Overcrowding in emergency departments: A review of strategies to decrease future challenges

Managing low-acuity patients in an Emergency Department through simulation-based multiobjective optimization using a neural network metamodel

This paper deals with Emergency Department (ED) fast-tracks for low-acuity patients, a strategy often adopted to reduce ED overcrowding. We focus on optimizing resource allocation in minor injuries units, which are the ED units that can treat low-acuity patients, with the aim of minimizing patient waiting times and ED operating costs. We formulate this problem as a general multiobjective simulation-based optimization problem where some of the objectives are expensive black-box functions that can only be evaluated through a time-consuming simulation. To efficiently solve this problem, we propose a metamodeling approach that uses an artificial neural network to replace a black-box objective function with a suitable model. This approach allows us to obtain a set of Pareto optimal points for the multiobjective problem we consider, from which decision-makers can select the most appropriate solutions for different situations. We present the results of computational experiments conducted on a real case study involving the ED of a large hospital in Italy. The results show the reliability and effectiveness of our proposed approach, compared to the standard approach based on derivative-free optimization.

Perceived Causes and Effects of Overcrowding Among Nurses in the Emergency Departments of Tertiary Hospitals: A Multicenter Study

Purpose

Emergency department (ED) overcrowding is a significant concern in many hospitals in Saudi Arabia, resulting in long waiting times, delays in treating patients who need urgent care, and, consequently, decreased patient satisfaction. Additionally, ED overcrowding has been linked to increased nurse turnover rates. Therefore, this study aimed to assess nurses' perceived causes and effects of overcrowding in the EDs of five tertiary hospitals in Saudi Arabia.

Methods

This study used a descriptive cross-sectional design. We surveyed 311 nurses working in the EDs of five tertiary hospitals in Saudi Arabia using the convenience sampling technique. The self-administered questionnaires used in the study were developed by the researchers. The study was conducted from October 16 to November 10, 2022. Consensus-Based Checklist for Reporting of Survey Studies was followed.

Results

The results revealed that the primary perceived causes of ED overcrowding in five tertiary hospitals were unnecessary visits due to a lack of standard procedures (mean = 2.70; SD = 0.58) and lack of inpatients beds (mean = 2.69; SD = 0.65). The perceived effect of overcrowding was stress and burnout among nurses (mean = 2.85; SD = 0.47). The perceived causes and effects of overcrowding in the ED were found to be highly significant (p <0.001) based on Pearson correlation and Spearman's rank correlation.

Conclusion

Unnecessary visits due to a lack of standard procedures lead to overcrowding. In addition, a lack of inpatient beds in the ED affects the care provided to patients seeking immediate medical attention. This may prolong patient waiting time, causing their conditions to deteriorate and prolonging hospital stay. Overcrowding leads to increased stress and burnout among nurses. The results of this study can be used to develop a comprehensive action plan to address ED overcrowding and its effects on patients, staff, and ED flow.

Overcrowding in emergency departments: an overview of reviews describing global solutions and their outcomes

Emergency Department (ED) crowding is defined as a situation wherein the demands of emergency services overcome the ability of a department to provide high-quality care within an appropriate time frame. There is a need for solutions, as the harms of crowding impact patients, staff, and healthcare spending. An overview of ED crowding was previously published by our group, which outlines these global issues. The problem of overcrowding in emergency departments has emerged as a global public health concern, and several healthcare agencies have addressed the issue and proposed possible solutions at each level of emergency care. There is no current literature summarizing the extensive research on interventions and solutions, thus there is a need for data synthesis to inform policymakers in this field. The aim of this overview was to summarize the interventions at each level of emergency care: input, throughput, and output. The methodology was supported by the current PRIOR statement for an overview of reviews. The study summarized twenty-seven full-text systematic reviews, which encompassed three hundred and eight primary studies. The results of the summary displayed a requirement for increasing studies in input and output interventions, as these showed the best outcomes with regard to ED crowding metrics. Moreover, the results displayed heterogeneous results at each level of ED care; these reflected that generally solutions have not been matched to specific problems facing regional centres. Thus, individual factors need to be considered when implementing solutions in Emergency Departments.

An Evaluation on the Attitude Toward Using Patient Rights and Satisfaction Levels in Emergency Department Patients

INTRODUCTION

In emergency health care services, patient satisfaction is one of the fundamental indicators of quality emergency care, making it essential to identify factors that can impact this component of care. This study aimed to determine emergency service patients' attitudes toward using patient rights, their satisfaction levels with emergency service, and related factors.

METHODS

The cross-sectional study was conducted with 382 patients who presented to the emergency department between November 2022 and March 2023. Data were collected using the Patient Description Form, the Emergency Department Patient Satisfaction Scale, and the Scale of Patient Rights Using Attitude. The study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist for reporting.

RESULTS

The mean score of the Emergency Department Patient Satisfaction Scale was 53.88 ± 6.88 (minimum score, 30; maximum score, 68), and the mean score of the Scale of Patient Rights Using Attitude was 108.89 ± 11.90 (minimum score, 73; maximum score, 135). As a result of the regression analysis, it was found that the Scale of Patient Rights Using Attitude scores and frequency of ED visits significantly contributed to the Emergency Department Patient Satisfaction Scale scores. Younger patients who had higher educational status presented to the emergency department more frequently and had chronic diseases were associated with positive attitudes about using patient rights and had higher levels of ED patient satisfaction (P < .001).

DISCUSSION

The study has provided valuable information for assessing the attitudes of ED patients toward exercising their patient rights and their satisfaction levels. Respect for patient rights and their effective utilization by patients can enhance the quality of ED services and increase patient satisfaction.

Evaluation of a STAT MRI Protocol for Emergent Ophthalmology Patients

BACKGROUND

Evaluating patients with potentially sight-threatening conditions frequently involves urgent neuroimaging, and some providers recommend expediting emergency department (ED) evaluation. However, several factors may limit the practicality of ED evaluation. This pilot study assessed the feasibility and safety of a STAT magnetic resonance imaging (MRI) protocol, designed to facilitate outpatient MRI within 48 hours of referral, compared with ED evaluation for patients with optic disc edema.

METHODS

A retrospective chart review was performed. Demographics, clinical data, and baseline ophthalmic measures were compared between patients in STAT and ED groups using the t test or Fisher exact test. Multivariate analyses compared changes in visual acuity (VA), visual field mean deviation (VF MD), retinal nerve fiber layer thickness, and edema grade between presentation and follow-up using a mixed-effects model adjusting for age, sex, and baseline measures.

RESULTS

A total of 70 patients met the study criteria-24 (34.3%) in the STAT MRI cohort and 46 (65.7%) in the ED cohort. Demographic variables were similar between groups. Patients referred to the ED had worse VA ( P < 0.001), larger VF MD ( P < 0.001), and higher edema grade ( P = 0.002) at presentation. Four patients in the ED group and none in the STAT group were found to have space-occupying lesions. Multivariate analyses showed that follow-up measures were significantly associated with their baseline values (all P < 0.001) but not with referral protocol (all P > 0.099). The STAT MRI protocol was associated with lower average patient charges and hospital costs.

CONCLUSIONS

The STAT MRI protocol did not result in inferior visual outcomes or delay in life-threatening diagnoses. Urgent outpatient evaluation, rather than ED referral, seems safe for some patients with optic disc edema. These findings support continued utilization of the protocol and ongoing improvement efforts.

Application of exponential smoothing method and SARIMA model in predicting the number of admissions in a third-class hospital in Zhejiang Province

OBJECTIVE

To establish the exponential smoothing prediction model and SARIMA model to predict the number of inpatients in a third-class hospital in Zhejiang Province, and evaluate the prediction effect of the two models, and select the best number prediction model.

METHODS

The data of hospital admissions from January 2019 to September 2022 were selected to establish the exponential smoothing prediction model and the SARIMA model respectively. Then compare the fitting parameters of different models: R2_adjusted, R2, Root Mean Square Error (RMSE)、Mean Absolute Percentage Error (MAPE)、Mean Absolute Error(MAE) and standardized BIC to select the best model. Finally, the established model was used to predict the number of hospital admissions from October to December 2022, and the prediction effect of the average relative error judgment model was compared.

RESULTS

The best fitting exponential smoothing prediction model was Winters Addition model, whose R2_adjusted was 0.533, R2 was 0.817, MAPE was 6.133, MAE was 447.341. The best SARIMA model is SARIMA(2,2,2)(0,1,1)12 model, whose R2_adjusted is 0.449, R2 is 0.199, MAPE is 8.240, MAE is 718.965. The Winters addition model and SARIMA(2,2,2)(0,1,1)12 model were used to predict the number of hospital admissions in October-December 2022, respectively. The results showed that the average relative error was 0.038 and 0.015, respectively. The SARIMA(2,2,2)(0,1,1)12 model had a good prediction effect.

CONCLUSION

Both models can better fit the number of admissions, and SARIMA model has better prediction effect.

