ReviewInternal and emergency medicine2024
Overcrowding in emergency departments: an overview of reviews describing global solutions and their outcomes.
Review in Internal and emergency medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- An instrument as an action against the blind spot of acute medical care in general practice - a systematic review.BMC primary care · 2025Pooled it
- Article
- Where's the way out? Access block, boarding and hospital overcrowding: a narrative review.Internal and emergency medicine · 2026Review
- Older adults with advanced chronic illness and emergency department care: the hermeneutic stories of Bill, Edie and Nicole.BMC geriatrics · 2026Article
- Impact of hospital restructuring on emergency department quality: a natural experiment.Internal and emergency medicine · 2026Article
- The ethical and clinical conflict of cardiopulmonary resuscitation (CPR) in the emergency department: futile interventions.BMC medical ethics · 2026Article
- Evaluating an AI Decision Support System for the Emergency Department: Retrospective Study.JMIR AI · 2026Article
- Barriers and Facilitators for Medication Safety in Emergency Care Using a SEIPS Model: A Qualitative Study.Journal of nursing management · 2026Article
- The Effect of Emergency Department Nurses' Core Self-Evaluations on Their Perceptions of Clinical Decision-Making: A Cross-Sectional Study.Journal of nursing management · 2026Article
- Perceptions and attitudes of emergency department nurses toward artificial intelligence applications in triage: a qualitative study.Frontiers in public health · 2026Article
- Current situation and influencing factors of emergency department visits by non-urgent young and middle-aged patients in China: A retrospective study.Belitung nursing journal · 2026Article
- Factors Associated With Difficult Emergency Department Referral: A Cross-Sectional Study.Cureus · 2025Article
- Patient Navigation Behavior in a Symptom-Based Self-Triage Mobile App for Direct-to-Consumer Urgent Care: Retrospective Observational Study.Journal of medical Internet research · 2025Observational
- Utilization of acute medical services in general practice: a retrospective routine data analysis.International journal of emergency medicine · 2025Article
- The Epidemiology of Emergency Calls in a Tertiary Emergency Department for Admitted Patients: A TECOR Study.Healthcare (Basel, Switzerland) · 2025Article
- Fifteen Years of Emergency Visits for Whiplash Injuries: Impact of COVID-19 and Campaign to Reduce Minor Injury Admission.Life (Basel, Switzerland) · 2025Article
- Psychiatric Emergency or General Emergency: Evolution or Involution? A Qualitative Study With Mental Health and Emergency Professionals.International journal of mental health nursing · 2025Article
- Artificial intelligence for severity triage based on conversations in an emergency department in Korea.Scientific reports · 2025Article
- Temperature directly correlates with emergency surgical case admissions independent of seasonality.Scientific reports · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Emergency Department (ED) crowding is defined as a situation wherein the demands of emergency services overcome the ability of a department to provide high-quality care within an appropriate time frame. There is a need for solutions, as the harms of crowding impact patients, staff, and healthcare spending. An overview of ED crowding was previously published by our group, which outlines these global issues. The problem of overcrowding in emergency departments has emerged as a global public health concern, and several healthcare agencies have addressed the issue and proposed possible solutions at each level of emergency care. There is no current literature summarizing the extensive research on interventions and solutions, thus there is a need for data synthesis to inform policymakers in this field. The aim of this overview was to summarize the interventions at each level of emergency care: input, throughput, and output. The methodology was supported by the current PRIOR statement for an overview of reviews. The study summarized twenty-seven full-text systematic reviews, which encompassed three hundred and eight primary studies. The results of the summary displayed a requirement for increasing studies in input and output interventions, as these showed the best outcomes with regard to ED crowding metrics. Moreover, the results displayed heterogeneous results at each level of ED care; these reflected that generally solutions have not been matched to specific problems facing regional centres. Thus, individual factors need to be considered when implementing solutions in Emergency Departments.
Indexed as
Identifiers
38041766What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.