Crowding is the strongest predictor of left without being seen risk in a pediatric emergency department

Changes in Behavioral Health Visits, Operations, and Boarding in a Pediatric Emergency Department

STUDY OBJECTIVE

Over the past decade, there has been a dramatic increase in pediatric emergency department (PED) visits seeking mental and behavioral health care. We aimed to determine the relationship between hours of care devoted to patients with mental and behavioral health complaints and markers of PED throughput and timeliness.

METHODS

We performed a retrospective, single-center, cross-sectional study of PED encounters between 2010 and 2022. We reported effect of care for patients with mental and behavioral health complaints on operational metrics, including 4 throughput metrics and 3 care metrics (eg, vital signs within 30 minutes of arrival or left without being seen rates). We estimated a series of negative binomial regression models with the monthly count of the given metric as the dependent variable and monthly ED volume as the offset.

RESULTS

We included a total of 720,914 visits over the study period, of which 22,901 (3.2%) were mental and behavioral health complaints. The total number of mental and behavioral health visits increased over the study period, from 1,113 in 2010 to 2,554 in 2021, whereas the median monthly behavioral health care hours showed a 1,483% increase. All outcomes worsened as behavioral health care hours increased in both operational and care categories.

CONCLUSION

In our single-center study, the increase in mental and behavioral health visits and hours of care was associated with significantly worsened PED throughput and timeliness of care metrics. This relationship highlights the challenges that PEDs face in caring for mental and behavioral health patients while simultaneously providing high-quality care to patients with acute nonmental and behavioral health emergencies.

Does Calm Always Follow the Storm? A Comprehensive Temporal Analysis of Emergency Department Visits in Northern Italy Before and After the COVID-19 Pandemic

BACKGROUND/OBJECTIVES

Emergency department (ED) crowding has become a pressing global concern exacerbated by the COVID-19 pandemic. No studies have addressed this issue in Europe during the post-pandemic period so far. This study examined ED visit volumes, patient acuity, hospital admission rates, emergency vehicle arrivals, and crowding metrics before, during, and after the pandemic.

METHODS

We conducted a retrospective descriptive study including data on all ED visits in the Lombardy Region of Italy from January 2019 to December 2023. Furthermore, an inferential statistical analysis was performed to compare ED trends between 2019 and 2023.

RESULTS

During the analyzed period, there were 15,515,128 visits across all Lombardy EDs. ED visits dropped from 3,514,426 in 2019 to 2,380,005 in 2020, then rebounded to 3,464,756 in 2023. In 2019, triage code distribution was 9.9% white, 68.7% green, 19.0% yellow, and 1.9% red. During the pandemic, the proportion of white and green codes decreased. By 2023, these comprised 80.7% of the total. The percentage of admitted patients was 11.9% in 2019, rose to 16.2% in 2020, and returned to 11.4% in 2023. The median ED length of stay (EDLOS) for admitted patients in 2023 was 5.2 h (IQR [2.1-17.4]), compared to 3.8 h (IQR [1.6-8.6]) in 2019 (p-value < 0.01). The median EDLOS for discharged patients in 2023 was 2.7 h (IQR [1.4-4.9]), compared to 2.4 h (IQR [1.3-4.4]) in 2019 (p-value < 0.01). The rate of patients leaving before completing treatment was 5.0% in 2019 and peaked at 6.8% in 2023 (p-value < 0.01).

CONCLUSIONS

In 2023, ED visits in Lombardy increased, compared to the pandemic period, but remained below 2019 levels. The proportion of high-acuity codes and hospital admissions was slightly lower than in 2019. However, ED crowding metrics worsened. The high levels of lower-acuity visits and the deterioration in crowding metrics highlight systemic challenges within the healthcare system.

Understanding Strategies to Reduce the Impact of Non-urgent Visits to the Pediatric Emergency Department: A Scoping Review

CONTEXT

The pediatric emergency department (PED) is increasingly being used for non-urgent reasons. This impacts PED input and throughput, and contributes to overcrowding. To identify solutions, it is essential to identify and describe the approaches that have been trialed.

OBJECTIVE

We completed a scoping review to identify and then describe the design and outcomes of all initiatives undertaken to reduce the impact of non-urgent visits on the PED.

DATA SOURCES

We searched 4 databases (MEDLINE, EMBASE, EBM, and CINAHL) to identify research published from the database inception until March 31, 2024.

STUDY SELECTION

Studies met our inclusion criteria if they focused on the pediatric ED, defined non-urgent visits, described an intervention (hypothesizing it would reduce the impact of non-urgent visits on the PED), and reported on the interventions impact.