Factors influencing the occurrence of ambulatory care sensitive conditions in the emergency department - a single-center cross-sectional study

Background and importance

The differentiation between patients who require urgent care and those who could receive adequate care through ambulatory services remains a challenge in managing patient volumes in emergency departments (ED). Different approaches were pursued to characterize patients that could safely divert to ambulatory care. However, this characterization remains challenging as the urgency upon presentation is assessed based on immediately available characteristics of the patients rather than on subsequent diagnoses. This work employs a core set of Ambulatory Care Sensitive Conditions (core-ACSCs) in an ED to describe conditions that do not require inpatient care if treated adequately in the ambulatory care sector. It subsequently analyzes the corresponding triage levels and admission status to determine whether core-ACSCs relevantly contribute to patient volumes in an ED.

Settings and participants

Single center cross-sectional analysis of routine data of a tertiary ED in 2019.

Outcome measures and analysis

The proportion of core-ACSCs among all presentations was assessed. Triage levels were binarily classified as "urgent" and "non-urgent," and the distribution of core-ACSCs in both categories was studied. Additionally, the patients presenting with core-ACSCs requiring inpatient care were assessed based on adjusted residuals and logistic regression. The proportion being discharged home underwent further investigation.

Main results

This study analyzed 43,382 cases of which 10.79% (n = 4,683) fell under the definition of core-ACSC categories. 65.2% of all core-ACSCs were urgent and received inpatient care in 62.8% of the urgent cases. 34.8% of the core-ACSCs were categorized as non-urgent, 92.4% of wich were discharged home. Age, triage level and sex significantly affected the odds of requiring hospital admission after presenting with core-ACSCs. The two core-ACSCs that mainly contributed to non-urgent cases discharged home after the presentation were "back pain" and "soft tissue disorders."

Discussion

Core-ACSCs contribute relevantly to overall ED patient volume but cannot be considered the primary drivers of crowding. However, once patients presented to the ED with what was later confirmed as a core-ACSC, they required urgent care in 65.2%. This finding highlights the importance of effective ambulatory care to avoid emergency presentations. Additionally, the core-ACSC categories "back pain" and "soft tissue disorders" were often found to be non-urgent and discharged home. Although further research is required, these core-ACSCs could be considered potentially avoidable ED presentations.

Clinical trial registration

The study was registered in the German trials register (DRKS-ID: DRKS00029751) on 2022-07-22.

A Physiotherapy-Led Transition to Home Intervention for Older Adults Following Emergency Department Discharge: A Pilot Feasibility Randomised Controlled Trial (ED PLUS)

Background

Older adults frequently attend the emergency department (ED) and experience high rates of subsequent adverse outcomes including functional decline, ED re-presentation and unplanned hospital admission. The development of effective interventions to prevent such outcomes is a key priority for research and service provision. Our aim was to evaluate the feasibility of a physiotherapy-led integrated care intervention for older adults discharged from the ED (ED PLUS).

Patients and Methods

Older adults presenting to the ED of a university teaching hospital with undifferentiated medical complaints and discharged within 72 hours were computer randomised in a ratio of 1:1:1 to deliver usual care, Comprehensive Geriatric Assessment (CGA) in the ED, or ED PLUS. ED PLUS is an evidence-based and stakeholder-informed intervention to bridge the care transition between the ED and community by initiating a CGA in the ED and implementing a six-week, multi-component, self-management programme in the patient's home. Feasibility and acceptability were assessed quantitatively and qualitatively. All clinical and process outcomes were assessed by a research nurse blinded to group allocation. Data analyses were primarily descriptive.

Results

Twenty-nine participants were recruited indicating a 67% recruitment rate. At 6 months, there was 100% retention in the usual care group, 88% in the CGA group and 90% in the ED PLUS group. ED PLUS participants expressed positive feedback, and there was a trend towards improved function and quality of life and less ED revisits and unscheduled hospitalisations in the ED PLUS group.

Conclusion

ED PLUS bridges the transition of care between the index visit to the ED and the community and is feasible using systematic recruitment strategies. Despite recruitment challenges in the context of COVID-19, the intervention was successfully delivered and well received by participants. There was a lower incidence of functional decline and improved quality of life in the ED PLUS group.

Trial Registration

The trial was registered in Clinical Trials Protocols and Results System as of 21st July 2021, with registration number NCT04983602.

Pathology testing in non-trauma patients presenting to the emergency department with recurrent seizures

OBJECTIVE

Excessive pathology testing is associated with ED congestion, increased healthcare costs and adverse patient health outcomes. This study aimed to determine the frequency, yield and influence of pathology tests among patients presenting to the ED with atraumatic recurrent seizures.

METHODS

This was a retrospective cohort study conducted at a level 4 adult ED in Australia and included atraumatic patients presenting to ED with recurrent seizures over a 4-year period (2017-2020). The primary outcome was the frequency of pathology tests. Additionally, the proportion of abnormal pathology test results and the association between pathology tests and change in management were assessed.

RESULTS

Of the 398 eligible presentations, 346 (86.9%, 95% confidence interval [CI] 83.3-89.9%) underwent at least one pathology test. In total 18.3% (n = 517) of pathology tests had an abnormal result which led to 15 changes in ED management among 12 presentations. Patients who had an abnormal pathology test result were more likely to undergo a change in antiepileptic drug management (odds ratio 2.08, 95% CI 1.23-3.65; P = 0.008).

CONCLUSION

Most patients presenting to the ED with atraumatic recurrent seizures underwent pathology tests. Abnormalities were frequently detected but were uncommonly associated with change in management. Abnormal pathology test results were associated with changes in antiepileptic drug management although rarely led to acute changes in patient management. This study suggests that pathology tests may be excessively requested in this population.

Urgent Care Versus Emergency Department Utilization for Foot and Ankle Fractures

INTRODUCTION

Foot and ankle fractures are common injuries for which patients may need urgent evaluation and care. Many such injuries are managed in emergency departments (EDs), but urgent care facilities may sometimes be an appropriate setting. Understanding which foot and ankle fractures are managed at which facility might help define care algorithms, improve patient experience, and suggest directions for containing costs.

METHODS

This retrospective cohort study used the 2010 to 2020 M151 PearlDiver administrative database. Adult patients less than 65 years old presenting to EDs and urgent care facilities for foot and ankle fractures were identified using ICD-9 and ICD-10 diagnosis codes, excluding polytrauma, and Medicare patients. Patient/injury variables associated with urgent care utilization relative to ED utilization and utilization trends of urgent care relative to ED were assessed with univariable and multivariable analyses.

RESULTS

From 2010 to 2020, 1,120,422 patients with isolated foot and ankle fractures presented to EDs and urgent care facilities. Urgent care visits evolved from 2.2% in 2010 to 4.4% in 2020 (P , 0.0001). Independent predictors of urgent care relative to ED utilization were defined. In decreasing odds ratios (ORs), these were insurance (relative to Medicaid, commercial OR 8.03), geographic region (relative to Midwest, Northeast OR 3.55, South OR 1.74, West OR 1.06), anatomic location of fracture (relative to ankle, forefoot OR 3.45, midfoot 2.20, hindfoot 1.63), closed fracture (OR 2.20), female sex (OR 1.29), lower ECI (OR 1.11 per unit decrease), and younger age (OR 1.08 per decade decrease) (P , 0.0001 for all).

DISCUSSION

A small but increasing minority of patients with foot and ankle fractures are managed in urgent care facilities relative to EDs. While patients with certain injury types were associated with increased odds of urgent care relative to ED utilization, the greatest predictors were nonclinical, such as geographic regions and insurance type, suggesting areas for optimizing access to certain care pathways.

LEVEL OF EVIDENCE

III.

Medical emergency department triage data processing using a machine-learning solution

Over the years, artificial intelligence has demonstrated its ability to overcome many challenges in our day-to-day life. The evolution of it inquired more studies about Machine Learning possible solutions for different domains, including health care. The increasing demand for artificial intelligence solutions has brought accessibility to loads of data, including clinical data. The availability of medical records facilitates new opportunities to explore Machine Learning models and their abilities to process a significant amount of data and to identify patterns with the purpose of solving a medical problem. Understanding the applicability of artificial intelligence on this type of data has to be a compelling aim for emergency medicine clinicians. This paper focuses on the general clinical problem of the complex correlation between medical records and later diagnosis and, especially, on the process of emergency department triage which uses the Emergency Severity Index (ESI) as triage protocol. This study presents a comparison between three different Machine Learning models, such as Logistic Regression, Random Forest Tree and NN-Sequentail, with the purpose of classifying patients with an emergency code. We conducted four experiments because of imbalanced data. A web-based application was developed to improve the triage process after our theoretical and exploratory results. Overall, in all experiments, the NN-Sequential model had better results, having, in the first experiment, a ROC-AUC score for each ESI emergency code of: 0.59%, 0.76%, 0.71%, 0.78% 0.64%. After applying methods to balance the data, the model yielded a ROC-AUC score for each emergency code of 0.72%, 0.75%, 0.69%, 0.74%, 0.78%. In the last experiment consisting of a three-class classification problem, the NN-Sequential and Random Forest Tree models had similar metric outcomes, and the NN-Sequential algorithm had a ROC-AUC score for each emergency code of: 0.76%, 0.72%, 0.84%. Without any doubt, our research results presented in this paper endorse this tremendous curiosity in Machine Learning applications to enrich aspects of emergency medical care by applying specific methods for processing both medical data and medical records.

