The voice of patients in system redesign: A case study of redesigning a centralized system for intake of referrals from primary care to rheumatologists for patients with suspected rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: The published literature demands examples of health-care systems designed with the active engagement of patients to explore the application of this complex phenomenon in practice. METHODS: This case study explored how the voice of patients was incorporated into the process of redesigning an element of the health-care system, a centralized system for intake of referrals from primary care to rheumatologists for patients with suspected rheumatoid arthritis (RA)-centralized intake. The phenomenon of patient engagement using "patient and community engagement researchers" (PaCERs) in research and the process of redesigning centralized intake were selected as the case. In-depth evaluation of the case was undertaken through the triangulation of findings from the document review and participants' reflection on the case. RESULTS: In this case, patients and PaCERs participated in multiple activities including an initial meeting of key stakeholders to develop the project vision; a patient-to-patient PaCERs study to gather perspectives of patients with RA on the challenges they face in accessing and navigating the health-care system, and what they see as key elements of an effective system that would be responsive to their needs; the development of an evaluation framework for future centralized intake; and the choice of candidate centralized intake strategies to be evaluated. CONCLUSIONS: The described feasible multistep approach to active patient engagement in health-care system redesign contributes to an understanding of the application of this complex phenomenon in practice. Therefore, the manuscript serves as one more step towards a patient-centred health-care system that is redesigned with active patient engagement.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".