Overcrowding in emergency departments in Hong Kong and interventions to improve emergency care
Notice bibliographique
Résumé
Background Emergency department (ED) overcrowding has become a worldwide problem over the past few years, which has been reported in USA, Canada, New Zealand and Australia. For the past two decades, ED overcrowding has also become a controversial issue in Hong Kong, due to high demand for emergency service and misuse of emergency services. In 2002, although there was a charge for emergency department visit which led to a markedly decrease (19.1%) of total attendance, but now the rising trend of ED overcrowding seems to resurface during past few years. This paper aims to review and synthesis causes of ED overcrowding and possible interventions so as to provide possible recommendations for emergency care in Hong Kong. \n \nMethods Literatures on ED overcrowding and potential interventions were searched from PubMed, Google Scholar and Google to locate all relevant articles in English up to May 2013. Through PubMed, ED was described using “Emergency Medicine [MeSH]” OR “emergency department” OR “emergency”, and overcrowding was described using “Crowding [MeSH]” OR “crowded” OR “overcrowding” OR “overcrowded” OR “congestion”, and interventions was described using “interventions” OR “solutions”. Besides, relevant emergency medicine literatures published from the Hong Kong Journal of Emergency Medicine were also reviewed. \n \nResults \nI identified and reviewed relevant articles and found that ED attendance has been steadily rising during the past decades in Hong Kong. Although the causes may be somewhat different between different countries, causes of ED overcrowding could be related to easy access to emergency services, barriers to primary care as well as specialist care, and the rising aging population which might be an important underlying cause. As the problem of ED overcrowding will have significant negative impact on patient outcomes, such as unnecessary death, two common interventions to the problem are increasing the resources and demand management. Apart from increasing resources within emergency departments to cater for the increasing demand, it is of highly significance to improve community and primary care for the needs of older people who will contribute a great proportion to ED overcrowding in the future. \n \nConclusion Semi-urgent and non-urgent visits do account for a great proportion among the total attendance, so it is important triage these patients to alleviate the overcrowding. What’s more, pressure on EDs can be related to a significant increase in the number of elderly patients who may require more investigation or admissions, and need much longer time to manage. As a result, future health policies should focus more on the aging population to improve emergency care.
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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 | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».