Overcrowding in emergency departments in Hong Kong and interventions to improve emergency care
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".