooperating emergency departments came mostly from the emergency department and hospital information systems. There was considerable heterogeneity in the data. Not all variables were available in every emergency department because, for example, they were not standardized and digitally available or the extraction effort was judged to be too high.

CONCLUSION

Relevant data from emergency departments are stored in different structures and in several IT systems. Thus, the creation of a harmonized data set requires considerable resources on the part of the hospital as well as the data processing unit. This needs to be generously calculated for future projects.

Emergency department condition acuity, length of stay, and revisits among deaf and hard-of-hearing patients: A retrospective chart review

OBJECTIVE

Deaf and hard-of-hearing (DHH) patients are understudied in emergency medicine health services research. Theory and limited evidence suggest that DHH patients are at higher risk of emergency department (ED) utilization and poorer quality of care. This study assessed ED condition acuity, length of stay (LOS), and acute ED revisits among DHH patients. We hypothesized that DHH patients would experience poorer ED care outcomes.

METHODS

We conducted a retrospective chart review of a single health care system using data from a large academic medical center in the southeast United States. Data were received from the medical center's data office, and we sampled patients and encounters from between June 2011 and April 2020. We compared DHH American Sign Language (ASL) users (n = 108), DHH English speakers (n = 358), and non-DHH English speakers (n = 302). We used multilevel modeling to assess the differences among patient segments in outcomes related to ED use and care.

RESULTS

As hypothesized, DHH ASL users had longer ED LOS than non-DHH English speakers, on average 30 min longer. Differences in ED condition acuity, measured through Emergency Severity Index and triage pain scale, were not statistically significant. DHH English speakers represented a majority (61%) of acute ED revisit encounters.

CONCLUSIONS

Our study identified that DHH ASL users have longer ED LOS than non-DHH English speakers. Additional research is needed to further explain the association between DHH status and ED care outcomes (including ED LOS and acute revisit), which may be used to identify intervention targets to improve health equity.

Forecasting admissions in psychiatric hospitals before and during Covid-19: a retrospective study with routine data

The COVID-19 pandemic has strong effects on most health care systems. Forecasting of admissions can help for the efficient organisation of hospital care. We aimed to forecast the number of admissions to psychiatric hospitals before and during the COVID-19 pandemic and we compared the performance of machine learning models and time series models. This would eventually allow to support timely resource allocation for optimal treatment of patients. We used admission data from 9 psychiatric hospitals in Germany between 2017 and 2020. We compared machine learning models with time series models in weekly, monthly and yearly forecasting before and during the COVID-19 pandemic. A total of 90,686 admissions were analysed. The models explained up to 90% of variance in hospital admissions in 2019 and 75% in 2020 with the effects of the COVID-19 pandemic. The best models substantially outperformed a one-step seasonal naïve forecast (seasonal mean absolute scaled error (sMASE) 2019: 0.59, 2020: 0.76). The best model in 2019 was a machine learning model (elastic net, mean absolute error (MAE): 7.25). The best model in 2020 was a time series model (exponential smoothing state space model with Box-Cox transformation, ARMA errors and trend and seasonal components, MAE: 10.44). Models forecasting admissions one week in advance did not perform better than monthly and yearly models in 2019 but they did in 2020. The most important features for the machine learning models were calendrical variables. Model performance did not vary much between different modelling approaches before the COVID-19 pandemic and established forecasts were substantially better than one-step seasonal naïve forecasts. However, weekly time series models adjusted quicker to the COVID-19 related shock effects. In practice, multiple individual forecast horizons could be used simultaneously, such as a yearly model to achieve early forecasts for a long planning period and weekly models to adjust quicker to sudden changes.

Impact of iodinated contrast allergies on emergency department operations

OBJECTIVES

Adverse reactions to intravenous (IV) iodinated contrast media are classified by the American College of Radiology (ACR) Manual on Contrast Media as either allergic-like (ALR) or physiologic (PR). Premedication may be beneficial for patients who have prior documented mild or moderate ALR. We sought to perform a retrospective analysis of patients who received computed tomography (CT) imaging in our emergency department (ED) to establish whether listing of an iodinated contrast media allergy results in a delay in care, increases the use of non-contrast studies, and to quantify the incidence of listing iodinated contrast allergies which do not necessitate premedication.

