Partnering with patients in quality improvement: towards renewed practices for healthcare organization managers?
Bibliographic record
Abstract
Abstract Background Around the world, many healthcare organizations engage patients as a quality improvement strategy. In Canada, the University of Montreal developed a model which consists in partnering with patient advisors, providers, and managers in quality improvement. This model was introduced through its Partners in Care Programs experimented in several quality improvement teams in Quebec, Canada. Partnering with patients in quality improvement brings about new challenges for healthcare managers. Although this model is recent, little is known about how managers contribute to implementing and sustaining it using key practices. Methods In-depth multi-level case studies were conducted within two healthcare organizations which have implemented a Partners in Care Program in quality improvement. The longitudinal design of this research enabled us to monitor the implementation of patient partnership initiatives from 2015 to 2017. In total, 38 interviews were carried out with managers of different levels (top-level, mid-level, and front-line) involved in the implementation of Partners in Care Program. Additionally, seven focus groups were conducted with patients and providers. Results Our findings show that healthcare managers are engaged in four main types of practices: 1-designing the patient partnership approach for it to make sense to the entire organization; 2-structuring patient partnership to support its implementation and sustainability; 3-managing patient advisor integration in quality improvement to avoid tokenistic participation; 4-evaluating patient advisor integration to support continuous improvement. Designing and structuring patient partnership are based on traditional management practices usually used to implement quality improvement initiatives in healthcare organizations, whereas managing and evaluating patient advisor’ integration require new daily practices from managers. Our results revealed that managers of all levels, from top to front-line, are concerned with the implementation of patient partnership in quality improvement. Conclusion This research adds empirical support to the lack of evidence on daily managerial practices used for implementing patient partnership initiatives in quality improvement and contributes to guiding healthcare organizations and managers when integrating such approaches.
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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 | 0.060 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".