Toward a Person-Centred Learning Health System: Understanding Value from the Perspectives of Patients and Caregivers
Bibliographic record
Abstract
What matters most to people who use healthcare? What matters to their caregivers? How do we use this information to support ongoing quality improvement in the healthcare system? In this paper, we explore three concepts from the current healthcare discourse, intended to drive health system improvements: person-centred care, value-based healthcare and learning health systems. We propose that key tenets from each of these concepts should be combined to create a person-centred learning health system (PC-LHS). We highlight two key points: First, in achieving a PC-LHS, the experiences, priorities and values of patients and their caregivers should be continually collected and fed into data systems to monitor ongoing quality improvement and performance benchmarking. Second, the information collected in determining value must include important contextual factors - including the social determinants of health - as patient health and well-being outcomes will ultimately be shaped by these factors, in addition to health system and disease factors. In summary, improving value for patients and caregivers, by capturing the things that matter most to them, within their life contexts, needs to be part of the continuous quality improvement cycle that lies at the heart of a learning health system.
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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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.004 | 0.007 |
| 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".