METHODS

We performed a retrospective analysis of CT scans performed in our academic medical center ED during a 6-month period. There were 12,737 unique patients of whom 454 patients had a listed iodinated contrast allergy. Of these, 106 received IV contrast and were categorized as to whether premedication was necessary. Descriptive statistics were used to evaluate patient demographics, clinical characteristics, and operational outcomes. A multivariate linear regression model was used to predict time from order to start (OTS time) of CT imaging while controlling for co-variates.

RESULTS

Non-allergic patients underwent contrast-enhanced CT imaging at a significantly higher rate than allergic patients (45.9% vs. 23.3%, p < 0.01). The OTS time for allergic patients who underwent contrast-enhanced CT imaging was 360 min and significantly longer than the OTS time for non-allergic patients who underwent contrast-enhanced CT imaging (118 min, p < 0.001). Of the 106 allergic patients who underwent contrast-enhanced CT imaging, 27 (25.5%) did not meet ACR criteria for necessitating premedication. The average OTS time for these 27 patients was 296 min, significantly longer than the OTS for non-allergic patients (118 min, p < 0.01) and did not differ from the OTS time for the 79 patients who did meet premedication criteria (382 min, p = 0.23). A multivariate linear regression showed that OTS time was significantly longer if a contrast allergy was present (p < 0.001).

CONCLUSION

A chart-documented iodinated contrast allergy resulted in a significant increase in time to obtain a contrast-enhanced CT study. This delay persisted among patients who did not meet ACR criteria for premedication. Appropriately deferring premedication could potentially reduce the ED length-of-stay by over 4 h for these patients.

Reducing time to admission in emergency department patients: a cross-functional quality improvement project

Crowding and boarding are common issues facing emergency departments (EDs) in the USA. These issues have negative effects on efficiency, patient care, satisfaction and healthcare team well-being. Data from an audit of the admissions process at a large, urban, academic US ED demonstrated a lengthy process, exceeding national benchmarks in both length of stay and boarding of admitted patients.

We performed a pre–post study between July 2019 and July 2021 focused on the first step of the admission process at our institution, the time to bed request. All patients admitted to an internal medicine (IM) floor team from the ED were included in the study. The primary outcome was the time from decision to admit by the emergency medicine physician to placement of the bed request order by the IM physician. Quality improvement (QI) occurred in three phases: an initial preintervention process and electronic health record change to better capture admission times, a primary intervention focused on process change and provider education and a second intervention focused on improvements to provider communication.

During the study period, 25 183 patients were admitted to IM floor teams and met inclusion criteria. Prior to the primary intervention, the mean time from ED decision to admit to IM placement of the bed request order was 75.1 min. Postintervention, the mean time decreased to 39.7 min, a statistically significant improvement of 35.4 min (p value <0.0001).

This QI project demonstrates the ability of interventions to reduce the time to admission bed request order, a key step in the overall admission process and a contributor to boarding at our institution. In making process changes, the team also reduced provider handoffs and improved provider communication.

Insurance and inpatient admission of emergency department patients with depression in the United States

OBJECTIVE

To estimate the contribution of insurance on rates of inpatient admission for emergency department visits with depression diagnoses.

METHODS

We identified 3,681,581 visits for depression in the National Emergency Department Sample (2007-2018). We classified them by concurrent injury, suicidal ideation, or neither. Payer categories were defined, non-exclusively, as Medicare, Medicaid, private insurance, and no insurance. Logistic regression models, adjusted for age, year, and comorbidities, were used to describe differences in rates of inpatient admission by payer type, stratified by visit features.

RESULTS

Rates of inpatient admission for visits with neither injury nor suicidal ideation (31.9%; 95%CI, 30.8-33.0) were lower than for visits with injury (37.9%; 95%CI, 36.7-39.1) or with suicidal ideation (39.7%; 95%CI, 37.3-42.1). Rates of admission were significantly lower for those without insurance (26.6%; 95%CI, 25.5-27.8) than for those with insurance (37.1%; 95%CI, 36.1-38.1). In adjusted models, insurance was associated with increased likelihood (OR = 1.81, 95%CI, 1.69-1.94) of admission. Insurance continued to be a significant predictor of admission among ED visits for depression with concurrent injury (OR = 1.39; 95%CI, 1.29-1.51).

CONCLUSION

After controlling for demographic characteristics and medical comorbidities, patients with depression who have insurance are significantly more likely to be admitted to the hospital compared to those without insurance.

Potentially avoidable interfacility transfers following reduced emergency department volumes due to COVID-19 "Safer-at-Home" orders

OBJECTIVES

We sought to assess if a state-wide lockdown implemented due to COVID-19 was associated with increased odds of being a potentially avoidable transfer (PAT).

