Measuring the Impact of Patient Engagement From the Perspective of Health Professionals Leading Quality Improvement Projects
Bibliographic record
Abstract
INTRODUCTION: The value of engaging patients and families in health care quality improvement (QI) initiatives is to help align health care system efforts with patient priorities. Meaningful evaluation of engaging with patients and families within QI may promote future collaboration. The aim of this study was to identify the experiential impact of patient engagement from the perspective of health professionals who were leading health care QI projects. METHODS: Point-of-care health professionals who completed a fellowship capacity building program between 2014 and 2018 that provided an opportunity to learn about patient engagement concepts and to engage patients, families, and caregivers in their QI projects were invited to participate in the study. The Most Significant Change technique was used as a participatory approach to obtain qualitative evaluative data from semistructured interviews with health professional fellows. Significant change stories were curated from self-narratives grounded in the experiences of health professional fellows. RESULTS: The stories demonstrated that gaining new knowledge on concepts related to patient engagement as part of a structured curriculum is effective in both supporting engagement in practice and cultivating the importance of patient engagement among health professionals. The early and ongoing involvement of patients was a key factor in shaping the project while fostering a patient-centered focus. Seeking out the patient voice throughout the QI project led to improvements in patient care experiences. DISCUSSION: The findings of this study can inform programs seeking to promote patient engagement in health care QI. The positive changes that stem from aligning capacity building programs with patient-oriented priorities support the vision that patient engagement should be at the foundation of health care QI.
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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 | 0.027 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".