EMS Administration of Systemic Corticosteroids to Pediatric Asthma Patients: An Analysis by Severity and Transport Interval

INTRODUCTION

Pediatric asthma exacerbations are a common cause of emergency medical services (EMS) encounters. Bronchodilators and systemic corticosteroids are mainstays of asthma exacerbation therapy, yet data on the efficacy of EMS administration of systemic corticosteroids are mixed. This study's objective was to assess the association between EMS administration of systemic corticosteroids to pediatric asthma patients on hospital admission rates based on asthma exacerbation severity and EMS transport intervals.

METHODS

This is a sub-analysis of the Early Administration of Steroids in the Ambulance Setting: An Observational Design Trial (EASI AS ODT). EASI AS ODT is a non-randomized, stepped wedge, observational study examining outcomes one year before and one year after seven EMS agencies incorporated an oral systemic corticosteroid option into their protocols for the treatment of pediatric asthma exacerbations. We included EMS encounters for patients ages 2-18 years confirmed by manual chart review to have asthma exacerbations. We compared hospital admission rates across asthma exacerbation severities and EMS transport intervals using univariate analyses. We geocoded patients and created maps to visualize the general trends of patient characteristics.

RESULTS

A total of 841 pediatric asthma patients met inclusion criteria. While most patients were administered inhaled bronchodilators by EMS (82.3%), only 21% received systemic corticosteroids, and only 19% received both inhaled bronchodilators and systemic corticosteroids. Overall, there was no significant difference in hospitalization rates between patients who did and did not receive systemic corticosteroids from EMS (33% vs. 32%, p = 0.78). However, although not statistically significant, for patients who received systemic corticosteroids from EMS, there was an 11% decrease in hospitalizations for mild exacerbation patients and a 16% decrease in hospitalizations for patients with EMS transport intervals greater than 40 min.

CONCLUSION

In this study, systemic corticosteroids were not associated with a decrease in hospitalizations of pediatric patients with asthma overall. However, while limited by small sample size and lack of statistical significance, our results suggest there may be a benefit in certain subgroups, particularly patients with mild exacerbations and those with transport intervals longer than 40 min. Given the heterogeneity of EMS agencies, EMS agencies should consider local operational and pediatric patient characteristics when developing standard operating protocols for pediatric asthma.

The Effects of Adopting Mobile Health and Fitness Apps on Hospital Visits: Quasi-Experimental Study

BACKGROUND

Overcrowding in public hospitals, a common issue in many countries, leads to a range of negative outcomes, such as insufficient access to medical services and patient dissatisfaction. Prior literature regarding solutions to reducing hospital overcrowding primarily focuses on organizational-level operational efficiency. However, few studies have investigated the strategies from the individual patient perspective. Specifically, we considered using mobile health and fitness apps to promote users' health behaviors and produce health benefits, thereby reducing hospital visits.

OBJECTIVE

This study estimated the causal effect of health and fitness app adoption on hospital visits by exploiting the staggered timing of adoption. We also investigated how the effect varied with users' socioeconomic status and digital literacy. This study provides causal evidence for the effects of health apps, extends the digital health literature, and sheds light on mobile health policies.

METHODS

This study used a data set containing health and fitness app use and hospital-related geolocation data of 267,651 Chinese mobile phone users from January to December 2019. We used the difference-in-differences and difference-in-difference-in-differences designs to estimate the causal effect. We performed a sensitivity analysis to establish the robustness of the findings. We also conducted heterogeneity analyses based on the interactions of postadoption indicators with users' consumption levels, city tiers, and digital literacy.

RESULTS

The preferred model (difference-in-difference-in-differences) showed a significant decrease in hospital visits after the adoption of health and fitness apps. App adoption led to a 5.8% (P<.001), 13.1% (P<.001), and 18.4% reduction (P<.001) in hospital visits 1, 2, and 3 months after adoption, respectively. In addition, the moderation analysis shows that the effect is greater for users with high consumption levels, in high-tier cities, or with high digital literacy.

CONCLUSIONS

This study estimated the causal effect of health and fitness app adoption on hospital visits. The results and sensitivity analysis showed that app adoption can reduce users' hospital visits. The effect varies with users' consumption levels, city tiers, and digital literacy. These findings provide useful insights for multiple stakeholders in the Chinese health care context.

Impact of Trauma Resuscitation Emergency Care Nurse Deployment in Trauma Activations in a Rural Trauma Center

BACKGROUND

Although the role of a dedicated trauma nurse has been implemented in an urban setting, it has not been studied in the rural trauma setting. We instituted a trauma resuscitation emergency care (TREC) nurse role to respond to trauma activations at our rural trauma center.

OBJECTIVE

This study aims to determine the impact of TREC nurse deployment on the timeliness of resuscitation interventions in trauma activations.

METHODS

This pre- and postintervention study at a rural Level I trauma center compared the time to resuscitation interventions before (August 2018 to July 2019) and after (August 2019 to July 2020) deploying TREC nurses to trauma activations.

RESULTS

A total of 2,593 participants were studied, of which 1,153 (44%) were in the pre-TREC group and 1,440 (56%) in the post-TREC group. After TREC deployment, the median (interquartile range [IQR]) emergency department times within the first hour decreased from 45 (31.23-53) to 35 (16-51) min ( p = .013). The median (IQR) time to the operating room within the first hour decreased from 46 (37-52) to 29 (12-46) min ( p = .001), and within the first 2 hr, decreased from 59 (43.8-86) to 48 (23-72) min ( p = .014).

CONCLUSION

Our study found that TREC nurse deployment improved resuscitation intervention timeliness during the first 2 hr (early phase) of trauma activations.

Comparing In-Hospital Mortality Prediction by Senior Emergency Resident's Judgment and Prognostic Models in the Emergency Department

Background

A comparison of emergency residents' judgments and two derivatives of the Sequential Organ Failure Assessment (SOFA), namely, the mSOFA and the qSOFA, was conducted to determine the accuracy of predicting in-hospital mortality among critically ill patients in the emergency department (ED).

Methods

A prospective cohort research was performed on patients over 18 years of age presented to the ED. We used logistic regression to develop a model for predicting in-hospital mortality by using qSOFA, mSOFA, and residents' judgment scores. We compared the accuracy of prognostic models and residents' judgment in terms of the overall accuracy of the predicted probabilities (Brier score), discrimination (area under the ROC curve), and calibration (calibration graph). Analyses were carried out using R software version R-4.2.0.

Results

In the study, 2,205 patients with median age of 64 (IQR: 50-77) years were included. There were no significant differences between the qSOFA (AUC 0.70; 95% CI: 0.67-0.73) and physician's judgment (AUC 0.68; 0.65-0.71). Despite this, the discrimination of mSOFA (AUC 0.74; 0.71-0.77) was significantly higher than that of the qSOFA and residents' judgments. Additionally, the AUC-PR of mSOFA, qSOFA, and emergency resident's judgments was 0.45 (0.43-0.47), 0.38 (0.36-0.40), and 0.35 (0.33-0.37), respectively. The mSOFA appears stronger in terms of overall performance: 0.13 vs. 0.14 and 0.15. All three models showed good calibration.

Conclusion

The performance of emergency residents' judgment and the qSOFA was the same in predicting in-hospital mortality. However, the mSOFA predicted better-calibrated mortality risk. Large-scale studies should be conducted to determine the utility of these models.

Access block and overcrowding at the emergency department at Tupua Tamasese Meaole Hospital in Samoa

BACKGROUND

Access block and overcrowding are known to adversely impact on patient outcomes, service delivery, and patient experiences within emergency departments (ED) worldwide. There are no studies on access block or overcrowding from the Pacific Islands. The aim of the present study is to provide preliminary data on access block and overcrowding in the ED of the national tertiary hospital of Samoa.

METHODS

Mixed methods study design. Data collection was performed in March 2020. The quantitative strand calculated (1) the point prevalence of patients impacted by access block in the ED, and (2) the ED bed occupancy rate to assess for overcrowding. The qualitative strand used thematic analysis of two focus group interviews exploring access block and overcrowding with ED medical and nursing staff members.

