The effect of emergency department crowding on clinically oriented outcomes

Toward a More Nuanced Perspective on Detachment: Differentiating Schizoid and Avoidant Personality Styles through Qualities of the Self-Representation

Avoidant personality disorder was introduced in DSM-III (American Psychiatric Association [APA], 1980), and debate persists regarding the utility of having two separate variants of the "detached personality." The present study addressed this issue through ratings of open-ended self-descriptions provided by community adults with high scores on schizoid versus avoidant personality traits (N = 229). The self-concept of individuals with avoidant personality style reflected a lack of positive self-regard and low self-efficacy/agency. Regarding schizoid personalities, neither positive nor negative self-regard, self-complexity, or self-efficacy/agency was found. Examination of specific variables yielded a relationship between avoidant personality styles, depression, and anxiety, consistent with literature noting simultaneous desire and fear of interpersonal relationships in avoidant patients (APA, 1980; Sheldon & West, 1990). Similarly, examination of individual variables yielded a negative association between schizoid personality styles and tolerance for contradictory aspects of the self, consistent with theoretical writings in this area (Kernberg, 1976; McWilliams, 2006). Results support the argument that these two personality styles represent distinct constructs. Findings support the utility of self-concept assessment to assist treatment planning and differential diagnosis. Treatment implications include using open-ended descriptions of patients' self-concepts to explore changes that may not be accessible via more structured forms of patient self-report.

Interventions to improve emergency department throughput and care delivery indicators: A systematic review and meta-analysis

BACKGROUND

Emergency department (ED) crowding has repercussions on acute care, contributing to prolonged wait times, length of stay, and left without being seen (LWBS). These indicators are regarded as systemic shortcomings, reflecting a failure to provide equitable and accessible acute care. The objective was to evaluate the effectiveness of interventions aimed at improving ED care delivery indicators.

METHODS

This was a systematic review and meta-analysis of randomized controlled trials (RCTs) assessing ED interventions aimed at reducing key metrics of time to provider (TTP), time to disposition (TTD), and LWBS. We excluded disease-specific trials (e.g., stroke). We used Cochrane's revised tool to assess the risk of bias and Grading of Recommendations, Assessment, Development, and Evaluations to rate the quality of evidence. The meta-analysis was performed using a random-effects model and Cochrane Q test for heterogeneity. Data were summarized as means (±SD) for continuous variables and risk ratios (RR) with 95% confidence intervals (CIs).

RESULTS

We searched MEDLINE, EMBASE, and other major databases. A total of 1850 references were scanned and 20 RCTs were selected for inclusion. The trials reported at least one of the three outcomes of TTD, TTP, or LWBS. Most interventions focused on triage liaison physician and point-of-care (POC) testing. Others included upfront expedited workup (ordering tests before full evaluation by a provider), scribes, triage kiosks, and sending notifications to consultants or residents. POC testing decreased TTD by an average of 5-96 min (high heterogeneity) but slightly increased TTP by a mean difference of 2 min (95% CI 0.6-4 min). Utilizing a triage liaison physician reduced TTD by 28 min (95% CI 19-37 min; moderate-quality evidence) and was more effective in reducing LWBS than routine triage (RR 0.76, 95% CI 0.66-0.88; moderate quality).

CONCLUSIONS

Operational strategies such as POC testing and triage liaison physicians could mitigate the impact of ED crowding and appear to be effective. The current evidence supports these strategies when tailored to the appropriate practice environment.

Improvement of Emergency Department Chest Pain Evaluation Using Hs-cTnT and a Risk Stratification Pathway

BACKGROUND

Chest pain is among the most common reasons for presentation to the emergency department (ED) worldwide. Additional studies on most cost-effective ways of differentiating serious vs. benign causes of chest pain are needed.

OBJECTIVES

Our study aimed to evaluate the effectiveness of a novel risk stratification pathway utilizing 5th generation high-sensitivity cardiac troponin T assay (Hs-cTnT) and HEART score (History, Electrocardiogram, Age, Risk factors, Troponin) in assessing nontraumatic chest pain patients in reducing ED resource utilization.

METHODS

A retrospective chart review was performed 6 months prior to and after the implementation of a novel risk stratification pathway that combined hs-cTnT with HEART score to guide evaluation of adult patients presenting with nontraumatic chest pain at a large academic quaternary care ED. Primary outcome was ED length of stay (LOS); secondary outcomes included cardiology consult rates, admission rates, number of ED boarders, and number of eloped patients.

