Engaging community to co-design learning health systems: lessons from storytelling and Design Jam, a community case study from British Columbia, Canada
Bibliographic record
Abstract
Health and research systems produce vast amounts of data, yet only a fraction is used to improve healthcare delivery-especially for equity-deserving communities. In Canada, Learning Health Systems (LHS) are guided by the Quadruple Aim: improving population health, enhancing patient and provider experience, and reducing costs, with equity now recognized as a critical additional aim. As LHS evolve, advancing health equity has become a core driver, particularly in Canada. An equitable LHS prioritizes inclusion, accessibility, and co-creation, ensuring that historically marginalized communities are active partners in shaping healthcare solutions. Community engagement is foundational to LHS, where individuals, families, and communities collaborate with clinicians, researchers, and decision-makers to drive meaningful improvements. This community case study describes how a large health authority in British Columbia integrated design thinking and a participatory action research approach to co-develop a vision for a community-centered LHS. Fifty diverse partners participated, including individuals and families, clinicians, non-clinical health staff, health administrators, researchers, and students. The project team drew on a Canadian LHS framework, appreciative inquiry, and design thinking to guide engagement activities. Participants co-designed a vision for LHS, proposing actions across six key areas, including (1) Legal and Ethical, (2) Science and Research, (3) Data and Technology, (4) Policy, Process, and Resources, (5) Indigenous Leadership & Participation, (6) Social, Community, and Equity. Through the sessions, lived experiences helped surface barriers and community priorities. Storytelling and Design Jam methods were key tools for fostering meaningful engagement. We propose practical considerations (INSPIRE) that researchers and policymakers can apply to enhance participation, foster equity, and ensure that Learning Health Systems remain community-driven and responsive to diverse needs: Inclusion first, Nurture Trust, Show impact, Partner with lived experience experts, Institutionalize diverse engagement, Recognize ethical responsibilities, and Ensure sustainability. Future research should investigate how to overcome barriers to participation, embed participatory approaches, and consider design-thinking in health system transformation. By focusing on community engagement, this case study demonstrates how LHS can be co-developed as inclusive and equity-driven.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.015 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".