RESULTS

On the day of data collection, a total of 60 patients presented through the ED triage system. Of the 20 patients who were admitted into ED, 80% were triaged as 'see without delay' (CAT1), 'emergency' (CAT2) or 'urgent' (CAT3). For patients requiring admission to hospital wards, 100% waited 4 + h in ED, and 100% waited 8 + h, suggesting the presence of access block. Overcrowding in the ED setting was also evident, with an ED bed occupancy rate of 0.95, and an adjusted bed occupancy rate of 1.43. The major themes emerging from the ED staff focus groups and individual in-depth interviews were (1) the adverse impacts of access block and overcrowding, i.e., violence towards ED staff members, (2) the preventable contributing factors, i.e., lack of physical beds in the ED, and (3) practical recommendations to improve patient flow through the ED, i.e., improved collaboration between ED, outpatient services, and the hospital wards.

CONCLUSIONS

Preliminary evidence suggested the presence of access block and overcrowding in the ED of the national tertiary hospital of Samoa. ED staff interviews provided insight into the ED frontline challenges and offered practical recommendations for ED health service improvement.

Main Features and Control Strategies to Reduce Overcrowding in Emergency Departments: A Systematic Review of the Literature

Purpose

Overcrowding is a problem that affects emergency departments (ED) all over the world; it occurs due to a disproportion between user demand and the physical, human and structural resources available. Essential prerequisites to assessing and managing the phenomenon are its accurate measurement and an understanding of its impact. The objective of this systematic review is to identify the characteristics of the problem, analyzing the proposed strategies aimed at improving patient flow, delay in services provided and overcrowding of emergency departments.

Methods

To achieve our objectives, a manual computerized search was performed in the bibliographic databases using as keywords "Emergency Department", "Overcrowding", "Emergency Room", "Emergency Service", "Emergency Unit"",Emergency Ward", "Emergency Outpatient Unit", "Emergency Hospital", "Crowding", "Mass Gathering", "Management" and "Comprehensive Health Care". Two independent reviewers analyzed abstracts, titles and full text articles for admissibility, according to the selected inclusion and exclusion criteria.

Results

The process lead to include 19 articles. It was possible to group the solutions proposed in five categories: work organization, investment in primary care, creation of new dedicated professional figures, work and structural modifications and implementation of predictive simulation models using mathematical algorithms.

Conclusion

The most effective measures to guarantee an improvement in the flow of patients are represented by both improving the efficiency of human resources and by developing predictive mathematical models, regardless of the type of hospital and its location. Considering the complexity of EDs and the multiple characteristics of overcrowding and that the causes of crowding are different and site-specific, a careful examination of the specifics of each ED is necessary to identify improving fields.

The impact of emergency department length of stay on the outcomes of trauma patients requiring hospitalization: a retrospective observational study

BACKGROUND

We aimed to explore the impact of the emergency department length of stay (EDLOS) on the outcome of trauma patients.

METHODS

A retrospective study was conducted on all trauma patients requiring hospitalization between 2015 and 2019. Patients were categorized into 4 groups based on the EDLOS (<4 h, 4-12 h,12-24 h, and >24 h). Data were analyzed using Chi-square test (categorical variables), Student's t-test (continuous variables), correlation coefficient, analysis of variance and multivariate logistic regression analysis for identifying predictors of short EDLOS and hospital mortality.

RESULTS

The study involved 7,026 patients with a mean age of 32.1±15.6 years. One-fifth of patients had a short EDLOS (<4 h) and had higher level trauma team T1 activation (TTA-1), higher Injury Severity Score (ISS), higher shock index (SI), and more head injuries than the other groups (P=0.001). Patients with an EDLOS >24 h were older (P=0.001) and had more comorbidities (P=0.001) and fewer deaths (P=0.001). Multivariate regression analysis showed that the predictors of short EDLOS were female gender, GCS, SI, hemoglobin level, ISS, and blood transfusion. The predictors of mortality were TTA-1 (odds ratio [OR]=4.081, 95%CI: 2.364-7.045), head injury (OR=3.920, 95%CI: 2.413-6.368), blood transfusion (OR=2.773, 95%CI: 1.668-4.609), SI (OR=2.132, 95%CI: 1.364-3.332), ISS (OR=1.077, 95%CI: 1.057-1.096), and age (OR=1.040, 95%CI: 1.026-1.054).

CONCLUSIONS

Patients with shorter EDLOS had different baseline characteristics and hospital outcomes compared with patients with longer EDLOS. Patients with prolonged EDLOS had better outcomes; however, the burden of prolonged boarding in the ED needs further elaboration.

Factors Relating to Decision Delay in the Emergency Department: Effects of Diagnostic Tests and Consultations

Purpose

The purpose of this study is to investigate the factors increasing waiting time (WT) and length of stay (LOS) in patients, which may cause delays in decision-making in the emergency departments (ED).

Patients and Methods

Patients who arrived at a training hospital in the central region of Izmir City, Turkey, during the first quarter of 2020 were retrospectively analyzed. WT and LOS were the outcome variables of the study, and gender, age, arrival type, triage level determined based on the clinical acuity, diagnosis encoded based on International Classification of Diseases-10 (ICD-10), the existence of diagnostic tests or consultation status were the identified factors. The significance of the differences in WT and LOS values based on each level of these factors was analyzed using independent sample t-tests and ANOVA.

Results

While patients for which no diagnostic testing or consultation was requested had a significantly higher WT in EDs, their LOS values were substantially lower than those for which at least one diagnostic test or consultation was ordered (p≤0.001). Besides, elderly and red zone patients and those who arrived by ambulance had significantly lower WT and higher LOS values than other levels for all groups of patients for which laboratory-type or imaging-type diagnostic test or consultation was requested (p≤0.001 for each comparison).

Conclusion

Besides ordering diagnostic tests or consultation in EDs, different factors may extend patients' WT and LOS values and cause significant decision-making delays. Understanding the patient characteristics associated with longer waiting times and LOS values and, thus, delayed decisions will enable practitioners to improve operations management in EDs.

Developing a machine learning model to predict patient need for computed tomography imaging in the emergency department

Overcrowding is a well-known problem in hospitals and emergency departments (ED) that can negatively impact patients and staff. This study aims to present a machine learning model to detect a patient's need for a Computed Tomography (CT) exam in the emergency department at the earliest possible time. The data for this work was collected from ED at Thunder Bay Regional Health Sciences Centre over one year (05/2016-05/2017) and contained administrative triage information. The target outcome was whether or not a patient required a CT exam. Multiple combinations of text embedding methods, machine learning algorithms, and data resampling methods were experimented with to find the optimal model for this task. The final model was trained with 81, 118 visits and tested on a hold-out test set with a size of 9, 013 visits. The best model achieved a ROC AUC score of 0.86 and had a sensitivity of 87.3% and specificity of 70.9%. The most important factors that led to a CT scan order were found to be chief complaint, treatment area, and triage acuity. The proposed model was able to successfully identify patients needing a CT using administrative triage data that is available at the initial stage of a patient's arrival. By determining that a CT scan is needed early in the patient's visit, the ED can allocate resources to ensure these investigations are completed quickly and patient flow is maintained to reduce overcrowding.

Integrated Care Model Salut+Social Assessment by Professionals, Informal Caregivers and Chronic or Social Dependent Patients: A Qualitative Study

We explored the views of the professionals (from primary care and social services) and users (caregivers and patients) who participated in the clinical trial of the Salut+Social integrated care model to identify the implementation barriers and facilitators, to assess the impact on health and wellbeing and to obtain an assessment of the program. A qualitative descriptive study with a pragmatic, utilitarian approach was performed. Participants were recruited by purposive and convenience sampling. A focus group (FG) and in-depth interviews were conducted with professionals and users, respectively. Thematic content analysis was employed. A total of 11 professionals and 8 users participated in the FG and interviews, respectively. Seven themes were identified: (1) contextualizing the previous scenario; (2) achievements of the program from the professionals' perspective; (3) facilitators and barriers of the integrated care model; (4) proposals for improving the integrated care model; (5) users' assessment of the care received within the program framework; (6) users' perception of the impact on health and wellbeing; (7) users' demands for better care. Professionals reported improved coordination between services and highlighted the need for a protocol for emergencies and to strengthen community orientation. Users proposed more frequent home visits. This study shows the acceptability of the new model by professionals and the users' satisfaction with the care received.

Overcrowding in Emergency Department: Causes, Consequences, and Solutions—A Narrative Review

Overcrowding in Emergency Departments (EDs) is a phenomenon that is now widespread globally and causes a significant negative impact that goes on to affect the entire hospital. This contributes to a number of consequences that can affect both the number of resources available and the quality of care. Overcrowding is due to a number of factors that in most cases lead to an increase in the number of people within the ED, an increase in mortality and morbidity, and a decrease in the ability to provide critical services in a timely manner to patients suffering from medical emergencies. This phenomenon results in the Emergency Department reaching, and in some cases exceeding, its optimal capacity. In this review, the main causes and consequences involving this phenomenon were collected, including the effect caused by the SARS-CoV-2 virus in recent years. Finally, special attention was paid to the main operational strategies that have been developed over the years, strategies that can be applied both at the ED level (microlevel strategies) and at the hospital level (macrolevel strategies).

Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study

BACKGROUND

During the coronavirus disease 2019 (COVID-19) pandemic, calculation of the number of emergency department (ED) beds required for patients with vs. without suspected COVID-19 represented a real public health problem. In France, Amiens Picardy University Hospital (APUH) developed an Artificial Intelligence (AI) project called "Prediction of the Patient Pathway in the Emergency Department" (3P-U) to predict patient outcomes.

MATERIALS

Using the 3P-U model, we performed a prospective, single-center study of patients attending APUH's ED in 2020 and 2021. The objective was to determine the minimum and maximum numbers of beds required in real-time, according to the 3P-U model. Results A total of 105,457 patients were included. The area under the receiver operating characteristic curve (AUROC) for the 3P-U was 0.82 for all of the patients and 0.90 for the unambiguous cases. Specifically, 38,353 (36.4%) patients were flagged as "likely to be discharged", 18,815 (17.8%) were flagged as "likely to be admitted", and 48,297 (45.8%) patients could not be flagged. Based on the predicted minimum number of beds (for unambiguous cases only) and the maximum number of beds (all patients), the hospital management coordinated the conversion of wards into dedicated COVID-19 units.

DISCUSSION AND CONCLUSIONS

The 3P-U model's AUROC is in the middle of range reported in the literature for similar classifiers. By considering the range of required bed numbers, the waste of resources (e.g., time and beds) could be reduced. The study concludes that the application of AI could help considerably improve the management of hospital resources during global pandemics, such as COVID-19.

Interdependencies or integration? A qualitative evaluation of a national emergency department improvement programme

Purpose

In Wales (United Kingdom (UK)), a programme known as the emergency department quality and delivery framework (EDQDF) was launched in 2018 with the purpose of designing a framework of what good looks like for emergency care and then implementing this framework in a measurable and sustainable way.

Design/methodology/approach

A gatekeeper emailed attendees of the EDQDF launch event (n = 70), providing recipients with an information sheet and inviting them to contact the researcher (KJ) if they agreed to be interviewed. The authors conducted semi-structured interviews with all respondents (n = 8) after three invitation rounds sent between August and October 2021. The authors used a thematic analysis approach (Braun and Clarke, 2006).

Findings

Participants agreed with the aims and design of the framework, and the authors identified four themes relating to barriers and to facilitators of implementation. Participants perceive a softening of geographical boundaries through the project, but findings correspond with evidence generated elsewhere regarding emergency departments’ (EDs') system-wide interdependencies and a need for cross-organisational collaboration.

Research limitations/implications

A quality improvement method for health services known as CAREMORE® is found to be a useful approach for the collaborative design of service improvements. Participants perceive a softening of geographical boundaries through the project, but the interviews correspond with evidence generated elsewhere regarding EDs' system-wide interdependencies and a need for cross-organisational collaboration.

Practical implications

This evaluation relies on a relatively small number of participants, but as a qualitative evaluation it does not aim towards broadly generalisable findings but rather contributes to broad field concerned with the production of knowledge on the implementation of health service improvements. The project under evaluation is also on-going, and the findings reflect the period from inception to December 2021, but not beyond that date.

Originality/value

This evaluation builds upon previous work in relation to the application of CAREMORE to design a quality improvement framework in a complex area (see Nelson et al., 2018), but this evaluation considers the implementation process. The findings echo research elsewhere and add to a growing body of research that underlines system interconnectivities that impact upon the emergency department.

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.

A Systematic Analysis of the Impact of an Ambulatory Ophthalmology Urgent Care Clinic

Importance A same-day ophthalmic urgent care clinic can provide efficient eye care, a rich educational environment, and can improve patient experience.

Objective The aim of this study was to systematically evaluate volume, financial impact, care metrics, and the breadth of pathology of urgent new patient encounters based on their site of initial presentation.

Design, Setting, and Participants A retrospective analysis was performed on consecutive urgent new patient evaluations in our same-day triage clinic at the Henkind Eye Institute at Montefiore Medical Center between February 2019 and January 2020. The cohort of patients who presented directly to this urgent care clinic were referred to as the “TRIAGE” group. Patients who initially presented to an emergency department (ED), and were subsequently referred to our triage clinic, are referred to as the “ED + TRIAGE” group.

Main Outcomes and Measures Visits were evaluated on a variety of metrics, including diagnosis, duration, charge, cost, and revenue. Furthermore, return to the ED or inpatient admission was documented.

Results Of 3,482 visits analyzed, 2,538 (72.9%) were in the “TRIAGE” group. Common presenting diagnoses were ocular surface disease (n = 486, 19.1%), trauma (n = 342, 13.5%; most commonly surface abrasion n = 195, 7.7%), and infectious conjunctivitis (n = 304, 12.0%). Patients in the “TRIAGE” group, on average, were seen 184.6% faster (158.2 vs. 450.2 minutes) than patients in the “ED + TRIAGE” group (p < 0.001). The “ED + TRIAGE” group were furthermore found to generate 442.1% higher charges ($870.20 vs. 4717.70) and were associated with 175.1% higher cost ($908.80 vs. 330.40) per patient. The hospital was found to save money when noncommercially insured patients with ophthalmic complaints presented to the triage clinic instead of the ED. Patients seen in the triage clinic had a low rate of readmission to the ED (n = 42, 1.2%).

Conclusions and Relevance A same-day ophthalmology triage clinic provides efficient care, while providing a rich learning environment for residents. Less wait time with direct access to subspecialist care can help improve quality, outcome, and satisfaction metrics.

Biologically Active Adrenomedullin (bio-ADM) is of Potential Value in Identifying Congestion and Selecting Patients for Neurohormonal Blockade in Acute Dyspnea

PURPOSE

This study was designed to evaluate the role of biologically active adrenomedullin (bio-ADM) in congestion assessment and risk stratification in acute dyspnea.

METHODS

This is a sub-analysis of the Lithuanian Echocardiography Study of Dyspnea in Acute Settings. Congestion was assessed by means of clinical (peripheral edema, rales) and sonographic (estimated right atrial pressure) parameters. Ninety-day mortality was chosen for outcome analysis.

RESULTS

There were 1188 patients included. Bio-ADM concentration was higher in patients with peripheral edema at admission (48.2 [28.2-92.6] vs 35.4 [20.9-59.2] ng/L, P < .001). There was a stepwise increase in bio-ADM concentration with increasing prevalence of rales: 29.8 [18.8-51.1], 38.5 [27.5-67.1], and 51.1 [33.1-103.2] ng/L in patients with no rales, rales covering less than one-half, and greater than or equal to one-half of the pulmonary area, respectively (P < 0.001). Bio-ADM concentration demonstrated gradual elevation in patients with normal, moderately, and severely increased estimated right atrial pressure: 25.1 [17.6-42.4] ng/L, 36.1 [23.1-50.2], and 47.1 [30.7-86.7] ng/L, respectively (P < .05). Patients with bio-ADM concentration >35.5 ng/L were at more than twofold increased risk of dying (P < .001). Survival in those with high bio-ADM was significantly modified by neurohormonal blockade at admission (P < .05), especially if NT-proBNP levels were lower than the median (P = .002 for interaction).

CONCLUSION

Bio-ADM reflects the presence and the degree of pulmonary, peripheral, and intravascular volume overload and is strongly related to 90-day mortality in acute dyspnea. Patients with high bio-ADM levels demonstrated survival benefit from neurohormonal blockade.

From the Triage to the Intermediate Area: A Simple and Fast Model for COVID-19 in the Emergency Department

Introduction: The early identification of patients with SARS-CoV-2 infection is still a real challenge for emergency departments (ED). First, we aimed to develop a score, based on the use of the lung ultrasonography (LUS), in addition to the pre-triage interview, to correctly address patients; second, we aimed to prove the usefulness of a three-path organization (COVID-19, not-COVID-19 and intermediate) compared to a two-path organization (COVID-19, non-COVID-19). Methods: We retrospectively analysed 292 patients admitted to our ED from 10 April to 15 April 2020, with a definite diagnosis of positivity (93 COVID-19 patients) or negativity (179 not-COVID-19 patients) for SARS-COV-2 infection. Using a logistic regression, we found a set of predictors for infection selected from the pre-triage interview items and the LUS findings, which contribute with a different weight to the final score. Then, we compared the organization of two different pathways. Results: The most informative factors for classifying the patient are known nasopharyngeal swab positivity, close contact with a COVID-19 patient, fever associated with respiratory symptoms, respiratory failure, anosmia or dysgeusia, and the ultrasound criteria of diffuse alveolar interstitial syndrome, absence of B-lines and presence of pleural effusion. Their sensitivity, specificity, accuracy, and AUC-ROC are, respectively, 0.83, 0.81, 0.82 and 0.81. The most significant difference between the two pathways is the percentage of not-COVID-19 patients assigned to the COVID-19 area, that is, 10.6% (19/179) in the three-path organization, and 18.9% (34/179) in the two-path organization (p = 0.037). Conclusions: Our study suggests the possibility to use a score based on the pre-triage interview and the LUS findings to correctly manage the patients admitted to the ED, and the importance of an intermediate area to limit the spread of SARS-CoV-2 in the ED and, as a consequence, in the hospital.

