Strategies for optimising health system managers’ engagement in quality improvement projects: lessons learnt from the COMPAS+ project
Bibliographic record
Abstract
BACKGROUND: Quality improvement strategies are used in healthcare to enhance the quality, safety and efficiency of service delivery. While the involvement of managers is considered critical, their roles remain underdocumented. This study examines the roles of managers in COMPAS+, a quality improvement collaborative conceived to enhance chronic disease care in Quebec, Canada. It explores managers' specific contributions to quality improvement projects to deepen understanding of effective managerial engagement. METHODS: This qualitative case study compares the roles played by managers (health network directors, division managers and local service network and family medicine group directors) within four regional health networks that participated in COMPAS+ from 2016 to 2019. Deductive and inductive thematic analysis of workshop reports, action plans and interviews with 24 key actors was performed, informed by a recent scoping review of decision-makers' roles in quality improvement projects and project management literature. RESULTS: The study revealed variability in project management across cases, particularly in the distribution of responsibility among upper, middle and lower management. Upper management provided strategic direction, middle management oversaw project execution and bridged organisational tiers, while lower management coordinated local change efforts. Middle managers were tasked with project management but often lacked role clarity and training. A significant gap was found in methodological guidance, typically provided by a quality improvement facilitator. This gap hindered projects' potential and, in some cases, led to deviations from the intended quality improvement model. CONCLUSIONS: Effective quality improvement project management requires well-defined managerial roles, training and communication between management levels. Our findings highlight the importance of integrating a facilitator role to provide methodological expertise and ensure adherence to quality improvement processes. Contextual expertise and local change leadership may be complemented by external quality improvement expertise. These insights lay the groundwork for future research on evidence-based strategies for effective project management.
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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 | 0.072 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".