Barriers to and facilitators of the development and utilization of context appropriate evidence based clinical algorithms to optimize clinical care and patient outcomes in the Tikur Anbessa emergency department: a multi-component qualitative study
Notice bibliographique
Résumé
BACKGROUND: Evidence-based clinical algorithms (EBCA) are knowledge tools to promote evidence use by codifying evidence into action plans to facilitate appropriate care. However, their impact on process and outcomes of care varies considerably across practice settings and providers, highlighting the need for tailoring of both these knowledge tools and their implementation strategies to target end users and the setting in which EBCAs are to be employed. Leadership at the Tikur Anbessa Specialized Hospital emergency department (TASH-ED) in Addis Ababa, Ethiopia identified a need for context-appropriate EBCAs to improve evidence uptake to mitigate care gaps in this high volume, high acuity setting. We aimed to identify barriers and facilitators to utilization of EBCAs in the TASH-ED, to identify priority targets for development of EBCAs tailored for the TASH-ED context and to understand the process of care in the TASH-ED to inform implementation planning. METHODS: We employed a multi-component qualitative design including: semi-structured interviews with TASH-ED clinical, administrative and support services staff, and Toronto EM physicians who had worked in the TASH-ED; direct observation of the process of care in TASH-ED; document review. RESULTS: Although most TASH-ED participants reported an awareness of EBCAs, they noted little or no experience using them, primarily due to the poor fit of many EBCAs to their practice setting. All participants felt that context-appropriate EBCAs were needed to ensure standardized and evidence-based care and improve patient outcomes for common ED presentations. Trauma, sepsis, acute cardiac conditions, hypertensive emergencies, and diabetic keto-acidosis were most commonly identified as priorities for EBCA development. Lack of medication, equipment and human resources were identified as the primary barriers to use of EBCAs in the TASH-ED. Support from leadership and engagement of stakeholders outside the ED where EBCAs were believed to be less well accepted were identified as essential facilitators to implementation of EBCAs in the TASH-ED. CONCLUSIONS: This study found a perceived need for EBCAs tailored to the TASH-ED setting to support uptake of evidence-based care into routine practice for common clinical presentations. Barriers and facilitators provide information essential to development of both context-appropriate EBCAs and plans for their implementation in the TASH-ED.
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 | 0,028 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».