Implementation of the Acute Care Clinic Easy Scheduling System at the Brooke Army Medical Center

Introduction

Emergency departments (EDs) have continued to struggle with overcrowding, causing delays in patient care and increasing stress on the medical staff and resources. This was further illustrated during the recent coronavirus disease 2019 pandemic, where we saw large unpredictable surges to the ED as hospitals tried to meet the medical needs of patients while trying to minimize the spread of coronavirus disease. A previous study from the Department of Emergency at the Brooke Army Medical Center (BAMC) found that nearly half of the patients presenting to the ED could have been managed in a primary care setting. We sought to pilot an alternate appointment scheduling system, Acute Care Clinic Easy Scheduling System, to allow patients to see and book available appointments while waiting in the ED waiting room.

Materials and Methods

Our appointment display system was created through collaboration with the BAMC Information Management Division. A Tableau data interface connects to the Composite Health Care System to view available primary appointments across the San Antonio Military Health Care System. These are displayed in real-time on multiple TV screens outside the ED and in the ED waiting room. Patients were provided signage that provides a way to call or use a World Wide Web–based interface to immediately schedule the open appointments within the next 48 hours. Patients voluntarily opted to use this system and may opt to leave the ED if another appointment became available within an acceptable time frame to them.

Results

This section is not applicable to this article.

Conclusions

Expansion of the Acute Care Clinic Easy Scheduling System within the Military Health Care System may (1) help reduce ED crowding, (2) improve access to care through a live-tracking system that patients can review and select from, and (3) reduce the number of unfilled primary care appointments. The system in place in the BAMC ED serves as a template for other MTFs to use.

ES-RED (Early Seizure Recurrence in the Emergency Department) Calculator: A Triage Tool for Seizure Patients

Seizure is a common neurological presentation in patients visiting the emergency department (ED) that requires time for evaluation and observation. Timely decision and disposition standards for seizure patients need to be established to prevent overcrowding in the ED and achieve patients’ safety. Here, we conducted a retrospective cohort study to predict early seizure recurrence in the ED (ES-RED). We randomly assigned 688 patients to the derivation and validation cohorts (2:1 ratio). Prediction equations extracted routine clinical and laboratory information from EDs using logistic regression (Model 1) and machine learning (Model 2) methods. The prediction equations showed good predictive performance, the area under the receiver operating characteristics curve showing 0.808 in Model 1 [95% confidential interval (CI): 0.761–0.853] and 0.805 in Model 2 [95% CI: 0.747–0.857] in the derivation cohort. In the external validation, the models showed strong prediction performance of 0.739 [95% CI: 0.640–0.824] in Model 1 and 0.738 [95% CI: 0.645–0.819] in Model 2. Intriguingly, the lowest quartile group showed no ES-RED after 6 h. The ES-RED calculator, our proposed prediction equation, would provide strong evidence for safe and appropriate disposition of adult resolved seizure patients from EDs, reducing overcrowding and delays and improving patient safety.

A qualitative content analysis for determining indexes and factors affecting for evaluation of disaster exercises immediate feedback stage

BACKGROUND

Taking exercise in health sector is one of the important steps to implement the disaster risk management programs, especially preparedness phase. The present study aimed to identify indexes and factors affecting successful evaluation of disasters preparedness exercises in hot wash stage.

MATERIALS AND METHODS

This study was a qualitative content analysis. Data were collected by purposeful sampling through in-depth and semi-structured individual interviews with 25 health professionals in the field of disasters. The data were analyzed using directed content analysis method by which the initial codes were extracted after transcribing the recorded interviews and immersing them in the data analysis. The initial codes were reviewed, classified, and subdivided into several stages to determine the main classes.

RESULTS

The data analysis resulted in the production of 24 initial codes, 5 subcategories, 2 main categories of "evaluation and exercise debriefing" and "modification of programs and promotion of exercise operational functions" under the original theme of "exercise immediate feedback."

CONCLUSION

This study can be considered a suitable standard guide for health care organizations to evaluate successfully disasters exercises in hot wash stage, maintain and promote their preparedness, and properly respond to disasters.

Machine learning-based triage to identify low-severity patients with a short discharge length of stay in emergency department

BACKGROUND

Overcrowding in emergency departments (ED) is a critical problem worldwide, and streaming can alleviate crowding to improve patient flows. Among triage scales, patients labeled as "triage level 3" or "urgent" generally comprise the majority, but there is no uniform criterion for classifying low-severity patients in this diverse population. Our aim is to establish a machine learning model for prediction of low-severity patients with short discharge length of stay (DLOS) in ED.

METHODS

This was a retrospective study in the ED of China Medical University Hospital (CMUH) and Asia University Hospital (AUH) in Taiwan. Adult patients (aged over 20 years) with Taiwan Triage Acuity Scale level 3 were enrolled between 2018 and 2019. We used available information during triage to establish a machine learning model that can predict low-severity patients with short DLOS. To achieve this goal, we trained five models-CatBoost, XGBoost, decision tree, random forest, and logistic regression-by using large ED visit data and examined their performance in internal and external validation.

RESULTS

For internal validation in CMUH, 33,986 patients (75.9%) had a short DLOS (shorter than 4 h), and for external validation in AUH, there were 13,269 (82.7%) patients with short DLOS. The best prediction model was CatBoost in internal validation, and area under the receiver operating cha racteristic curve (AUC) was 0.755 (95% confidence interval (CI): 0.743-0.767). Under the same threshold, XGBoost yielded the best performance, with an AUC value of 0.761 (95% CI: 0.742- 0.765) in external validation.

CONCLUSIONS

This is the first study to establish a machine learning model by applying triage information alone for prediction of short DLOS in ED with both internal and external validation. In future work, the models could be developed as an assisting tool in real-time triage to identify low-severity patients as fast track candidates.

Modelling Granular Process Flow Information to Reduce Bottlenecks in the Emergency Department

Increasing demand and changing case-mix have resulted in bottlenecks and longer waiting times in emergency departments (ED). However, many process improvement efforts addressing the bottlenecks have limitations, as they lack accurate models of the real system as input accounting for operational complexities. To understand the limitation, this research modelled granular procedural information, to analyse processes in a Level-1 ED of a 1200-bed teaching hospital in the UK. Semi-structured interviews with 21 clinicians and direct observations provided the necessary information. Results identified Majors as the most crowded area, hence, a systems modelling technique, role activity diagram, was used to derive highly granular process maps illustrating care in Majors which were further validated by 6 additional clinicians. Bottlenecks observed in Majors included awaiting specialist input, tests outside the ED, awaiting transportation, bed search, and inpatient handover. Process mapping revealed opportunities for using precedence information to reduce repeat tests; informed alerting; and provisioning for operational complexity into ED processes as steps to potentially alleviate bottlenecks. Another result is that this is the first study to map care processes in Majors, the area within the ED that treats complex patients whose care journeys are susceptible to variations. Findings have implications on the development of improvement approaches for managing bottlenecks.

Diagnostic testing in nontrauma patients presenting to the emergency department with recurrent seizures: A systematic review

BACKGROUND

There is a lack of consensus regarding the role of investigations among patients presenting to the emergency department (ED) with recurrent seizures. The aim of this systematic review was to determine the frequency and utility of commonly requested investigations for nontrauma patients presenting to the ED with recurrent seizures.

METHODS

The MEDLINE, EMBASE, and Cochrane Library databases were searched (March 2021) for articles on this topic using search terms related to recurrent seizures, investigations, and the ED. The inclusion criteria required that articles include adult nontrauma patients presenting to the ED. Studies exclusively investigating first-episode seizures, trauma patients, and status epilepticus were excluded. Eligible studies were assessed for bias using the Newcastle-Ottawa scale. Results of studies were presented using proportions.

RESULTS

There were six cohort studies included that contributed data from 36,595 patients. All six studies assessed at least one of our primary outcomes for computed tomography (CT) brain scans. The proportion of patients who underwent a head CT ranged from 13% to 42%. The rates of abnormal head CT findings ranged from 8% to 21%. One study reported on magnetic resonance imaging (MRI) and found it used infrequently in 0.79% of cases. The proportion and yield of nonneuroimaging investigations were not well evaluated in this patient population. Only one study reported on the utility of sodium levels or blood glucose results for this population and reported abnormalities in sodium levels for 19% of patients and abnormalities in glucose levels in 50% of patients.

CONCLUSIONS

In this population, CT brain scans appeared to be performed uncommonly but with moderate rates of abnormal findings. In the absence of prolonged alteration of consciousness, a history of brain tumor, or positive neurologic findings, however, neuroimaging was of low yield. Given the heterogeneity and potential limitations of these studies, further research on this topic is required.

