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.
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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 | 0.037 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| 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".