Improving emergency department flow through Rapid Medical Evaluation unit
Notice bibliographique
Résumé
The Toronto Western Hospital is an academic hospital in Toronto, Canada, with an annual Emergency Department (ED) volume of 64,000 patients. Despite increases in patient volumes of almost six percent per annum over the last decade, there have been no commensurate increases in resources, infrastructure, and staffing. This has led to substantial increase in patient wait times, most specifically for those patients with lower acuity presentations. Despite requiring only minimal care, these patients contribute disproportionately to ED congestion, which can adversely impact resource utilization and quality of care for all patients. We undertook a retrospective evaluation of a quality improvement initiative aimed at improving wait times experienced by patients with lower acuity presentations. A rapid improvement event was organized by frontline workers to rapidly overhaul processes of care, leading to the creation of the Rapid Medical Evaluation (RME) unit - a new pathway of care for patients with lower acuity presentations. The RME unit was designed by re-purposing existing resources and re-assigning one physician and one nurse towards the specific care of these patients. We evaluated the performance of the RME unit through measurement of physician initial assessment (PIA) times and total length of stay (LOS) times for multiple groups of patients assigned to various ED care pathways, during three periods lasting three months each. Weekly measurements of mean and 90th percentile of PIA and LOS times showed special cause variation in all targeted patient groups. Of note, the patients seen in the RME unit saw their median PIA and LOS times decrease from 98min to 70min and from 165min to 130min, respectively, from baseline. Despite ever-growing numbers of patient visits, wait times for all patients with lower acuity presentations remained low, and wait times of patients with higher acuity presentations assigned to other ED care pathways were not adversely affected. By specifically re-purposing a fraction of existing staff, resources, and infrastructure for patients with lower acuity presentations, we were able to streamline their care and decrease wait times in the ED. These results were achieved through the incremental improvements afforded by rapidly cycling through PDSA cycles, with strong frontline staff involvement and continuously eliciting feedback for improvement. We believe the model to be replicable in other academic medical centres.
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 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 | 0,008 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,002 | 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 ».