RESULTS

A total of 1707 patients and 1529 patients were included pre- and postimplementation, respectively. Median overall ED LOS decreased from 317 to 286 min, an absolute reduction of 31 min (95% confidence interval 22-41 min), after pathway implementation (p < 0.001). Furthermore, cardiology consult rate decreased from 26.9% to 16.0% (p < 0.0001), rate of admission decreased from 30.1% to 22.7% (p < 0.0001), and number of ED boarders as a proportion of all nontraumatic chest pain patients decreased from 25.13% preimplementation to 18.63% postimplementation (p < 0.0001).

CONCLUSIONS

Implementation of our novel chest pain pathway improved numerous ED throughput metrics in the evaluation of nontraumatic chest pain patients.

Determinants of appropriate antibiotic and NSAID prescribing in unscheduled outpatient settings in the veterans health administration

BACKGROUND

Despite efforts to enhance the quality of medication prescribing in outpatient settings, potentially inappropriate prescribing remains common, particularly in unscheduled settings where patients can present with infectious and pain-related complaints. Two of the most commonly prescribed medication classes in outpatient settings with frequent rates of potentially inappropriate prescribing include antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs). In the setting of persistent inappropriate prescribing, we sought to understand a diverse set of perspectives on the determinants of inappropriate prescribing of antibiotics and NSAIDs in the Veterans Health Administration.

METHODS

We conducted a qualitative study guided by the Consolidated Framework for Implementation Research and Theory of Planned Behavior. Semi-structured interviews were conducted with clinicians, stakeholders, and Veterans from March 1, 2021 through December 31, 2021 within the Veteran Affairs Health System in unscheduled outpatient settings at the Tennessee Valley Healthcare System. Stakeholders included clinical operations leadership and methodological experts. Audio-recorded interviews were transcribed and de-identified. Data coding and analysis were conducted by experienced qualitative methodologists adhering to the Consolidated Criteria for Reporting Qualitative Studies guidelines. Analysis was conducted using an iterative inductive/deductive process.

RESULTS

We conducted semi-structured interviews with 66 participants: clinicians (N = 25), stakeholders (N = 24), and Veterans (N = 17). We identified six themes contributing to potentially inappropriate prescribing of antibiotics and NSAIDs: 1) Perceived versus actual Veterans expectations about prescribing; 2) the influence of a time-pressured clinical environment on prescribing stewardship; 3) Limited clinician knowledge, awareness, and willingness to use evidence-based care; 4) Prescriber uncertainties about the Veteran condition at the time of the clinical encounter; 5) Limited communication; and 6) Technology barriers of the electronic health record and patient portal.

CONCLUSIONS

The diverse perspectives on prescribing underscore the need for interventions that recognize the detrimental impact of high workload on prescribing stewardship and the need to design interventions with the end-user in mind. This study revealed actionable themes that could be addressed to improve guideline concordant prescribing to enhance the quality of prescribing and to reduce patient harm.

Doctors' perspectives on the quality of medical imaging in public hospitals in eThekwini District

Background

There is a paucity of literature on perspectives of referring doctors about the quality of medical imaging services and this study closes this gap in literature.

Aim

This quality assurance (QA) study aimed to explore the perspectives of doctors on the quality of medical imaging services in selected regional hospitals within eThekwini District of KwaZulu-Natal.

Setting

The study was conducted in four public regional hospitals.

Methods

An exploratory descriptive qualitative research design involving 30 min-45 min of in-depth individual interviews was used. A purposive sampling technique was used to select research participants and hospitals to ensure adequate responses to the research questions. The sample involved nine participants and was guided by data saturation. Responses were recorded through notes and voice recordings and thematic analysis was used to analyse data.

Results

Three main themes (timeliness of examinations, communication and radiology reports and image quality) and eight subthemes (waiting times, shortage of radiographers, workload, communication between doctors and radiographers, requisition forms, unavailability of radiology reports, clarity of images and image acquisition protocols) emerged from the data. Challenges experienced were exacerbated by high workload and shortage of radiologists and radiographers. Doctors in the data collection sites were mainly dissatisfied with services provided by the medical imaging departments.