METHODS

We conducted a retrospective observational analysis using hospital administrative data of interfacility ED-to-ED transfers to a single, quaternary care adult ED after "Safer at Home" orders were issued March 23rd, 2020 in [Blinded for submission]. Using the PAT classification to identify transfers rapidly discharged from the ED or hospital and may not require in-person care, we used a multivariable logistic regression model to examine the association of the lockdown order with odds of a transfer being a PAT. We compared the period January 1, 2018 to March 23, 2020 with March 24, 2020 to September 30, 2020, adjusting for seasonality, patient, and situational factors.

RESULTS

There were 20,978 ED-to-ED transfers from during this period that were eligible and 4806 (23%) that met PAT criteria. While the first month post-lockdown saw a decrease in PATs (28%), this was not sustained. In the multivariable model there was a significant seasonal effect; May through September had the highest number of transfers as well as PATs. After adjusting for seasonality, the lockdown was not associated with PATs (adjusted odds ratio [aOR] 0.99, 95% CI 0.2, 5.2) and PATs decreased over time.

CONCLUSIONS

We did not find an effect of the COVID-19 lockdown on PATs though there was a considerable seasonal effect and an overall downward trend in PATs over time.

Making Space to Save Lives: A Critical Role for the Hospitalist During Mass Casualty Incidents

OBJECTIVE

An effective hospital response to mass casualty incidents (MCIs) requires rapid mobilization of personnel capable of caring for critically ill trauma patients and availability of resuscitation resources.

METHODS

Hospitals facing an MCI wrestle with the challenge of immediately adjusting their overextended clinical operations to resuscitate a large number of rapidly arriving patients without compromising the care of existing patients.

RESULTS

Hospitalists are well positioned to add significant value by off-loading the emergency department (ED) given their broad clinical expertise. We describe our institution's protocol to generate immediate and sustained surge capacity by integrating our hospitalist service into MCI response.

CONCLUSIONS

Our protocol details the safe and rapid transfer of care of existing ED patients to hospitalist teams to make ED staff and space available to care for incoming MCI patients.

Operations management solutions to improve ED patient flows: evidence from the Italian NHS

Background

Overcrowding occurs when the identified need for emergency services outweighs the available resources in the emergency department (ED). Literature shows that ED overcrowding impacts the overall quality of the entire hospital production system, as confirmed by the recent COVID-19 pandemic. This study aims to identify the most relevant variables that cause ED overcrowding using the input-process-output model with the aim of providing managers and policy makers with useful hints for how to effectively redesign ED operations.

Methods

A mixed-method approach is used, blending qualitative inquiry with quantitative investigation in order to: i) identifying and operationalizing the main components of the model that can be addressed by hospital operation management teams and ii) testing and measuring how these components can influence ED LOS.

Results

With a dashboard of indicators developed following the input-process-output model, the analysis identifies the most significant variables that have an impact on ED overcrowding: the type (age and complexity) and volume of patients (input), the actual ED structural capacity (in terms of both people and technology) and the ED physician-to-nurse ratio (process), and the hospital discharging process (output).

Conclusions

The present paper represents an original contribution regarding two different aspects. First, this study combines different research methodologies with the aim of capturing relevant information that by relying on just one research method, may otherwise be missed. Second, this study adopts a hospitalwide approach, adding to our understanding of ED overcrowding, which has thus far focused mainly on single aspects of ED operations.

Diagnostic Performance of the Rapid Antigen Test as a Screening Tool for SARS-CoV-2 Infection in the Emergency Department

The rapid antigen test (RAT) has been adopted as a screening tool for SARS-CoV-2 infection in many emergency departments (EDs). We aimed to investigate the diagnostic value of the accuracy of the SARS-CoV-2 RAT as a screening tool in the ED. This retrospective observational study included patients who underwent both RAT and RT−PCR and visited the ED from 1 December 2021 to 15 March 2022. RAT and RT−PCR were performed by appropriately trained physicians. We performed detailed analyses using the E gene cyclic threshold (Ct) values of RT−PCR. Out of a total of 1875 patients, 348 (18.6%) had positive and 1527 (81.4%) had negative RT−PCR results. The overall sensitivity, specificity, positive predictive value, and negative predictive value of the RAT were 67.8%, 99.9%, 99.6%, and 93.2%, respectively. The E gene Ct value was significantly lower in the RAT-positive patients than in the RAT-negative patients (18.5 vs. 25.3, p < 0.001). When the E gene Ct cutoff was 30.0, 25.0, 20.0, and 15.0, the sensitivity of the RAT was 71.9%, 80.3%, 93.0%, and 97.8%, respectively. The sensitivity of the RAT could be considered high in patients with a high viral load, and the RAT could be used as a screening tool in the ED.

