Learning Care Pathways Framework: A New Method to Implement, Learn, Replicate, and Scale up Care Pathways for and With the Patient
Bibliographic record
Abstract
BACKGROUND: Although care pathways are a response to the calls for a major change in health system redesign initiatives, very few articles have proposed an implementation method. Indeed, no method exists for large-scale projects of care pathways, as sets of interventions within health systems. Drawing on the systems thinking approach and the pragmatic sociology, we describe the implementation methodology of the Learning Care Pathways (LCP) framework, a method to implement, learn, replicate, and scale up care pathways for and with the patient. METHODS: The LCP was conceptually developed through a series of literature reviews on key methodological concepts. As a comprehensive, theory-informed approach, the LCP emerged by linking implementation strategies, research methods, learning mechanisms and outcomes dimensions aimed at optimising care pathways. RESULTS: Designed around 13 steps grouped into five phases, this framework provides implementation strategies, research methods and learning mechanisms, including levers for patient involvement. The pre-implementation phase enables the selection of the pilot project's receiving environment and the design of the project. The implementation phase is designed to co-construct and implement an optimised care pathway based on a scientific analysis of the patient journey, the care pathway perceived by professionals, the care pathway from data and integrating knowledge from international clinical practice guidelines. The post implementation phase aims to demonstrate value creation and set up a learning cycle. The replication phase is designed to repeat the method locally to develop horizontal learning and to evaluate scalability. Finally, the scale up phase aims to repeat the method in other territories to accelerate knowledge creation and develop horizontal and vertical learning. CONCLUSION: This framework is of particular interest to policy-makers, healthcare managers, and researchers alike, and must be the subject of several experiments to conduct reproducible research that can lead to national Learning Health Systems (LHS).
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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 | 0.093 | 0.105 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".