Conclusion

Regular engagements between medical imaging departments and doctors are important in enhancing the provision of quality care to patients. In-service training of radiographers and employment of additional radiographers and finding solutions to mitigate shortage of radiologists are recommended.

Contribution

This quality assurance (QA) study focused on experiences of doctors while many other medical imaging QA studies in South Africa are equipment based. In-service training of radiographers is recommended to improve image quality and communication skills.

Free-for-all: Does crowding impact outcomes because hospital emergency departments do not prioritise effectively?

Unexpected peaks in volumes of attendances at hospital emergency departments (EDs) have been found to affect waiting times, intensity of care and outcomes. We ask whether these effects of ED crowding on patients are caused by poor clinical prioritisation or a quality-quantity trade-off generated by a binding capacity constraint. We study the effects of crowding created by lower-severity patients on the outcomes of approximately 13 million higher-severity patients attending the 140 public EDs in England between April 2016 and March 2017. Our identification approach relies on high-dimensional fixed effects to account for planned capacity. Unexpected demand from low-severity patients has very limited effects on the care provided to higher-severity patients throughout their entire pathway in ED. Detrimental effects of crowding caused by low-severity patients materialise only at very high levels of unexpected demand, suggesting that binding resource constraints impact patient care only when demand greatly exceeds the ED's expectations. These effects are smaller than those caused by crowding induced by higher-severity patients, suggesting an efficient prioritisation of incoming patients in EDs.

Redesigning Medication Management in the Emergency Department: The Impact of Partnered Pharmacist Medication Charting on the Time to Administer Pre-Admission Time-Critical Medicines, Medication Order Completeness, and Venous Thromboembolism Risk Assessment

In order to enhance interdisciplinary collaboration and promote better medication management, a partnered pharmacist medication charting (PPMC) model was piloted in the emergency department (ED) of an Australian referral hospital. The primary objective of this study was to evaluate the impact of PPMC on the timeliness of time-critical medicines (TCMs), completeness of medication orders, and assessment of venous thromboembolism (VTE) risk. This concurrent controlled retrospective pragmatic trial involved individuals aged 18 years and older presenting to the ED from 1 June 2020 to 17 May 2021. The study compared the PPMC approach (PPMC group) with traditional medical officer-led medication charting approaches in the ED, either an early best-possible medication history (BPMH) group or the usual care group. In the PPMC group, a BPMH was documented promptly soon after arrival in the ED, subsequent to which a collaborative discussion, co-planning, and co-charting of medications were undertaken by both a PPMC-credentialled pharmacist and a medical officer. In the early BPMH group, the BPMH was initially obtained in the ED before proceeding with the traditional approach of medication charting. Conversely, in the usual care group, the BPMH was obtained in the inpatient ward subsequent to the traditional approach of medication charting. Three outcome measures were assessed -the duration from ED presentation to the TCM's first dose administration (e.g., anti-Parkinson's drugs, hypoglycaemics and anti-coagulants), the completeness of medication orders, and the conduct of VTE risk assessments. The analysis included 321 TCMs, with 107 per group, and 1048 patients, with 230, 230, and 588 in the PPMC, early BPMH, and usual care groups, respectively. In the PPMC group, the median time from ED presentation to the TCM's first dose administration was 8.8 h (interquartile range: 6.3 to 16.3), compared to 17.5 h (interquartile range: 7.8 to 22.9) in the early BPMH group and 15.1 h (interquartile range: 8.2 to 21.1) in the usual care group (p < 0.001). Additionally, PPMC was associated with a higher proportion of patients having complete medication orders and receiving VTE risk assessments in the ED (both p < 0.001). The implementation of the PPMC model not only expedited the administration of TCMs but also improved the completeness of medication orders and the conduct of VTE risk assessments in the ED.

Low Back Pain: Utilization of Urgent Cares Relative to Emergency Departments

STUDY DESIGN/SETTING

Retrospective study.

OBJECTIVE

To understand why patients utilize emergency departments (EDs) versus urgent care centers for low back pain (LBP).

SUMMARY OF BACKGROUND DATA

LBP is a common reason for ED visits. In the setting of trauma or recent surgery, the resources of EDs may be needed. However, urgent care centers may be appropriate for other cases.

