Involving patients in setting priorities for healthcare improvement: a cluster randomized trial
Bibliographic record
Abstract
BACKGROUND: Patients are increasingly seen as active partners in healthcare. While patient involvement in individual clinical decisions has been extensively studied, no trial has assessed how patients can effectively be involved in collective healthcare decisions affecting the population. The goal of this study was to test the impact of involving patients in setting healthcare improvement priorities for chronic care at the community level. DESIGN: Cluster randomized controlled trial. Local communities were randomized in intervention (priority setting with patient involvement) and control sites (no patient involvement). SETTING: Communities in a canadian region were required to set priorities for improving chronic disease management in primary care, from a list of 37 validated quality indicators. INTERVENTION: Patients were consulted in writing, before participating in face-to-face deliberation with professionals. CONTROL: Professionals established priorities among themselves, without patient involvement. PARTICIPANTS: A total of 172 individuals from six communities participated in the study, including 83 chronic disease patients, and 89 health professionals. OUTCOMES: The primary outcome was the level of agreement between patients' and professionals' priorities. Secondary outcomes included professionals' intention to use the selected quality indicators, and the costs of patient involvement. RESULTS: Priorities established with patients were more aligned with core generic components of the Medical Home and Chronic Care Model, including: access to primary care, self-care support, patient participation in clinical decisions, and partnership with community organizations (p < 0.01). Priorities established by professionals alone placed more emphasis on the technical quality of single disease management. The involvement intervention fostered mutual influence between patients and professionals, which resulted in a 41% increase in agreement on common priorities (95%CI: +12% to +58%, p < 0.01). Professionals' intention to use the selected quality indicators was similar in intervention and control sites. Patient involvement increased the costs of the prioritization process by 17%, and required 10% more time to reach consensus on common priorities. CONCLUSIONS: Patient involvement can change priorities driving healthcare improvement at the population level. Future research should test the generalizability of these findings to other contexts, and assess its impact on patient care. TRIAL REGISTRATION: The Netherlands National Trial Register #NTR2496.
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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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".