NAVIGATING FAILURE: HOW LEADERS DEFINE, DETECT, AND MANAGE FAILURE IN HEALTHCARE QUALITY IMPROVEMENT
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".