MATERIALS AND METHODS

Adult patients below 65 years of age presenting to the ED or urgent care on the day of diagnosis of LBP were identified from the 2019 PearlDiver M151 administrative database. Exclusion criteria included history of radiculopathy or sciatica, spinal surgery, spinal cord injury, other traumatic, neoplastic, or infectious diagnoses in the 90 days prior, or Medicare insurance. Patient age, sex, Elixhauser comorbidity index, geographic region, insurance, and management strategies were extracted. Factors associated with urgent care relative to ED utilization were assessed using multivariable analysis.

RESULTS

Of 356,284 LBP patients, ED visits were identified for 345,390 (96.9%) and urgent care visits for 10,894 (3.1%). Factors associated with urgent care use relative to the ED were: geographic region [relative to Midwest; Northeast odds ratio (OR): 5.49, South OR: 1.54, West OR: 1.32], insurance (relative to Medicaid; commercial OR: 4.06), lower Elixhauser comorbidity index (OR: 1.28 per two-point decrease), and higher age (OR: 1.10 per decade), female sex (OR: 1.09), and use of advanced imaging (OR: 0.08) within 1 week ( P <0.001 for all).

CONCLUSIONS

Most patients presenting for a first diagnosis of isolated LBP went to the ED relative to urgent care. The greatest drivers of urgent care versus ED utilization for LBP were insurance type and geographic region. Utilization of advanced imaging was higher among ED patients, but rates of surgical intervention were similar between those seen in the ED and urgent care.

Adapting to Change: A Systematic Literature Review of Environmental Flexibility in Emergency Departments

PURPOSE

This study aimed to offer a comprehensive analysis of distinct design strategies identified, evaluated, or discussed in the existing literature that promote environmental flexibility in the context of emergency departments (EDs).

BACKGROUND

EDs are subject to constant changes caused by several factors, including seasonal disease trends, the emergence of new technologies, and surges resulting from local or global disasters, such as mass casualty incidents or pandemics. Thus, integrating flexibility into ED design becomes crucial to effectively addressing these evolving needs.

METHODS

A systematic search was conducted in four databases: CINAHL, MEDLINE, PubMed, and ScienceDirect, in addition to a hand search. A two-stage review process was employed to determine the final list of included articles based on the inclusion criteria. Included studies were evaluated for quality, and findings were categorized using a hybrid deductive and inductive coding approach.

RESULTS

From the initial yield of 900 records, 22 studies met the inclusion criteria and were included in the final full-text review. The identified design strategies were organized into five categories: modifiability (n = 13 articles), versatility (n = 8 articles), tolerance (n = 6 articles), convertibility (n = 4 articles), and scalability (n = 7 articles). Specific design strategies under each category are reported in detail.

CONCLUSIONS

Our findings suggest that most flexibility design solutions are based on anecdotal evidence or descriptive studies, which carry less weight in terms of reliable support for conclusions. Therefore, more studies employing quantitative, relational, or causal designs are recommended.

Use of Perioperative Advanced Practice Providers to Reduce Cost and Readmission in the Postoperative Hepatopancreatobiliary Population: Results of a Simulation Study

BACKGROUND

Postoperative healthcare use and readmissions are common among the hepatopancreatobiliary (HPB) population. We evaluated the surgical volume required to sustain advanced practice providers (APPs) in the perioperative setting for cost reduction.

STUDY DESIGN

Using decision analysis modeling, we evaluated costs of employing dedicated perioperative APP navigators compared with no APPs navigators. Simulated subjects could: (1) present to an emergency department, with or without readmission, (2) present for direct readmission, (3) require additional office visits, or (4) require no additional care. We informed our model using the most current available published data and performed sensitivity analyses to evaluate thresholds under which dedicated perioperative APP navigators are beneficial.

RESULTS

Subjects within the APP navigator cohort accumulated $1,270 and a readmission rate of 6.9%, compared with $2,170 and 13.5% with no APP navigators, yielding a cost savings of $905 and 48% relative reduction in readmission. Based on these estimated cost savings and national salary ranges, a perioperative APPs become financially self-sustaining with 113 to 139 annual HPB cases, equating to 2 to 3 HPB cases weekly. Sensitivity analyses revealed that perioperative APP navigators were no longer cost saving when direct readmission rates exceeded 8.9% (base case 3.7%).

