Challenges and promising solutions to engaging patients in healthcare implementation in the United States: an environmental scan
Bibliographic record
Abstract
Abstract Background One practice in healthcare implementation is patient engagement in quality improvement and systems redesign. Implementers in healthcare systems include clinical leadership, middle managers, quality improvement personnel, and others facilitating changes or adoption of new interventions. Patients provide input into different aspects of health research. However, there is little attention to involve patients in implementing interventions, especially in the United States (U.S.), and this might be essential to reduce inequities. Implementers need clear strategies to overcome challenges, and might be able to learn from countries outside the U.S. Methods We wanted to understand existing work about how patients are being included in implementation activities in real world U.S. healthcare settings. We conducted an environmental scan of three data sources: webinars, published articles, and interviews with implementers who engaged patients in implementation activities in U.S. healthcare settings. We extracted, categorized, and triangulated from data sources the key activities, recurring challenges, and promising solutions using a coding template. Results We found 27 examples of patient engagement in U.S. healthcare implementation across four webinars, 11 published articles, and seven interviews, mostly arranging patient engagement through groups and arranging processes for patients that changed how engaged they were able to be. Participants rarely specified if they were engaging a population experiencing healthcare inequities. Participants described eight recurring challenges; the two most frequently identified were: (1) recruiting patients representative of those served in the healthcare system; and (2) ensuring processes for equitable communication among all. We matched recurring challenges to promising solutions, such as logistic solutions on how to arrange meetings to enhance engagement or training in inclusivity and power-sharing. Conclusion We clarified how some U.S. implementers are engaging patients in healthcare implementation activities using less and more intensive engagement. It was unclear whether reducing inequities was a goal. Patient engagement in redesigning U.S. healthcare service delivery appears similar to or less intense than in countries with more robust infrastructure for this, such as Canada and the United Kingdom. Challenges were common across jurisdictions, including retaining patients in the design/delivery of implementation activities. Implementers in any region can learn from those in other places.
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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) | 0.000 | 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) | 0.002 | 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".