Positioning patients to partner: exploring ways to better integrate patient involvement in the learning health systems
Bibliographic record
Abstract
Globally, health systems are increasingly striving to deliver evidence based care that improves patients', caregivers' and communities' health outcomes. To deliver this care, more systems are engaging these groups to help inform healthcare service design and delivery. Their lived experiences-experiences accessing and/or supporting someone who accesses healthcare services-are now viewed by many systems as expertise and an important part of understanding and improving care quality. Patients', caregivers' and communities' participation in health systems can range from healthcare organizational design to being members of research teams. Unfortunately, this involvement greatly varies and these groups are often sidelined to the start of research projects, with little to no role in later project stages. Additionally, some systems may forgo direct engagement, focusing solely on patient data collection and analysis. Given the benefits of active patient, caregiver and community participation in health systems on patient health outcomes, systems have begun identifying different approaches to studying and applying findings of patient, caregiver and community informed care initiatives in a rapid and consistent fashion. The learning health system (LHS) is one approach that can foster deeper and continuous engagement of these groups in health systems change. This approach embeds research into health systems, continuously learning from data and translating findings into healthcare practices in real time. Here, ongoing patient, caregiver and community involvement is considered vital for a well functioning LHS. Despite their importance, great variability exists as to what their involvement means in practice. This commentary examines the current state of patient, caregiver and community participation in the LHS. In particular, gaps in and need for resources to support their knowledge of the LHS are discussed. We conclude by recommending several factors health systems must consider in order to increase participation in their LHS. Systems must: (1) assess patients', caregivers and community understanding of how their feedback are used in the LHS and how collected data are used to inform patient care; (2) review the level and extent of these groups' participation in health system improvement activities; and (3) examine whether health systems have the workforce, capacity and infrastructure to nurture continuous and impactful 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 | 0.058 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.023 |
| Scholarly communication | 0.018 | 0.030 |
| Open science | 0.007 | 0.026 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".