Enhancing Person-Centred Practice in Primary Care: Co-designing Strategies to Implement Quality Indicators Informed by the Patient Perspective
Bibliographic record
Abstract
Person-Centred Care (PCC) promotes engagement with patients and caregivers to improve patient experiences and outcomes. Person-Centred Quality Indicators (PC-QIs) are quality improvement (QI) tools that support healthcare providers and organizations to identify gaps in the delivery of PCC and improve the quality of care for patients. Little is known about how to implement PC-QIs effectively so that they are adopted and used to create meaningful change in practice. We aimed to address these knowledge gaps by applying a person-centred and implementation science lens to QI and collaborate with stakeholders to inform the future implementation of PC-QIs for primary care in Alberta, Canada. The first study of this thesis was a systematic review and meta-analysis on the effectiveness of person-centred QI strategies on the management of hypertension in primary care. Our findings suggest that consistent features of person-centered QI interventions that were effective for improving hypertension outcomes included tailored communication with patients, use of health information technology, and multidisciplinary collaboration. The second and third studies of this thesis were mixed methods studies focussed on co-designing the implementation of the PC-QIs for future use in primary care in Alberta. In study two, our findings from an organizational readiness survey and interviews conducted with participants representing both Canadian system-level and Alberta clinical primary care perspectives shed light on key factors that may influence implementation. These factors included: the organization or clinics’ interest and motivation to implement the PC-QIs, resources and capacity to collect and use data for improvements, and the organizational climate for implementation of the PC-QIs, related to PCC and QI. In the third study, we conducted a consensus process with primary care stakeholders to prioritize the PC-QIs for implementation and implementation strategies that were identified, which emerged from our second study. Consensus panelists prioritized PC-QIs related to: patient and caregiver involvement in decisions about care, having a trusting relationship with the healthcare provider, health information technology to support PCC, co-designing care in partnership with communities, and overall experience. The strategies prioritized included: developing partnerships among stakeholder groups, obtaining QI resources, conducting a needs assessment, aligning measurement efforts provincially, and engaging champions.
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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 | 0.100 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".