Ambulance crew-initiated non-conveyance in the Helsinki EMS system-A retrospective cohort study

Background

Ambulance patients are usually transported to the hospital in the emergency medical service (EMS) system. The aim of this study was to describe the non‐conveyance practice in the Helsinki EMS system and to report mortality following non‐conveyance decisions.

Methods

All prehospital patients ≥16 years attended by the EMS but not transported to a hospital during 2013–2017 were included in the study. EMS mission‐ and patient‐related factors were collected and examined in relation to patient death within 30 days of the EMS non‐conveyance decision.

Results

The EMS performed 324,207 missions with a patient during the study period. The patient was not transported in 95,909 (29.6%) missions; 72,233 missions met the study criteria. The patient mean age (standard deviation) was 59.5 (22.5) years; 55.5% of patients were female. The most common dispatch codes were malaise (15.0%), suspected decline in vital signs (14.0%), and falling over (12.9%). A total of 960 (1.3%) patients died within 30 days after the non‐conveyance decision. Multivariate logistic regression analysis revealed that mortality was associated with the patient's inability to walk (odds ratio 3.19, 95% confidence interval 2.67–3.80), ambulance dispatch due to shortness of breath (2.73, 2.27–3.27), decreased level of consciousness (2.72, 1.75–4.10), decreased blood oxygen saturation (2.64, 2.27–3.06), and abnormal systolic blood pressure (2.48, 1.79–3.37).

Conclusion

One‐third of EMS missions did not result in patient transport to the hospital. Thirty‐day mortality was 1.3%. Abnormalities in multiple respiratory‐related vital signs were associated with an increased likelihood of death within 30 days.

An Analysis of Waiting Time for Emergency Treatment and Optimal Allocation of Nursing Manpower

Objective: Emergency care is the frontline of the healthcare system. Taiwanese typically seek emergency care when suffering from an acute or unknown illness, which leads to a large number of emergency patients and the related misallocation of nursing manpower, and the excessive workloads of emergency service providers have become serious issues for Taiwan’s medical institutions. Participants: This study conducted purposive sampling and recruited patients and nursing staffs from the emergency room of a medical center in New Taipei City as the research participants. Methods: This study applied the queueing theory and the derived optimal model to solve the problems of excessive workloads for emergency service providers and misallocation of nursing manpower, in an attempt to provide decision makers with more flexible resource allocation and process improvement suggestions. Results: This study analyzed the causes of emergency service overload and identified solutions for improving nursing manpower utilization. Conclusions: A wait-time model and the queueing theory were used to determine resource parameters for the optimal allocation of patient waiting times and to develop the best model for estimating nursing manpower.

Perspectives of GPs working in or alongside emergency departments in England: qualitative findings from the GPs and Emergency Departments Study

BACKGROUND

Around 43% of emergency department (ED) attendances can be managed in general practice. Strategies to address this include directing appropriate patients to GPs working in or alongside EDs (GPED). Views of GPs choosing to work in GPED roles may inform planning and implementation of GPED services as well as wider general practice provision.

AIM

To explore the experiences and motivations of GPs choosing to work in GPED services in England, and to identify factors that may support or hinder GPs working in GPED roles.

DESIGN AND SETTING

Thematic analysis of 42 semi-structured interviews of GPs working in 10 GPED case sites across England.

METHOD

Qualitative GP interviews from a mixed-methods study of GPs in GPED roles were thematically analysed in relation to research aims.

RESULTS

Four themes were generated: the 'pull' of a portfolio career; the 'push' of disillusionment with general practice; professional reciprocity; sustainability of GPED services and core general practice. Flexible, favourable working conditions, collaboration, and professional development made GPED an attractive workplace, often as part of a portfolio career or after retiring from core general practice. Working in GPED services was largely driven by disillusionment with core general practice. Both GPED and core general practice were thought to benefit from GPED GPs' skills. There were concerns about GPED sustainability and destabilisation of core general practice.

CONCLUSION

GPED may extend the clinical careers of experienced GPs and support recruitment and retention of more recently qualified GPs. Despite some benefits, GPED may destabilise core general practice and increase pressure on both environments.

Machine learning based forecast for the prediction of inpatient bed demand

Background

Overcrowding is a serious problem that impacts the ability to provide optimal level of care in a timely manner. High patient volume is known to increase the boarding time at the emergency department (ED), as well as at post-anesthesia care unit (PACU). Furthermore, the same high volume increases inpatient bed transfer times, which causes delays in elective surgeries, increases the probability of near misses, patient safety incidents, and adverse events.

Objective

The purpose of this study is to develop a Machine Learning (ML) based strategy to predict weekly forecasts of the inpatient bed demand in order to assist the resource planning for the ED and PACU, resulting in a more efficient utilization.

Methods

The data utilized included all adult inpatient encounters at Geisinger Medical Center (GMC) for the last 5 years. The variables considered were class of inpatient encounter, observation, or surgical overnight recovery (SORU) at the time of their discharge. The ML based strategy is built using the K-means clustering method and the Support Vector Machine Regression technique (K-SVR).

Results

The performance obtained by the K-SVR strategy in the retrospective cohort amounts to a mean absolute percentage error (MAPE) that ranges between 0.49 and 4.10% based on the test period. Additionally, results present a reduced variability, which translates into more stable forecasting results.

Conclusions

The results from this study demonstrate the capacity of ML techniques to forecast inpatient bed demand, particularly using K-SVR. It is expected that the implementation of this model in the workflow of bed capacity management will create efficiencies, which will translate in a more reliable, inexpensive and timely care for patients.

Aerodigestive clinic reduces emergency department and primary care utilization and increases access to ancillary and specialty care

OBJECTIVE

The study aimed to evaluate the effect of the aerodigestive clinic (ADC) on healthcare utilization.

STUDY DESIGN

Retrospective quality improvement project; before and after.

SETTING

The ADC at Tripler Army Medical Center (TAMC) in Honolulu, HI.

METHODS

We retrospectively analyzed the electronic medical records of children ≤17 years old seen in the ADC at TAMC between April 2015 and June 2019. The number of emergency department (ED), primary care (PC), specialty care (SC), ancillary care (AC), and teleconsult (TC) encounters were tallied before and after one year of the initial intake visit.

RESULTS

A total of 261 children were included during the study period. Comparing visits before aerodigestive evaluation to after aerodigestive evaluation, the total number of visits before and after were similar with significant changes in the distribution of encounters. The total number of ED (-38%) and PC (-40%) visits decreased significantly (p < 0.001 for both). The total number of other visits were found to have non-significant increases. PC visits accounted for nearly one-third (31%) of all visits prior to the initial ADC visit, but only 19% of visits after. PC visits decreased for all age groups. ED visits decreased by nearly half (-48.1%) for ages 1-17, but there was no change for <1-year olds.

CONCLUSION

There is a statistically significant reduction in the number of emergency department and primary care visits for patients seen in a multidisciplinary ADC. The distribution of visits differed strongly among age groups. These findings emphasize the positive impact that the multidisciplinary clinic has on healthcare utilization for pediatric aerodigestive patients.

A Machine Learning-Based Study of the Effects of Air Pollution and Weather in Respiratory Disease Patients Visiting Emergency Departments

Background. To date, investigating respiratory disease patients visiting the emergency departments related with fined dust is limited. This study aimed to analyze the effects of two variable-weather and air pollution on respiratory disease patients who visited emergency departments. Methods. This study utilized the National Emergency Department Information System (NEDIS) database. The meteorological data were obtained from the National Climate Data Service. Each weather factor reflected the accumulated data of 4 days: a patient’s visit day and 3 days before the visit day. We utilized the RandomForestRegressor of scikit-learn for data analysis. Result. The study included 525,579 participants. This study found that multiple variables of weather and air pollution influenced the respiratory diseases of patients who visited emergency departments. Most of the respiratory disease patients had acute upper respiratory infections [J00–J06], influenza [J09–J11], and pneumonia [J12–J18], on which PM10 following temperature and steam pressure was the most influential. As the top three leading causes of admission to the emergency department, pneumonia [J12–J18], acute upper respiratory infections [J00–J06], and chronic lower respiratory diseases [J40–J47] were highly influenced by PM10. Conclusion. Most of the respiratory patients visiting EDs were diagnosed with acute upper respiratory infections, influenza, and pneumonia. Following temperature, steam pressure and PM10 had influential relations with these diseases. It is expected that the number of respiratory disease patients visiting the emergency departments will increase by day 3 when the steam pressure and temperature values are low, and the variables of air pollution are high. The number of respiratory disease patients visiting the emergency departments will increase by day 3 when the steam pressure and temperature values are low, and the variables of air pollution are high.

The role of telemedicine services in changing users’ intentions for presenting to the emergency departments in Saudi Arabia

Background

Emergency department (ED) overcrowding is described as one of the main issues in any hospital. In Saudi Arabia, the ministry of health applied new telemedicine technology to serve patients by using the mobile application, including the Sehha application and 937 medical call center. The main aim of this study is to determine the role of different telemedicine services in changing the intention users’ intentions for visiting the emergency departments in Saudi Arabia.