DATA EXTRACTION

The title and abstract of each study were independently screened for inclusion by 2 reviewers (E.Q., K.N.), and disagreements were resolved by deliberation until consensus was achieved. This process was then repeated for the full text of all articles.

RESULTS

In total, we screened 11,600 articles and 20 were included. Nine interventions focused on PED input, 10 on PED throughput, and 1 on both PED input and throughput. Definitions of non-urgent visits and outcomes measures used to assess the effectiveness of an intervention differed between studies. Three types of strategies employed to reduce the impact of non-urgent visits on the PED were identified, these include (1) engaging nonpediatric emergency medicine clinicians by including them into the PED or connecting non-urgent patients to community locations for care, (2) reorganizing PED operations in anticipation of non-urgent visits, and (3) providing education to prevent future non-urgent visits.

CONCLUSIONS

Consistent definitions of non-urgent visits and standardized outcome measures may allow for more precise comparisons between studies. We identify 3 commonly employed strategies that may help reduce the impact of non-urgent visits on the PED.

The National Emergency Department Overcrowding Scale and Perceived Staff Workload: Evidence for Construct Validity in a Pediatric Setting

OBJECTIVE

The aim of the study is to determine if there is a correlation between perceived staff workload, measured by the NASA Task Load Index (TLX), and the National Emergency Department Overcrowding Scale (NEDOCS) in a pediatric ED.

METHODS

We collected staff questionnaires in a large, urban pediatric ED to assess perceived workload on each of six different TLX subscales, which we weighted evenly to create an overall estimate of workload. We evaluated the correlation between individual TLX responses and NEDOCS overall and by staff subgroup. Additionally, we analyzed: (1) the correlation between mean TLX responses and NEDOCS within a given hour and (2) the performance of a logistic regression model, using TLX as a predictor for "severely overcrowded," as measured by NEDOCS.

RESULTS

Four hundred one questionnaires between 6/2018 and 1/2019 demonstrated significant variation between concurrently collected TLX responses and an overall poor correlation between perceived workload and NEDOCS ( R2 0.096 [95% confidence interval, 0.048-0.16]). TLX responses by subgroups of fellows (n = 4, R2 0.96) and patient financial advisors (n = 15, R2 0.58) demonstrated the highest correlation with NEDOCS. Taking mean TLX responses within a given hour, during periods with NEDOCS >60 (extremely busy or overcrowded), a polynomial trend line matched the data best ( R2 0.638). On logistic regression, the TLX predicts "severely overcrowded" with an area under the curve of the receiver operating characteristic of 0.731.

CONCLUSIONS

NEDOCS does not have a strong correlation with individual responses on questionnaires of perceived workload for staff in a pediatric ED. NEDOCS, as a measure of overcrowding, may be better correlated with perceived workload during periods with elevated crowding or when interpreted categorically as yes/no "severely overcrowded".

Impact of emergency department overcrowding on the occurrence of in-hospital cardiac arrest

We aimed to determine whether emergency department (ED) overcrowding affects the occurrence of in-hospital cardiac arrest (IHCA) requiring resuscitation in the ED. This retrospective study was conducted in the ED of a single hospital. We applied the propensity score-matching method to adjust for differences in clinical characteristics in patients who visited the ED during overcrowded conditions. The indicators of overcrowding were: the total number of patients, number of patients undergoing treatment, and number of boarded patients awaiting hospital admission at the time of a patient's arrival. We defined the existence of ED overcrowding based on the 75%, 80%, and 90% thresholds of each indicator. We included 153,353 patients, and 160 cases of IHCA occurred, showing an incidence rate of 0.10%. The IHCA incidence rate increased during overcrowding, as indicated by the total number of patients and the number of boarded patients rising to 0.15% and 0.17%, respectively, at the 90% threshold (p = 0.0407 and 0.0203, respectively). The IHCA incidence rate did not increase during overcrowding based on the number of patients undergoing treatment. In the comparison conducted after propensity score matching, the incidence of IHCA was significantly higher in the overcrowding group than in the non-overcrowding group, indicated by 80% boarded patients (0.15% vs. 0.08%, p = 0.0222). The logistic regression results indicated that both the full-study and propensity score-matched cohorts showed a tendency for increased IHCA during overcrowding, as indicated by the total number of patients and number of boarded patients. The number of patients undergoing treatment did not affect the occurrence of IHCA. Although this needs to be confirmed in larger studies, we found in this study that ED overcrowding, particularly blocked access, tends to increase the incidence of IHCA requiring resuscitation in the ED. This suggests that to protect patient safety in ED overcrowding, it is essential for the entire hospital to make concerted efforts to maintain the flow of patients in the ED.

