Approaches to optimize patient and family engagement in hospital planning and improvement: Qualitative interviews
Bibliographic record
Abstract
BACKGROUND: Patient engagement (PE) in health-care planning and improvement is a growing practice. We lack evidence-based guidance for PE, particularly in hospital settings. This study explored how to optimize PE in hospitals. METHODS: This study was based on qualitative interviews with individuals in various roles at hospitals with high PE capacity. We asked how patients were engaged, rationale for approaches chosen and solutions for key challenges. We identified themes using content analysis. RESULTS: Participants included 40 patient/family advisors, PE managers, clinicians and executives from 9 hospitals (2 < 100 beds, 4 100 + beds, 3 teaching). Hospitals most frequently employed collaboration (standing committees, project teams), followed by blended approaches (collaboration + consultation), and then consultation (surveys, interviews). Those using collaboration emphasized integrating perspectives into decisions; those using consultation emphasized capturing diverse perspectives. Strategies to support engagement included engaging diverse patients, prioritizing what benefits many, matching patients to projects, training patients and health-care workers, involving a critical volume of patients, requiring at least one patient for quorum, asking involved patients to review outputs, linking PE with the Board of Directors and championing PE by managers, staff and committee/team chairs. CONCLUSION: This research generated insight on concrete approaches and strategies that hospitals can use to optimize PE for planning and improvement. On-going research is needed to understand how to recruit diverse patients and best balance blended consultation/collaboration approaches. PATIENT OR PUBLIC CONTRIBUTION: Three patient research partners with hospital PE experience informed study objectives and interview questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".