Apart from the Medical Complaints, Why do Patients Use Emergency Medical Services? Results of a Patient Survey

INTRODUCTION

Many countries face an increased use of emergency medical services (EMS) with a decreasing percentage of life-threatening complaints. Though there is a broad discussion among experts about the cause, patients' self-perceived, non-medical reasons for using EMS remain largely unknown.

METHODS

The written survey included EMS patients who had≥1 case of prehospital emergency care in 2016. Four German health insurance companies sent out postal questionnaires to 1312 insured patients. The response rate was 20%; 254 questionnaires were eligible for descriptive and interferential analyses (t-tests, chi2-tests, logistic models).

RESULTS

The majority of respondents indicated that their EMS use was due to an emergency or someone else's decision (≥84%; multiple checks allowed); 56% gave need for a quick transport as a reason. Other frequently stated reasons addressed the health care system (e. g., complaints outside of physicians' opening hours) and insecurity/anxiety about one's state of health (>45% of the respondents). "Social factors" were similarly important (e. g., 42% affirming, "No one could give me a ride to the emergency department or doctor's office."). Every fifth person had contact with other emergency care providers prior to EMS use. Respondents negating an emergency as a reason were less likely to confirm wanting immediate medical care on site or quick transports compared to those affirming an emergency. Patients using EMS at night more often denied having an emergency compared to patients with access to care during the day.

CONCLUSION

The study identified a bundle of reasons leading to EMS use apart from medical complaints. Attempts for needs-oriented EMS use should essentially include optimization of the health care and social support system and measures to reduce patients' insecurity.

Characteristics of frequent emergency department users in Korea: a 4-year retrospective analysis using Korea Health Panel Study data

Objective

We aimed to investigate the characteristics of frequent emergency department (ED) users in Korea.

Methods

We analyzed the Korea Health Panel Study data of a sampled population from the 2005 Population Census of Korea data, and adults (age ≥18 years) who visited the ED at least once a year between 2014 and 2017 were included in the study. People who visited three or more times a year were classified as frequent users. We compared demographic, socioeconomic, and health-related factors between nonfrequent and frequent users. We used a multivariable logistic regression analysis to determine factors related to frequent ED visits. We also compared the characteristics of ED use in both nonfrequent and frequent users.

Results

A total of 5,090 panels were included, comprising 6,853 visits. Frequent users were 333 (6.5% of all panels), and their ED visits were 1,364 (19.9% of all ED visits). In the multivariable regression analysis, medical aid coverage (adjusted odds ratio [aOR] of the National Health Service coverage, 0.55; 95% confidence interval [CI], 0.40–0.75), unemployment (aOR of employment, 0.72; 95% CI, 0.56–0.91), prior ward admission in a year (aOR, 2.14; 95% CI, 1.67–2.75), and frequent outpatient department use (aOR, 1.72; 95% CI, 1.35–2.20) were associated with frequent use. Moreover, frequent users visited the ED of public hospitals more often than than nonfrequent users (19.2% vs. 9.8%). Medical problems rather than injury/poisoning were the more common reasons for visiting the ED (84.5% vs. 71.2%).

Conclusion

We found that frequent ED users were likely to be those with socioeconomic disadvantage or with high demand for medical service. Based on this study, further studies on interventions to reduce frequent ED use are required for better ED services.

The effect of a specialist paramedic primary care rotation on appropriate non-conveyance decisions (SPRAINED) study: a controlled interrupted time series analysis

INTRODUCTION

NHS ambulance service non-conveyance rates in the United Kingdom are around 30%, despite an increase in non-emergency cases and a national policy of integrating urgent and emergency care to provide patients with the 'right care, in the right place, at the right time'. Emergency department overcrowding is a significant issue for patients, resulting in poorer quality of care, increased healthcare costs and potentially, increased mortality. It also contributes to increased ambulance turnaround times. Yorkshire Ambulance Service NHS Trust (YAS) introduced a specialist paramedic (SP) to improve the management of lower acuity cases, but non-conveyance rates in this group have not been as high as expected.In 2018, Health Education England funded a pilot scheme to rotate paramedics into a range of healthcare settings and in YAS, 10 SPs undertook a 10-week placement in a GP practice. This study aimed to evaluate whether a primary care placement appropriately increased the level and trend of non-conveyance decisions made by SPs following a 10-week GP placement, in a cost-effective manner.

