Promoting Patient and Family Partnerships in Ambulatory Care Improvement: A Narrative Review and Focus Group Findings
Bibliographic record
Abstract
INTRODUCTION: Ambulatory practices that actively partner with patients and families in quality improvement (QI) report benefits such as better patient/family interactions with physicians and staff, and patient empowerment. However, creating effective patient/family partnerships for ambulatory care improvement is not yet routine. The objective of this paper is to provide practices with concrete evidence about meaningfully involving patients and families in QI activities. METHODS: Review of literature published from 2000-2015 and a focus group conducted in 2014 with practice advisors. RESULTS: Thirty articles discussed 26 studies or examples of patient/family partnerships in ambulatory care QI. Patient and family partnership mechanisms included QI committees and advisory councils. Facilitators included process transparency, mechanisms for acting on patient/family input, and compensation. Challenges for practices included uncertainty about how best to involve patients and families in QI. Several studies found that patient/family partnership was a catalyst for improvement and reported that partnerships resulted in process improvements. Focus group results were concordant. CONCLUSION: This paper describes emergent mechanisms and processes that ambulatory care practices use to partner with patients and families in QI including outcomes, facilitators, and challenges. FUNDING: Gordon and Betty Moore Foundation.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".