Patient and Family Involvement: A Discussion of Co-Led Redesign of Healthcare Services
Bibliographic record
Abstract
The involvement of patients and their families in the redesign of healthcare services is an important option in providing a service that addresses the patients' needs and improves health outcomes. However, it is a resource-intensive approach, and it is currently not clear when it should be used, and what should be the reasoning behind this decision. Some health systems of international standing have created a patient engagement program as a selling point. This paper discusses how co-led redesign can be beneficial in improving health service and more effectively engaging patients. Potential barriers for patient involvement are discussed. Patient involvement can be integrated into the health system at three main levels of engagement: direct care, organizational design and governance, and policy-making. The aim of this paper is to describe how co-led redesign is compatible with different levels of patient involvement and to address the challenges in delivering a co-led redesign in healthcare. Co-led redesign not only involves the collection of quantitative data for assessing the current systems but also the collection of qualitative data through patient, family, and staff interviews to determine the barriers to patient satisfaction. Co-led redesign is a resource-rich process that requires expertise in data collection and a clinical group that is devoted to implementing recommended changes. Currently, a number of countries have utilized co-led redesign for many different types of healthcare services. Resource availability and cost, process time, and lack of outcome measures are three major limiting factors.
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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 | 0.105 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.045 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 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".