Temporal trends in emergency department volumes and crowding metrics in a western Canadian province: a population-based, administrative data study
Notice bibliographique
Résumé
BACKGROUND: Emergency Department (ED) crowding is a pervasive problem, yet there have been few comparisons of the extent of, and contributors to, crowding among different types of EDs. The study quantifies and compares crowding metrics for 16 high volume regional, urban and academic EDs in one Canadian province. METHODS: The National Ambulatory Care Reporting System (NACRS) provided ED presentations by adults to 16 high volume Alberta EDs during April 2010 to March 2015 for this retrospective cohort study. Time to physician initial assessment (PIA), length of stay (LOS) for discharges and admissions were grouped by start hour of presentation and facility. Multiple crowding metrics were created by taking the means, medians (PIA-M, LOS-M), and 90th percentiles of the hourly, ED-specific values. Similarly, proportion left against medical advice (LAMA) and proportion left without being seen (LWBS) were day and ED aggregated. Calculated based on the start of the presentation and the facility and for PIA and LOS. The mean, median, and 90th percentiles for the date and time ED-specific metrics for PIA and LOS were obtained. Summary statistics were used to describe crowding metrics. RESULTS: There were 3,925,457 presentations by 1,420,679 adults. The number of presentations was similar for each sex and the mean age was 46 years. Generally, the three categories of EDs had similar characteristics; however, urban and academic/teaching EDs had more urgent triage scores and a higher percentage of admissions than regional EDs. The median of the PIA-M metric was 1 h23m across all EDs. For discharges, the median of the LOS-M metric was 3h21m whereas the median of the LOS-M metric for admissions was 10h08m. Generally, regional EDs had shorter times than urban and academic/teaching EDs. The median daily LWBS was 3.4% and the median daily LAMA was about 1%. CONCLUSIONS: Emergency presentations have increased over time, and crowding metrics vary considerably among EDs and over the time of day. Academic/teaching EDs generally have higher crowding metrics than other EDs and urgent action is required to mitigate the well-known consequences of ED crowding.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».