Value-creating Learning Health Systems – an Organizing Framework for the Journey to Integrated Care
Bibliographic record
Abstract
BACKGROUND AND CONTEXT: The province of Ontario in Canada, like many other jurisdictions globally, has identified a vision for healthcare that involves “a modern, sustainable and integrated health care system that is centred on the patient.” In addition to person-centred integration, efficiency and alignment of data, services and financial incentives, innovation, and capacity development were identified as key areas of focus. Ontario Health Teams (a group of healthcare providers and organizations that are responsible, both clinically and fiscally, for delivering a fully coordinated continuum of care to a defined geographic population) were developed as a primary vehicle to deliver this focus. This vision and mandate require significant health system transformation, at a time when our health care system is facing significant challenges: increasing acuity and complexity of health and social needs, a fatigued and stretched workforce, and a fiscally constrained environment. Value-creating learning health systems (LHS) are increasingly being looked to as an organizing framework for driving improvements in population health management, accelerating learning and improvement, breaking down silos and facilitating interoperability, and supporting data-driven insights and improvement. WORKSHOP OUTLINE (90 Minutes) 1) Introduction to Value-creating Learning Health Systems (10 minutes) The concept of a learning health system (LHS) was first introduced by the Institute of Medicine in 2007 as a system where “science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience”. While there have been many iterations since then, learning health systems share common elements: a clear articulation of the desired outcomes (often articulated as a health equity-driven quadruple aim), incorporation of a learning health cycle (which includes data to knowledge, knowledge to practice, and practice to data), ecosystems of change (which identify level and scale), supporting pillars (e.g., relationships, technology, policy), and shared core values (Menear et al, 2019). 2) Use of the LHS framework (20 minutes) Participants will be briefly introduced to 3 examples of application of the LHS framework that vary in scale and scope: -Refresh of a Regional Geriatric Program -Cross-OHT collaboration on enablers for integrated care -Establishing a provincial coalition for the development of population health management enablers Participants will be asked to reflect on these use cases, and to compare and contrast implementation across different contexts and settings. 3) Application of the LHS framework (40 minutes) Working in small groups, participants will be asked to consider a use case from their own jurisdiction and using a series of generative questions related to the LHS framework, identify current and future initiatives aligned with the LHS components, that would further their journey towards integrated care. 5) Report back and Summary of Lessons Learned (20 minutes)
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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 | 0.024 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.055 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".