Quality of care and emergency department throughput during the COVID-19 pandemic in a community health system Pandemic in a Community Health System
Notice bibliographique
Résumé
Objective: This retrospective study explores the strategic plan formulated by AHMC Health System in California, USA, to sustain and improve quality of care and emergency department (ED) efficiency during the COVID-19 pandemic. It also analyzes the plan’s outcomes.Background: The COVID-19 pandemic has posed challenges for both individuals and healthcare industries alike, impacting decision-making and access to care. AHMC faced staff and resource shortages, patient reluctance, and difficulties adapting to rapidly evolving public health guidelines. These challenges highlighted the critical need for effective plans to maintain or improve healthcare quality and ED performance.Methods: AHMC adopted a comprehensive three-layer strategic plan in 2020. The first layer, “Pandemic Response,” focused on leadership, staff training and education, infection control, new treatments, and employee vaccination rates. The second layer, “ED Throughput,” set objectives for metrics such as door-to-doctor (door-to-doc) time, ancillary turnaround time (TAT), ED length of stay (LOS), and the left-without-being-seen (LWBS) rates. Progress was monitored through monthly improvement meetings. The third layer, “Quality Excellence,” tracked improvements in COVID-adapted objectives on quality initiatives, based on CMS Quality Star Ratings, Leapfrog Hospital Safety Grades, and Yelp review scores.Results: By 2023, the three-layer strategic plan had led to many improvements in the quality of care and ED efficiency. AHMC identified 22,287 positive COVID-19 cases, expanded its ventilator inventory by 50%, and enhanced patient outcomes by applying updated treatments. Additionally, AHMC saw a 3% reduction in ED wait times and sustained its overall patient satisfaction rates, CMS Quality Star Rating, and Leapfrog Hospital Safety Grade scores.Conclusions: AHMC’s three-layer strategic plan showed effectiveness in maintaining quality of care and ED efficiency during the COVID-19 pandemic. By focusing on “Pandemic Response,” “ED Throughput,” and “Quality Excellence,” AHMC was able to adapt to the rapidly evolving public health guidelines, expand its capacity to treat COVID-19 patients and sustain its overall patient safety, satisfaction, and quality ratings. The implementation of this plan highlights the importance of proactive and comprehensive strategies in managing healthcare crises.
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 | 0,006 | 0,001 |
| 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 | 0,002 |
| 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 ».