Experience-based co-design: A method for patient and family engagement in system-level quality improvement
Bibliographic record
Abstract
Integrating patient and family member needs, wants and preferences in healthcare is of utmost importance. However, a standardized patient and family engagement model to understand these needs, wants and preferences in order to translate into high quality improvement activities is lacking. Experience based co-design (EBCD) is an approach that enables patients, family members and healthcare providers to co-design improvement initiatives together. In this study, EBCD was employed to: 1) assess the current state of information and educational resources at a local oncology center and 2) partner with patients, family members, and healthcare providers to create quality improvement initiatives targeting identified issues. Three focus groups were conducted: 1) patient and family member-specific, 2) healthcare provider-specific, and 3) all participants (including patients, family members and healthcare providers). Discussion questions were focused around current educational resources, barriers encountered throughout the cancer continuum, and recommendations for improvement. Six themes emerged from the two initial focus groups with patients and family members and healthcare providers: 1) patient-provider communication, 2) accessing information, 3) tailored information, 4) side effect information, 5) caregiver information, and 6) partners in care. Themes were presented to participants to ensure findings accurately depicted their experience and five quality improvement projects were created, aligning with the themes. This study provides an example of how EBCD helped to foster a safe environment, where patients, family members, and healthcare providers worked together in order to improve educational resources.
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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 | 0.066 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".