Value-creating Learning Health Systems – an Organizing Framework for the Journey to Integrated Care
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,024 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,012 | 0,055 |
| Communication savante | 0,021 | 0,013 |
| Science ouverte | 0,004 | 0,013 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».