METHODS

We conducted a controlled interrupted time series analysis using data from incidents between 1 June 2017 and 31 December 2019, to study appropriate non-conveyance rates before and after a GP placement. A costing analysis, examining the average cost per appropriate non-conveyance achieved for patients receiving care from intervention group SPs pre- and post-placement, was also conducted.

RESULTS

A total of 7349 incidents attended by intervention group SPs were eligible for inclusion. Following removal of cases with missing data, 5537 (75.3%) cases remained. Post-placement, the intervention group demonstrated an increase in appropriate non-conveyance rate of 35.0% (95% CI 23.8%-46.2%), and a reduction in the trend of appropriate non-conveyance of -1.2% (95% CI -2.8%-0.5%), relative to the control group.Post-placement, the cost per appropriate non-conveyance for intervention group SPs was a mean of £509.41 (95% bootstrapped CI £454.92-£564.26) versus £1257.81 (95% bootstrapped CI £1233.42-£1283.95) for the same group in the pre-placement phase.

CONCLUSION

In this single UK NHS ambulance service study, we found a clinically important and statistically significant increase in appropriate non-conveyance rates by SPs who had completed a 10-week GP placement. This improvement persisted for the 12-month period following the placement and demonstrated cost savings compared to usual care.

Are 5-level triage systems improved by using a symptom based approach?-a Danish cohort study

Background

Five-level triage systems are being utilized in Danish emergency departments with and without the use of presenting symptoms. The aim of this study was to validate and compare two 5-level triage systems used in Danish emergency departments: “Danish Emergency Process Triage” (DEPT) based on a combination of vital signs and presenting symptoms and a locally adapted version of DEPT (VITAL-TRIAGE) using vital signs only.

Methods

This was a retrospective cohort using data from five Danish emergency departments. All patients attending an emergency department during the period of 1 April 2012 until 31 December 2015 were included. Validity of the two triage systems was assessed by comparing urgency categories determined by each triage system with critical outcomes: admission to Intensive care unit (ICU) within 24 h, 2-day mortality, diagnosis of critical illness, surgery within 48 h, discharge within 4 h and length of hospital stay.

Results

We included 632,196 ED contacts. Sensitivity for 24-h ICU admission was 0.79 (95% confidence interval 0.78–0.80) for DEPT and 0.44 (0.41–0.47) for VITAL-TRIAGE. The sensitivity for 2-day mortality was 0.69 (0.67–0.70) for DEPT and 0.37 (0.34–0.41) for VITAL-TRIAGE. The sensitivity to detect diagnoses of critical illness was 0.48 (0.47–0.50) for DEPT and 0.09 (0.08–0.10) for VITAL-TRIAGE. The sensitivity for predicting surgery within 48 h was 0.30 (0.30–0.31) in DEPT and 0.04 (0.04–0.04) in VITAL-TRIAGE. Length of stay was longer in VITAL-TRIAGE than DEPT. The sensitivity of DEPT to predict patients discharged within 4 h was 0.91 (0.91–0.92) while VITAL-TRIAGE was higher at 0.99 (0.99–0.99). The odds ratio for 24-h ICU admission and 2-day mortality was increased in high-urgency categories of both triage systems compared to low-urgency categories.

Conclusions

High urgency categories in both triage systems are correlated with adverse outcomes. The inclusion of presenting symptoms in a modern 5-level triage system led to significantly higher sensitivity measures for the ability to predict outcomes related to patient urgency. DEPT achieves equal prognostic performance as other widespread 5-level triage systems.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13049-022-01016-2.

