From Planting Seeds to Driving Transformation - Patient/Family Advisors with Care Providers Co-designing Healthcare Improvement
Bibliographic record
Abstract
Background: Transforming or making a difference in healthcare quality and safety requires active involvement of those affected in delivery as well as receiving care. Co-design is an opportunity for care providers to partner with patients/families and for patients/families to be more then recipients of care. However, understanding the barriers and challenges around co-design readiness for quality and safety improvement or sustained transformation is critical for effective implementation. The objective of our work was to () bring together key stakeholders of patients/families, care providers, and leaders in quality, safety and policy to explore and map out the essential components of co-design as related to quality and safety improvements in care; (2) co-develop a co-design approach that could be implemented in different care settings; and (3) evaluate the outcomes of the co-design process as well as resulting improvements in patient experiences with the quality and safety of their care. Approach: Different care settings across Alberta Health Services were approached (i.e. acute, home care, continuing care, long term care and others). Sites in agreement to apply co-design recruited staff, care providers, leaders, and patient/family advisors (PFAs) to be actively involved as care setting teams throughout the process. Each care setting team participated in identifying what they needed to understand the reality of quality and safety in their settings (e.g. existing patient safety and care experience data; safety issues, etc.), and what they felt they needed to implement their co-designed plans and activities with the intent of improving or transforming their quality and safety outcomes. Developmental evaluation was embedded throughout the co-design process to gather the experiences and perceptions of all stakeholders involved. Real-time patient/family experiences were gathered by PFAs via interviews/surveys pre and post quality/safety improvement initiatives for each care setting. Quality and safety metrics were reviewed at team meetings to inform any further actions needed. Results: The co-design approach was implemented and evaluated with 22 care settings, involving 69 staff/care providers and 7 patient/family advisors. As the co-design process unfolded in each care setting there was confirmation of what an appropriate co-design approach was. Experience data gathered from care setting team members determined that an orientation and four-phased co-design implementation approach for quality and safety improvement worked best. Experiences overall were positive and affirming for the co-design approach and outcomes achieved. Themes emerged for each of the four phases of the co-designed work - for example, "having clear direction for work planned/proposed" and "making a difference". Real-time patient/family experiences gathered also indicated improvements in various quality and safety areas - e.g. reduction in noise levels enhanced patients' much needed rest/sleep and reduced stress levels. Implication: Care setting teams learned to co-design, experienced the benefits of co-designing quality and safety improvement for their care settings, and found it feasible to sustain. They felt they made a difference in the overall experiences of patients and families. The resulting Kovacs Burns Geoge co-design orientation 0 guide for healthcare quality and safety improvement has been published and is available for teams to explore and apply.
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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 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".