Clinical effect on major trauma patients during simultaneous or overlapping presentations at an urban level I trauma center in Korea

OBJECTIVE

Overcrowding in an emergency department (ED) or intensive care unit (ICU) of the trauma center (TC) is an important issue for timely acute health care of a critically injured patient. Accumulated scientific evidence has indicated the negative influence of overcrowding to the process and clinical outcome of trauma care.

METHOD

The institutional trauma registry at an urban level I TC was retrospectively evaluated for 5 years (2018-2022). Major trauma was defined as an injury severity score (ISS) of >15. We determined simultaneous or overlapping presentations of major trauma (SOMT) in two or more patients with ISS of >15 who presented within a 4-h time window. When only two patients were included within a single time window, they were classified as SOMT-2, whereas when three or more patient clusters were identified in a single time window, they were classified as SOMT-3. The outcome measurement included process and clinical variables, such as trauma team activation (TTA) ratio, ED length of stay (LOS), time to blood product transfusion (TF), time to emergency surgery or intervention (ESI), ICU LOS, and mortality.

RESULT

A total of 2,815 patients were included, of which 39.6% (N = 1,116) classified as SOMT. The SOMT group had lower TTA ratio than the non-SOMT group (69.4% vs. 73.4%, P = 0.022). The TTA ratio exhibited a decreasing trend in non-SOMT, SOMT-2, and SOMT-3 groups (P = 0.006). The time to TF was significantly delayed in the SOMT group (129 vs. 91 min, P < 0.001).

CONCLUSION

SOMT regularly occurs and results in fewer trauma team activation and a delayed time to blood transfusion. The current intensive trauma care system should be strategically modified to improve critical trauma care and enhance disaster preparedness.

Routine outcomes to investigate differences between ethnic minorities and White British people presenting to emergency services for injury: the stakeholder consultation

Introduction

Research has found differences in processes and outcomes of care between people in ethnic minorities and White British populations in some clinical conditions, although findings have been mixed. The Building an understanding of Ethnic minority people's Service Use Relating to Emergency care for injuries study is investigating differences in presentation, experience and health outcomes between people from ethnic minorities and White British people who seek emergency health care for injury.

Objective

Our aim was to consult with stakeholders to define measurable outcomes available in routine ambulance and emergency department data; to assess the appropriateness of existing outcome measures for ethnic minorities and White British people; and to identify any gaps.

Method

Clinicians, public contributors, researchers, people from the third sector, public health, healthcare inclusion were invited to join an online workshop to discuss routine outcomes.

Results

Twenty participants attended the stakeholder consultation, with only one being a public contributor, a limitation. Eleven were from a minority ethnic background and seven were female. The integrated list of outcomes included 25 items, combining routine outcomes from the Building an understanding of Ethnic minority people's Service Use Relating to Emergency care for injuries protocol and literature (n = 17) with additional outcomes (n = 8). Notably, the initial list lacked provisions for safeguarding referrals and cases of treatment refusal, which were new additions. Safety concerns arose due to the lack of safeguarding referrals, treatment refusal and self-discharge. Factors such as pre-existing health conditions, injury location and experiences of discrimination were identified as possible influences on care quality and waiting times for ethnic minority patients.

Conclusion

Although the number of stakeholders taking part in our consultation was low, their participation identified outcomes not found in routine data, supporting the adoption of a mixed-methods approach to answer our research questions. A future consultation could look to include more public members and wider range of clinicians including those who work in safeguarding and rehabilitation services.

Funding

This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR132744.

Smartphone App PACOM to Provide Advice Regarding Self-Triage for the Acute Primary Care Needs of Children: Accuracy of Algorithms

BACKGROUND

We developed a phone app, PACOM (Parents Application Conseils et Orientation Médicale), to provide medical advice to caregivers based on several algorithms and a series of binary questions related to children's symptoms. We compared the recommendations of the PACOM algorithms and clinicians for children visiting the emergency department (ED).

METHODS

Between January and February 2022, we prospectively recruited French-speaking parents of children without any chronic disease who presented to the pediatric ED with any complaint except for mental health problems or trauma. Isolated head trauma was included. They completed questionnaires and the various PACOM algorithms. The first algorithm, called "Quick Look," was developed to identify children with life-threatening emergencies. The standard reference was the advice of the ED clinicians who were blinded to the parental responses to the PACOM algorithm questions. The recommendations included "call urgent medical support," "visit the ED," "visit your general practitioner within 24 hours," and "visit your general practitioner in the next days."