Psychiatric patients at the emergency department: factors associated with length of stay and likelihood of hospitalization

Emergency department (ED) care for psychiatric patients is currently understudied despite being highly utilized. Therefore, we aimed to analyze psychiatric patients' length of stay (LOS) and LOS-related factors at the ED and to investigate and quantify the likelihood of being hospitalized after an emergency psychiatric evaluation. Charts of 408 individuals who sought help at the ED were retrospectively assessed to identify patients' sociodemographic and clinical data upon ED admission and discharge. All interventions performed at the ED (e.g., medications, hospitalization, clinical advice at discharge) were collected as well. The LOS for psychiatric patients was relatively short (6.5 h), and substance/alcohol intoxication was the main factor impacting LOS. Upon ED arrival, hospitalized patients were mostly men, most often had a yellow/severe triage code, and most often had a positive history of psychiatric illness, psychotic symptoms, euphoric mood, or suicidal ideation. Manic symptoms and suicidal ideation were the conditions most frequently leading to hospitalization. Given the paucity of real-world data on psychiatric patients' LOS and outcomes in the ED context, our findings show that psychiatric patients are evaluated in a reasonable amount of time. Their hospitalization is mostly influenced by clinical conditions rather than predisposing (e.g., age) or system-related factors (e.g., mode of arrival).

Improving the Flow: Optimization of Available Triage Standing Medication Orders in the Pediatric Emergency Department

BACKGROUND

Emergency department (ED) overcrowding has led to longer waits for patients to be seen, treated, and released. This has been coupled with an increased number of patients who leave without ever being seen by an ED provider. Improving patient throughput and decreasing ED patient length of stay while continuing to provide high-quality care is of paramount importance. Optimization of available standing orders for fever, pain, and/or nausea and vomiting at the time of nurse triage may offer an opportunity to improve patient outcomes, decrease time to medication (TTM) administration for ill and injured patients, and decrease length of stay.

PATIENTS AND METHODS

This was a retrospective cohort patient analysis for patients younger than 18 years of age presenting to the ED before (February 2019) and after (February 2020) an intensive campaign aimed at educating the triage nurses regarding the use of ED standing orders for acetaminophen, ibuprofen, and ondansetron. The investigators sought to evaluate percentage change in nurse triage-initiated (NTI) orders between the 2 time frames. Secondary outcomes evaluated TTM, documented adverse effects, percentage of inappropriate medication administrations, and correlate TTMs to ED discharge.

RESULTS

The percentage of patients who received an NTI medication was 49% in 2019 and 58% in 2020 (P = 0.257). For NTI orders, TTM initiation decreased in the postintervention group from an average of 45 minutes down to 37.6 minutes. In the overall cohort, TTM administration in the NTI group was a mean of 41.2 minutes compared with 75.9 minutes in the provider-initiated group (P < 0.0001). Length of stay was the same for the NTI versus the provider-initiated group in overall cohort (134.4 vs 142.9, P = 0.4303). No adverse events were identified.

CONCLUSIONS

Nurse-initiated medication administration at the time of triage offers an opportunity to get patients vital treatment more quickly. Medications administrated earlier in the pediatric ED encounter have a correlation to improved throughput and higher possibility of quicker discharge from the ED. Further research is needed to fully assess the impact of continuing education on improving utilization of triage standing orders and the impact on ED throughput.

The Effect of Human Supervision on an Electronic Implementation of the Canadian Triage Acuity Scale (CTAS)

BACKGROUND

Most electronic emergency department (ED) triage systems allow nurses to modify computer-generated triage scores. It is currently unclear how this affects triage validity.

OBJECTIVE

Are nurse-generated triage scores more strongly associated with rates of admission, intensive care unit (ICU) consultation, and mortality than computer-generated scores?

METHODS

Retrospective observational cohort study of all adult visits to a tertiary ED. An electronic implementation of the Canadian Triage Acuity Scale (CTAS) generated a CTAS score for each visit. In some cases, the triage nurse overwrote the computer-generated CTAS score with a score they felt was more appropriate. Among visits with nurse-modified triage scores, we compared the rate of acuity-related outcomes (mortality, ICU consultation, hospital admission) in each CTAS level as categorized by nurse-generated vs. computer-generated scores.

RESULTS

In a cohort of 229,744 patients, 19,566 (8.51%) had nurse-modified triage scores. Most modifications consisted of assigning a higher acuity triage score than recommended by the computer. Visits with triage scores 1-2 according to the nurse-generated scores had the same or higher rates of the acuity outcomes than visits that were CTAS 1-2 according to the computer-generated CTAS scores. Conversely, visits with triage scores 4-5 according to the nurse-generated scores had lower rates of the outcomes than visits that were CTAS 4-5 according to the computer-generated CTAS scores.

CONCLUSIONS

Nursing supervision of the computer-automated CTAS triage system was associated with fewer hospital admissions, ICU consultations, and deaths in the triage score 4-5 categories, suggesting a safer triage process than the automated CTAS algorithm alone.