The role of health care organizations in patient engagement: Mechanisms to support a strong relationship between patients and clinicians
Bibliographic record
Abstract
BACKGROUND: Patient engagement (PE) is critical to improving patient experience and outcomes, as well as clinician work life and lowering health care costs, yet health care organizations (HCOs) have limited guidance about how to support PE. The engagement capacity framework considers the context of engagement and examines precursors to engagement, including patients' self-efficacy, resources, willingness, and capabilities. PURPOSE: The aim of this study was to explore clinician and patient perspectives related to mechanisms through with the HCOs can facilitate PE through the lens of the engagement capacity framework. METHODOLOGY/APPROACH: We administered an online open-ended survey to clinicians and patient advisors across the United States, including questions focused on the influences of, barriers to, and skills and tools required for PE. A common theme emerged focusing on the role of HCOs in facilitating engagement. Our analysis examined all responses tagged with the "health care system" code. RESULTS: Over 750 clinicians and patient advisors responded to our survey. Respondents identified offering advice and support for patients to manage their care (self-efficacy), providing tools to facilitate communication (resources), working to encourage connection with patients (willingness), and training for HCO employees in cultural competency and communication skills (capabilities) as important functions of HCOs related to engagement. CONCLUSION: HCOs play an important role in supporting a strong partnership between the patient and clinicians. Our study identifies important mechanisms through which HCOs can fulfill this role. PRACTICE IMPLICATIONS: HCO leadership and administration can help establish the culture of care provided. Policies and initiatives that provide appropriate communication tools and promote culturally competent care can increase 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 | 0.042 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 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".