Effective implementation strategies and drivers of culture change for improving time to treatment for severe maternal hypertension
Notice bibliographique
Résumé
Objective: To assess effective implementation strategies to reduce time to treatment for severe maternal hypertension and drivers of culture change among high-performing sites in a statewide quality improvement (QI) initiative. Methods: Using a mixed-methods sequential explanatory design, mixed effect linear regression models with a logit link were used to assess the association between achievement of system changes at each hospital and the proportion of cases in which time to treatment was achieved, including fixed effects for system changes and time (quarter of implementation year) and random effects for hospital and quarter within hospital (random slope). All models were adjusted for birth volume, location (urban/rural), and patient population demographics, and a sensitivity analysis was performed for multiple comparisons. Then, key informant interviews of 11 high-performing hospital teams explored implementation strategies driving system and clinical culture change. Results: Among 108 participating hospitals, 79 submitted quarterly survey data on progress toward implementing system changes. Our quantitative analysis demonstrated that several individual system changes were initially associated with a reduction in time to treatment for maternal hypertension, but these associations were not significant after adjusting for multiple comparisons. Through qualitative interviews, we learned that high-performing sites enacted the system changes which showed initial promise in our quantitative analysis by reducing burden for their QI teams by utilizing existing QI support, educating clinical teams and patients to empower them as agents of behavior change, promoting clinician engagement using multi-level strategies, optimizing workflow and infrastructure, and fostering innovation based on other teams' experiences. Key drivers of clinical culture change included hospital environments that emphasized communication at the patients' bedside around QI priorities, valued interprofessional relationships and communication, promoted shared values around providing high-quality maternal care and protecting maternal safety, and harnessed external support from professional societies and leadership. Conclusion: This mixed-methods analysis identifies key implementation strategies that perinatal quality collaboratives and individual hospitals can utilize to sustain behavior change to reduce time to treatment for severe maternal hypertension. While we were unable to definitively identify singular system changes that reduced time to treatment, our qualitative data suggest that a combination of these changes may change clinical culture and lead to improved outcomes. Future work to assess the impact of selected system changes as well as clinical culture change on obstetric QI efforts is needed.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,045 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».