The role of patients, caregivers, and communities in Learning Health Systems: a narrative review
Bibliographic record
Abstract
Learning Health Systems (LHSs) seek to continuously generate and apply evidence in clinical practice. Most Canadian LHS models emphasize engagement with patients, caregivers, and communities (herein contributors). Yet, there is limited guidance about how engagement works in these dynamic systems and how it may differ from engagement in other settings, for example patient-oriented research and quality improvement. This review examines engagement activities in existing and emerging LHSs for insights into the roles that contributors play in creating patient-oriented and equitable LHSs. A narrative review was conducted using the PerSPEcTiF framework. Search terms were identified for three domains: contributors, LHSs operating in direct patient care settings, and active engagement. Four databases (PubMed-MEDLINE, CINAHL, PsycINFO, Embase) were searched in December 2022. Articles were screened using a domain-based rubric and sampled for richness. Data were extracted including who was engaged, when, where, and how. Engagement activities were coded inductively, then deductively using the International Association for Public Participation (IAP2) Spectrum of Public Participation. An advisory group, including a Patient Partner, provided input at several stages in the project. Thirty-six articles describing engagement in 30 LHSs were included. In all, 192 engagement activities were coded to create a taxonomy of engagement; 139 activities were also coded to the IAP2 Spectrum. Contributors' influence over decision-making was often unclear or limited, with engagement frequently occurring after LHS implementation. However, LHSs also provided contributors with opportunities to engage in deliberative system design and effect change through distributed leadership. Ten contributor roles were synthesized, serving three functions: "System Shapers" (designing and defining LHSs), "Community and Capacity Builders" (expanding and supporting LHSs), and "Implementers" (hands-on efforts). This review provides an overview of engagement in LHSs, demonstrating how these practices can both build on and be constrained by engagement traditions in patient-oriented research and quality improvement. Findings offer a starting point for designing meaningful contributor roles and highlight opportunities to reimagine engagement practices by embedding contributors within systems and engaging communities beyond patient care settings.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".