CONCLUSIONS

We show that readmissions are reduced by nearly 50% with an associated cost savings of $900 when employing dedicated perioperative APPs. This position becomes financially self-sufficient with an annual HPB case load of 113 to 139 cases. High-volume HPB centers could benefit from postdischarge APP navigators to optimize outcomes, minimize high-value resource use, and ultimately save costs.

Strategies to improve care for older adults who present to the emergency department: a systematic review

BACKGROUND

The aim of this systematic review was to examine the relationship between strategies to improve care delivery for older adults in ED and evaluation measures of patient outcomes, patient experience, staff experience, and system performance.

METHODS

A systematic review of English language studies published since inception to December 2022, available from CINAHL, Embase, Medline, and Scopus was conducted. Studies were reviewed by pairs of independent reviewers and included if they met the following criteria: participant mean age of ≥ 65 years; ED setting or directly influenced provision of care in the ED; reported on improvement interventions and strategies; reported patient outcomes, patient experience, staff experience, or system performance. The methodological quality of the studies was assessed by pairs of independent reviewers using The Joanna Briggs Institute critical appraisal tools. Data were synthesised using a hermeneutic approach.

RESULTS

Seventy-six studies were included in the review, incorporating strategies for comprehensive assessment and multi-faceted care (n = 32), targeted care such as management of falls risk, functional decline, or pain management (n = 27), medication safety (n = 5), and trauma care (n = 12). We found a misalignment between comprehensive care delivered in ED for older adults and ED performance measures oriented to rapid assessment and referral. Eight (10.4%) studies reported patient experience and five (6.5%) reported staff experience.

CONCLUSION

It is crucial that future strategies to improve care delivery in ED align the needs of older adults with the purpose of the ED system to ensure sustainable improvement effort and critical functioning of the ED as an interdependent component of the health system. Staff and patient input at the design stage may advance prioritisation of higher-impact interventions aligned with the pace of change and illuminate experience measures. More consistent reporting of interventions would inform important contextual factors and allow for replication.

Inequities among patient placement in emergency department hallway treatment spaces

BACKGROUND

Limited capacity in the emergency department (ED) secondary to boarding and crowding has resulted in patients receiving care in hallways to provide access to timely evaluation and treatment. However, there are concerns raised by physicians and patients regarding a decrease in patient centered care and quality resulting from hallway care. We sought to explore social risk factors associated with hallway placement and operational outcomes.

STUDY DESIGN/METHODS

Observational study between July 2017 and February 2020. Primary outcome was the adjusted odds ratio (aOR) of patient placement in a hallway treatment space adjusting for patient demographics and ED operational factors. Secondary outcomes included left without being seen (LWBS), discharge against medical advice (AMA), elopement, 72-h ED revisit, 10-day ED revisit and escalation of care during boarding.

RESULTS

Among 361,377 ED visits, 100,079 (27.7%) visits were assigned to hallway beds. Patient insurance coverage (Medicaid (aOR 1.04, 95% CI 1.01,1.06) and Self-pay/Other (1.08, (1.03, 1.13))) with comparison to private insurance, and patient sex (Male (1.08, (1.06, 1.10))) with comparison to female sex are associated with higher odds of hallway placement but patient age, race, and language were not. These associations are adjusted for ED census, triage assigned severity, ED staffing, boarding level, and time effect, with social factors mutually adjusted. Additionally adjusting for patients' social factors, patients placed in hallways had higher odds of elopement (1.23 (1.07,1.41)), 72-h ED revisit (1.33 (1.08, 1.64)) and 10-day ED revisit (1.23 (1.11, 1.36)) comparing with patients placed in regular ED rooms. We did not find statistically significant associations between hallway placement and LWBS, discharge AMA, or escalation of care.

CONCLUSION

While hallway usage is ad hoc, we find consistent differences in care delivery with those insured by Medicaid and self-pay or male sex being placed in hallway beds. Further work should examine how new front-end processes such as provider in triage or split flow may be associated with inequities in patient access to emergency and hospital care.