RESULTS

The study included 269 parents. The response rate was 75%. The median age of the children was 3 years with interquartile range: 1 to 7 years. In total, 268 children completed the "Quick Look," 141 "fever," 83 "abdominal pain," 72 "cough," 70 "vomiting," 130 questionnaires relative to other proposed symptoms, and 70 "other symptom" questionnaires. The PACOM recommendations were "call urgent medical assistant" for 98 children, "ED visit" for 131, "visit general practitioner within 24 hours" for 13, and "visit general practitioner during the next days" for 24. The sensitivity and specificity of the PACOM recommendation to "call urgent medical support or visit the ED" were 98.1% (95% confidence interval, 95.5-100.00) and 22.1% (95% confidence interval, 15.3-28.8), respectively.

CONCLUSIONS

The PACOM algorithms has high sensitivity but low specificity for reducing ED visits and calls for urgent medical support.

Leaving Without Being Seen From the Pediatric Emergency Department: A New Baseline

BACKGROUND

Left without being seen (LWBS) rates are an important quality metric for pediatric emergency departments (EDs), with high-acuity LWBS children representing a patient safety risk. Since July 2021, our ED experienced a surge in LWBS after the most stringent COVID-19 quarantine restrictions ended.

OBJECTIVE

We assessed changes in LWBS rates and examined associations of system factors and patient characteristics with LWBS.

METHODS

We performed a retrospective study in a large, urban pediatric ED for all arriving patients, comparing the following three time-periods: before COVID-19 (PRE, January 2018-February 2020), during early COVID-19 (COVID, March 2020-June 2021), and after the emergence of COVID-19 variants and re-emergence of seasonal viruses (POST, July 2021-December 2021). We compared descriptive statistics of daily LWBS rates, patient demographic characteristics, and system characteristics. Negative binomial (system factors) and logistic regression (patient characteristics) models were developed to evaluate the associations between system factors and LWBS, and patient characteristics and LWBS, respectively.

RESULTS

Mean daily LWBS rates changed from 1.8% PRE to 1.4% COVID to 10.7% during POST. Rates increased across every patient demographic and triage level during POST, despite a decrease in daily ED volume compared with PRE. LWBS rates were significantly associated with patients with an Emergency Severity Index score of 2, mean ED census, and staff productivity within multiple periods. Patient characteristics associated with LWBS included lower assigned triage levels and arrival between 8 pm and 4 am.

CONCLUSIONS

LWBS rates have shown a large and sustained increase since July 2021, even for high-acuity patients. We identified system factors that may provide opportunities to reduce LWBS. Further work should develop strategies to prevent LWBS in at-risk patients.

Machine learning to identify attributes that predict patients who leave without being seen in a pediatric emergency department

PURPOSE

To characterize patients who left without being seen (LWBS) from a Canadian pediatric Emergency Department (ED) and create predictive models using machine learning to identify key attributes associated with LWBS.

METHODS

We analyzed administrative ED data from April 1, 2017, to March 31, 2020, from IWK Health ED in Halifax, NS. Variables included: visit disposition; Canadian Triage Acuity Scale (CTAS); triage month, week, day, hour, minute, and day of the week; sex; age; postal code; access to primary care provider; visit payor; referral source; arrival by ambulance; main problem (ICD10); length of stay in minutes; driving distance in minutes; and ED patient load. The data were randomly divided into training (80%) and test datasets (20%). Five supervised machine learning binary classification algorithms were implemented to train models to predict LWBS patients. We balanced the dataset using Synthetic Minority Oversampling Technique (SMOTE) and used grid search for hyperparameter tuning of our models. Model evaluation was made using sensitivity and recall on the test dataset.

RESULTS

The dataset included 101,266 ED visits where 2009 (2%) records were excluded and 5800 LWBS (5.7%). The highest-performing machine learning model with 16 patient attributes was XGBoost which was able to identify LWBS patients with 95% recall and 87% sensitivity. The most influential attributes in this model were ED patient load, triage hour, driving minutes from home address to ED, length of stay (minutes since triage), and age.

CONCLUSION

Our analysis showed that machine learning models can be used on administrative data to predict patients who LWBS in a Canadian pediatric ED. From 16 variables, we identified the five most influential model attributes. System-level interventions to improve patient flow have shown promise for reducing LWBS in some centres. Predicting patients likely to LWBS raises the possibility of individual patient-level interventions to mitigate LWBS.

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.

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.