NAVIGATING FAILURE: HOW LEADERS DEFINE, DETECT, AND MANAGE FAILURE IN HEALTHCARE QUALITY IMPROVEMENT
Notice bibliographique
Résumé
Background: Although Quality Improvement (QI) initiatives are widely implemented in healthcare, evidence suggests that failure is common. However, the concept of ‘QI failure’ remains underdefined in the literature, with few studies offering explicit definitions or frameworks to understand it. While existing research emphasizes the role of leadership in QI, it seldom explores how leaders recognize and respond to failure. Without a clear understanding of how healthcare leaders navigate QI failure, it is challenging to develop conceptual insights or offer practical, systematic approaches for identifying, managing, and preventing failure in QI efforts. Study Aim: The aim of this dissertation was to investigate how healthcare leaders conceptualize, detect, and respond to QI failure. Methods: A qualitative descriptive study, grounded in a constructivist paradigm, was conducted at a hospital system in Ontario, Canada. Thirty-three formal leaders representing various hierarchical levels participated in semi-structured interviews. Participants were purposively selected based on their involvement in completed QI initiatives that were either abandoned or substantially redesigned. Data were analyzed inductively using NVivo software to identify thematic patterns and conceptual categories in leaders’ accounts of QI failure. Results: A conceptual framework was developed of the QI failure process as experienced by healthcare leaders. The framework includes key antecedents to QI failure, strategies for detecting and managing QI failure, the outcomes of QI failure, and the individual and organizational factors that seemed to influence leaders’ experiences of QI failure. The results revealed that QI failures had a strong emotional toll on those involved, especially in the absence of psychological safety and structural institutional supports. Conclusion: This study reframes QI failure as a relational and institutional phenomenon, not just a technical or procedural one. Key contributions include an explicit definition and novel conceptual framework of QI failure in healthcare to guide future practice and research. In practice, healthcare organizations should implement a centralized digital repository for reporting, tracking and sharing QI failures to support transparency, accountability and collective learning. Additional recommendations include enhancing access to expert guidance and cultivating a psychologically safe, no-blame environment in which QI failure is openly discussed and used as a driver for improvement. Future research should investigate the identified leadership strategies and influencing factors across diverse settings and over time to better understand their underlying mechanisms.
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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 | 0,046 | 0,079 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,010 | 0,015 |
| Communication savante | 0,010 | 0,010 |
| Science ouverte | 0,002 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,005 |
| 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 ».