Control rooms in publicly-funded health systems: Reviving value in healthcare governance
Bibliographic record
Abstract
BACKGROUND: As part of reforms in 2015, the Ministry of Health and Social Services in Quebec, Canada mandated the national implementation of control rooms, making health system actors accountable for implementing value-based performance management. OBJECTIVE: To explore how do organizational actors appropriate control rooms as managerial tools to influence value-based performance in health systems. DESIGN: Multi-site organizational ethnographic case studies (N = 2) and narrative process analysis of triangulated qualitative data collected through non-participatory observations (179.5 h), individual semi-structured interviews (N = 34), and document review (N = 143). RESULTS: The process of appropriating control rooms plays a crucial role in achieving value-based performance management. Appropriating unfolds along three paths (cognitive, structural, technical) over three phases (implementing, testing, adapting). Implementing control rooms both produces and emerges from improvement capacities within healthcare organizations. Testing tools reveals that incompatibilities between tools, structures and values give rise to value-driven distributed clinical leadership. Adapting tools relies on the adaptability of organizations towards the value system driving the tools, rather than on the adaptability of tools to organizational design. CONCLUSION: There is no "one-size-fits-all" framework to design and support the successful appropriation of control rooms towards achieving value-based performance. However, we believe that consideration for the three distinct phases of appropriation and leveraging the right mechanism to support each phase is a first important step in reviving value in healthcare governance.
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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 | 0.121 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.036 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| 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".