Clinical and economic impact of partnered pharmacist medication charting in the emergency department

Introduction: Partnered pharmacist medication charting (PPMC), a process redesign hypothesised to improve medication safety and interdisciplinary collaboration, was trialed in a tertiary hospital's emergency department (ED). Objective: To evaluate the health-related impact and economic benefit of PPMC. Methods: A pragmatic, controlled study compared PPMC to usual care in the ED. PPMC included a pharmacist-documented best-possible medication history (BPMH), followed by a clinical conversation between a pharmacist and a medical officer to jointly develop a treatment plan and chart medications. Usual care included medical officer-led traditional medication charting in the ED, without a pharmacist-obtained BPMH or clinical conversation. Outcome measures, assessed after propensity score matching, were length of hospital or ED stay, relative stay index (RSI), in-hospital mortality, 30-day hospital readmissions or ED revisits, and cost. Results: A total of 309 matched pairs were analysed. The median RSI was reduced by 15.4% with PPMC (p = 0.029). There were no significant differences between the groups in the median length of ED stay (8 vs. 10 h, p = 0.52), in-hospital mortality (1.3% vs. 1.3%, p > 0.99), 30-day readmission rates (21% vs. 17%; p = 0.35) and 30-day ED revisit rates (21% vs. 19%; p = 0.68). The hospital spent approximately $138.4 for the cost of PPMC care per patient to avert at least one medication error bearing high/extreme risk. PPMC saved approximately $1269 on the average cost of each admission. Conclusion: Implementing the ED-based PPMC model was associated with a significantly reduced RSI and admission costs, but did not affect clinical outcomes, noting that there was an additional focus on medication reconciliation in the usual care group relative to current practice at our study site.

Safety and Feasibility of an Emergency Department-to-Outpatient Pathway for Patients With TIA and Nondisabling Stroke

Background and Objectives

Evaluation of transient ischemic attack/nondisabling ischemic strokes (TIA/NDS) in the emergency department (ED) contributes to capacity issues and increasing health care expenditures, especially high-cost duplicative imaging.

Methods

As an institutional quality improvement project, we developed a novel pathway to evaluate patients with TIA/NDS in the ED using a core set of laboratory tests and CT-based neuroimaging. Patients identified as 'low risk' through a safety checklist were discharged and scheduled for prompt outpatient tests and stroke clinic follow-up. In this prespecified analysis designed to assess feasibility and safety, we abstracted data from patients consecutively enrolled in the first 6 months.

Results

We compared data from 106 patients with TIA/NDS enrolled in the new pathway from April through September 2020 (age 67.9 years, 45% female), against 55 unmatched historical controls with TIA encountered from April 2016 through March 2017 (age 68.3 years, 47% female). Both groups had similar median NIHSS scores (pathway and control 0) and ABCD2 scores (pathway and control 3). Pathway-enrolled patients had a 44% decrease in mean ED length of stay (pathway 13.7 hours, control 24.4 hours, p < 0.001) and decreased utilization of ED MRI-based imaging (pathway 63%, control 91%, p < 0.001) and duplicative ED CT plus MRI-based brain and/or vascular imaging (pathway 35%, control 53%, p = 0.04). Among pathway-enrolled patients, 89% were evaluated in our stroke clinic within a median of 5 business days; only 5.5% were lost to follow-up. Both groups had similar 90-day rates of ED revisits (pathway 21%, control 18%, p = 0.84) and recurrent TIA/ischemic stroke (pathway 1%, control 2%, p = 1.0). Recurrent ischemic events among pathway-enrolled patients were attributed to errors in following the safety checklist before discharge.

Discussion

Our TIA/NDS pathway, implemented during the initial outbreak of COVID-19, seems feasible and safe, with significant positive impact on ED throughput and ED-based high-cost duplicative imaging. The safety checklist and option of virtual telehealth follow-up are novel features. Broader adoption of such pathways has important implications for value-based health care.

A Systematic Review of the Causes, Consequences, and Solutions of Emergency Department Overcrowding in Saudi Arabia

