Patient engagement in hospital health service planning and improvement: a scoping review
Bibliographic record
Abstract
OBJECTIVES: Patient engagement (PE) improves patient, organisation and health system outcomes, but most research is based on primary care. The primary purpose of this study was to describe the characteristics of published empirical research that evaluated PE in hospital health service improvement. DESIGN: Scoping review. METHODS: Five databases were searched from 2006 to September 2016. English language studies that evaluated patient or provider beliefs, participation in PE, influencing factors or impact were eligible. Screening and data extraction were done in triplicate. PE characteristics, influencing factors and impact were extracted and summarised. RESULTS: From a total of 3939 search results, 227 studies emerged as potentially relevant; of these, 217 were not eligible, and 10 studies were included in the review. None evaluated behavioural interventions to promote or support PE. While most studies examined involvement in standing committees or projects, patient input and influence on decisions were minimal. Lack of skill and negative beliefs among providers were PE barriers. PE facilitators included careful selection and joint training of patients and providers, formalising patient roles, informal interaction to build trust, involving patients early in projects, small team size, frequent meetings, active solicitation of patient input in meetings and debriefing after meetings. Asking patients to provide insight into problems rather than solutions and deploying provider champions may enhance patient influence on hospital services. CONCLUSIONS: Given the important role of PE in improving hospital services and the paucity of research on this topic, future research should develop and evaluate behavioural interventions for PE directed at patients and providers informed by the PE barriers and facilitators identified here. Future studies should also assess the impact on various individual and organisational outcomes.
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 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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".