Emergency Department Overcrowding: An Environmental Scan of Contributing Factors and a Summary of Systematic Review Evidence on Interventions
Notice bibliographique
Résumé
Emergency department (ED) overcrowding occurs when the demand for health services in the ED exceeds the capacity of the ED, hospital, or community to deliver quality care in a reasonable amount of time. Overcrowding is worsening in jurisdictions across Canada and there is a need to address its many causes and identify potential solutions. This report uses a modified version of a conceptual model developed by Asplin et al. (2003) that organizes the emergency care system into 3 interdependent parts: input (arrival to the ED), throughput (flowing through the ED), and output (leaving the ED). We also examined an additional fourth part related to contextual factors and systems that affect overcrowding but lay outside of input, throughout, and output. Examples of factors include, but are not limited to, increased complexity of needs (input), diagnostic testing and procedures (throughput), boarding (output), and limited resources for mental health and substance use (outside the ED). Examples of interventions that were effective in some settings include, but are not limited to, prehospital decision-making by first responders, which reduced ED visits (input); short stay crisis units for people experiencing mental health challenges, which improved emergency department length of stay, wait times, boarding, and patient safety (throughput); ED-based discharge planning, which reduced ED return visits (output); and time-based policy reforms, which reduced ED length of stay (outside the ED). Most of the factors we identified in the published literature existed either outside of the ED or at the interface of the ED and other health care services (input and output), whereas most of the interventions we identified existed within the ED (throughput). We heard from participants (during multistakeholder dialogue sessions) and content experts that ED overcrowding is a complex health system issue for which the causes, impacts, and solutions extend beyond the ED. Specifically, the novel insights we heard included: ED overcrowding is better viewed as a problem of hospital overcrowding and strained resources in the broader social and health care systems. Contributing factors both within and outside the ED influence and interact with each other and are affected by economic, cultural, and institutional realities. Solving the issue requires addressing accountability and implementing multifaceted solutions in which several systems and voices work collaboratively. Existing technologies and data use and collection are not being used to their full potential; they can be better leveraged to alleviate this issue. In the identified literature, there was a lack of explicit reporting around equity and ethical considerations for factors contributing to, and interventions to alleviate, ED overcrowding. Future work should strive to deliberately and explicitly include ethical considerations inherent in research, planning, and policy-making; considerations of equity-deserving groups; and dedicate the time needed to consider the various facets of this issue. This CADTH report and our series of reports on ED overcrowding are a starting point to bridge the literature, stakeholder discussion, and expert opinion to help decision-makers understand the various parts of the issue and consult the relevant updated evidence to inform their work.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,106 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,011 |
| Bibliométrie | 0,025 | 0,026 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».