This study aims to investigate and address the issue of emergency department (ED) overcrowding, a significant problem worldwide. The study seeks to understand the impacts of ED overcrowding on emergency medical healthcare services and patient outcomes. This systematic review follows the PRISMA flow diagram and the guidelines of the Cochrane Handbook. We systematically reviewed the causes and solutions of emergency department overcrowding. We went through Google Scholar, the National Center for Biotechnology Information, the British Medical Journal, Science Direct, Ovid, Cochrane, the Saudi Journal of Emergency Medicine, Medline, and PubMed as databases. Our criteria were articles done in Saudi Arabia from 2012 to 2022. One hundred and ninety-six (196) research papers were extracted; only 28 articles met our paper inclusion-exclusion criteria. The result of these papers regarding causes, consequences, and solutions was that non-urgent and returned visits lacked knowledge of PHC, triad, and telemedicine services. Prolonged LOS is due to slow bed turnover, laboratory and consultation time, and physical response to the final decision resulting in burnout staff, wrong diagnoses, and management plans. The crowding issues can be resolved by awareness, PHC access, triad systems, and technological and telemedicine services. High demand for emergency treatment should not be a hindrance to quality treatment. Physical, technological, and strategic measures should be put in place to fight the crowding problem in EDs in Saudi Arabia, as it may cause adverse effects such as transmission of diseases and death of patients.

Medical Admission Prediction Score (MAPS); a simple tool to predict medical admissions in the emergency department

INTRODUCTION

Overcrowding in the emergency departments (ED) is linked to adverse clinical outcomes, a negative impact on patient safety, patient satisfaction, and physician efficiency. We aimed to design a medical admission prediction scoring system based on readily available clinical data during ED presentation.

METHODS

In this retrospective cross-sectional study, data on ED presentations and medical admissions were extracted from the Emergency and Internal Medicine departments of a tertiary care facility in Qatar. Primary outcome was medical admission.

RESULTS

Of 320299 ED presentations, 218772 were males (68.3%). A total of 11847 (3.7%) medical admissions occurred. Most patients were Asians (53.7%), followed by Arabs (38.7%). Patients who got admitted were older than those who did not (p <0.001). Admitted patients were predominantly males (56.8%), had a higher number of comorbid conditions and a higher frequency of recent discharge (within the last 30 days) (p <0.001). Age > 60 years, female gender, discharge within the last 30 days, and worse vital signs at presentations were independently associated with higher odds of admission (p<0.001). These factors generated the scoring system with a cut-off of >17, area under the curve (AUC) 0.831 (95% CI 0.827-0.836), and a predictive accuracy of 83.3% (95% CI 83.2-83.4). The model had a sensitivity of 69.1% (95% CI 68.2-69.9), specificity was 83.9% (95% CI 83.7-84.0), positive predictive value (PPV) 14.2% (95% CI 13.8-14.4), negative predictive value (NPV) 98.6% (95% CI 98.5-98.7) and positive likelihood ratio (LR+) 4.28% (95% CI 4.27-4.28).

CONCLUSION

Medical admission prediction scoring system can be reliably applied to the regional population to predict medical admissions and may have better generalizability to other parts of the world owing to the diverse patient population in Qatar.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

PROBLEM

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Artificial intelligence for predicting acute appendicitis: a systematic review

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

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

Background

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

Objective(s), study design, settings and participants

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

Results

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

Limitations

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

Conclusions

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

Future work

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

Study registrations

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

Funding

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

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

INTRODUCTION

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

METHODS AND ANALYSIS

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

ETHICS AND DISSEMINATION

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

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

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

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

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

Utilizing Triage Data for Medical Imaging Studies in the Emergency Department

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

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION

N/A.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

OBJECTIVES

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

DESIGN

Hospital-based cohort study.

SETTING

All public hospitals in Denmark.

PARTICIPANTS

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

INTERVENTIONS

Stepped-wedge reconfiguration of the Danish emergency healthcare system.

MAIN OUTCOME MEASURES

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

RESULTS

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

CONCLUSIONS

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

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

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

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

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

STUDY OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

Background

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

Objective

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

Design

Observational cohort study.

Setting and Patients

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

Measurements

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

Methods

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

Results

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

Limitations

Missing data, requires external validation.

Conclusion

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

Early prediction of hospital admission of emergency department patients

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

Introduction

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

Methods

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

Results

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

Conclusion

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

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

BACKGROUND

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

CLINICAL QUESTION

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

EVIDENCE REVIEW

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

CONCLUSION

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

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

BACKGROUND

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

AIM

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

METHODS

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

RESULTS

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

CONCLUSION

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

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Association of emergency department crowding with inpatient outcomes

OBJECTIVE

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

DATA SOURCES AND STUDY SETTING

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

STUDY DESIGN

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

EXTRACTION METHODS

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

PRINCIPAL FINDINGS

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

CONCLUSIONS

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

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

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