Methods

A cross-sectional study was conducted during August 2020 to May 2021 among 319 patients using two telemedicine services in Saudi Arabia, including the medical call center and Sehha smartphone application. The primary endpoint of this study was to determine the number of patients intended to visit ER before and after contacting one of the two telemedicine services and the frequency of people who changed their opinion to visit an ED.

Results

This study analyzed the data from 319 patients who completed the survey provided by the Saudi Ministry of Health concerning information related to their health status and ED visits. Among patients that had the intention to visit the ED (N = 159), 53 of them did not go to EDs after using telemedicine services (p < 0.01). Regarding the medical call center and Sehha application, 9.6% and 24.4%, respectively, of the patients who used these telemedicine services changed their minds concerning visiting ED after taking the medical advice (p < 0.01).

Conclusions

The implemented telemedicine services in Saudi Arabia, namely the Sehha application and medical call center, could reduce those intended to visit ED and consequently reduce the overload of EDs by providing medical advice to patients concerning their minor medical issues.

Improving emergency departments during COVID-19 pandemic: a simulation and MCDM approach with MARCOS methodology in an uncertain environment

The coronavirus disease (COVID-19) pandemic forced healthcare systems to quickly modify to swapping healthcare essentials. The emergency department (ED) decision-making condition is complex and particularly unstable order for care in a stated period conducts decision-makers to attempt to alter assets to touch the demand. ED managers are generally enforced to discover strategies and improving scenarios for decreasing transfer of patients. For this end, the proposed framework of this study is first developed to integrate the simulation model of the flow process of the COVID-19 patients with the Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS) methodology in Spherical fuzzy context to assess and prioritize scenarios based on desired performance measures. As a contribution, the proposed framework determined the importance of the performance measures based on Spherical fuzzy sets. The proposed SF-MARCOS approach takes the performance measures weights from the expert’s team based on spherical fuzzy theory and the performance measures values from the simulation model, and rank the improving scenarios. Finally, a real-life study in a private hospital in Tehran, Iran, illustrates the effectiveness and feasibility of the proposed framework. The analysis of the results shows that the patients’ transfer rate can be reduced by applying new strategies with sensible expenditure.

Association between emergency department length of stay and patient outcomes: A systematic review

In this review, we investigate associations between time spent in the emergency department (ED) and patient reported outcomes. ED staff provide initial assessment, treatment and referral to patients presenting with an acute status to the hospital 24 h a day. ED length of stay, including ED boarding, and treatment received in the ED may affect patient outcomes. In this review we considered published studies that explored the association of ED length of stay of individuals of any age with their subsequent outcomes, including mortality and inpatient length of stay (IPLOS). Joanna Briggs Institute methods for systematic reviews of association were followed. Search strategies were developed to identify studies published in English since 2000 for inclusion. Two reviewers assessed the studies for inclusion and methodological quality and extracted data independently. In total, 34 studies were included in the review, including one case-control, one analytical cross-sectional, and 32 retrospective cohort studies, with a total sample size of 2,308,840 patients. Overall, there were variable associations of time spent in the ED and mortality, IPLOS, time-to-treatment and adverse events. However, findings indicated that older people are at risk for longer ED stays. They may also experience higher mortality. Specific focus should be placed upon elderly people in the ED, to reduce their exposure to the ED environment where possible and to implement focused initiatives that address their specific and complex treatment needs. We conclude that the diversity of individual settings and health systems will require locally defined and relevant solutions to locally identified issues.

Pre- and post-home visit behaviors after using after-hours house call (AHHC) medical services: a questionnaire-based survey in Tokyo, Japan

Background

After-hours house call (AHHC) medical services have been implemented in Japan to reduce ambulance use, as well as overcrowding at the emergency department (ED). Examining the pre-and post-home visit behaviors of those using AHHC medical services will provide insights into the usefulness of these services and help develop strategies to reduce ED visits and ambulance use further.

Methods

This questionnaire-based study used data from anonymized medical records and internet-based questionnaires completed by patients who used AHHC medical services in Tokyo, Japan, between January 1 and December 31, 2019. The questionnaire comprised two questions: (1) What action would the patient have taken in the absence of AHHC services and (2) what action was taken within 3 days following the use of the AHHC services. In addition, following home consultations, AHHC doctors classified the patient’s illness severity as mild (treatable with over-the-counter medications), moderate (requires hospital or clinic visit), or severe (requires ambulance transportation).

Results

Of the 15,787 patients who used AHHC medical services during the study period, 2128 completed the questionnaire (13.5% response rate). Individuals aged ≤15 years and 16–64 years were the most common users of AHHC services (≤15 years, 71.4%; 16–64 years, 26.8%). Before using the AHHC service, 46.4% of the total respondents reported that they would have visited an ED had AHHC services not been available (≤15 years, 47.8%; 16–64 years, 42.8%; ≥65 years, 43.6%). The proportion of patients originally planning to call an ambulance was higher among those in the older age groups (≤15 years, 1.1%; 16–64 years, 6.0%; ≥65 years, 20.5%). After using the AHHC services, most patients (68.1%) did not visit a hospital within 3 days; however, the proportion of patients who visited an ED and called an ambulance within 3 days increased with the severity of illness.

Conclusions

Increasing AHHC medical services awareness among older adults and patients assessed as having severe illnesses regularly availing of AHHC services may help reduce ED visits and ambulance use.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12873-021-00545-w.

Predicting next-day discharge via electronic health record access logs

OBJECTIVE

Hospital capacity management depends on accurate real-time estimates of hospital-wide discharges. Estimation by a clinician requires an excessively large amount of effort and, even when attempted, accuracy in forecasting next-day patient-level discharge is poor. This study aims to support next-day discharge predictions with machine learning by incorporating electronic health record (EHR) audit log data, a resource that captures EHR users' granular interactions with patients' records by communicating various semantics and has been neglected in outcome predictions.

MATERIALS AND METHODS

This study focused on the EHR data for all adults admitted to Vanderbilt University Medical Center in 2019. We learned multiple advanced models to assess the value that EHR audit log data adds to the daily prediction of discharge likelihood within 24 h and to compare different representation strategies. We applied Shapley additive explanations to identify the most influential types of user-EHR interactions for discharge prediction.

RESULTS

The data include 26 283 inpatient stays, 133 398 patient-day observations, and 819 types of user-EHR interactions. The model using the count of each type of interaction in the recent 24 h and other commonly used features, including demographics and admission diagnoses, achieved the highest area under the receiver operating characteristics (AUROC) curve of 0.921 (95% CI: 0.919-0.923). By contrast, the model lacking user-EHR interactions achieved a worse AUROC of 0.862 (0.860-0.865). In addition, 10 of the 20 (50%) most influential factors were user-EHR interaction features.

CONCLUSION

EHR audit log data contain rich information such that it can improve hospital-wide discharge predictions.

Usefulness analysis of the 2018 ASCO/IDSA guideline for outpatient management of fever and neutropenia in adults treated for malignancy

Although the clinical practice guideline for outpatient management of febrile neutropenia (FN) in adults treated for malignancy was updated by the ASCO/IDSA in 2018, most patients with FN in our hospital have been hospitalized. We performed this study to analyze the usefulness of the guideline. The medical records of patients hospitalized for FN in Kyungpook National University Chilgok Hospital from May 2016 to April 2018 were retrospectively reviewed. The feasibility of candidates for outpatient management according to the guideline was evaluated based on the outcomes. A total of 114 patients were enrolled and categorized into two groups, low-risk (38.6%) and high-risk (61.4%). The proportion of feasible candidates for outpatient management was 70.2% and was higher in the low-risk than in the high-risk group (90.0% vs. 57.1%; P < 0.001). The low-risk group had no mortality, no resistance to oral amoxicillin/clavulanate or ciprofloxacin, a higher rate of successful empirical antibiotics, and lower rates of glycopeptide or carbapenem administration. A significant number of hospitalized cancer patients treated for FN after chemotherapy were found to be feasible candidates for outpatient management. The guideline can be a useful tool to reduce labor of healthcare workers and hospitalization costs.

Interprofessional communication in an emergency care unit: a case study

OBJECTIVE

To map internal and external factors in an emergency care unit that interfere with interprofessional communicative practice.

METHOD

This is a single case study carried out in the emergency care unit of a general hospital. Data were collected through participant observation, document analysis, and semi-structured interviews, and were triangulated and subjected to thematic analysis, out of categories defined a priori, based on the SWOT matrix.

RESULTS

Twenty-two health care professionals participated in the study. As for the strengths and opportunities, it was found that professionals understand the importance of communication as a safety measure, and they use the shift change and written communication to share information. However, overcrowding, work overload, the lack of behaviors standardization, the inexperience of professionals, and the deficit in the interprofessional relationship are factors that hinder effective communication.

CONCLUSION

The fragile interprofessional communicative process hampers interaction and information sharing for shared decisions that allow the safe continuity of care.