Facilitating delivery of goal-oriented care through the collection, presentation, and use of meaningful data
Notice bibliographique
Résumé
Background: Use of routinely collected health and social care data has potential to drive population health improvements. Many organizations are data rich but information poor; collecting large amounts of data but having limited ability to transform data into actionable insights. Learning health systems aim to produce these types of insights not through stand-alone research, but as by-products of care delivery, using the care environment as a living lab to generate and apply new knowledge to improve both care delivery and population health. To effectively leverage data to support evidence-informed decision making, point-of-care clinicians and organizational leaders require access to data that is both reliable and meaningful, in a format that supports its real-time use. Audience: SE Health is a large Canadian not-for-profit social enterprise delivering care across the continuum. Embedded within SE Health is the SE Research Centre, a consortium of applied and impact-oriented health services researchers, who together with experts-by-experience work to develop, test, implement, evaluate, scale, and spread evidence to support transformative health system change. We invite anyone interested in this type of learning health system environment providers, decision-makers, researchers, patients, and families to come together in this workshop to learn about how we can use data to facilitate the delivery of goal-oriented care. Approach: Grounded in case-based learning methodology and using an example of facilitating goal-oriented care delivery, we will share learnings from our journey towards becoming a community-based learning health system. Leveraging a mix of presentation, applied activities and small group discussions, attendees will be guided through the three steps of the learning health system cycle - data-to-knowledge, knowledge-to-practice, and practice-to-data - using resources and tools created by the SE Research Centre to facilitate the delivery of goal-oriented, integrated care. First, a brief (~0 min) introductory presentation will provide important background on goal-oriented care and the development, testing and implementation of data collection instruments (e.g., Client Experience Survey for Integrated-Home and Community Care) and other resources (e.g., Holistic Health Needs Report) featured in the session. Following this, an evidence-informed client case study will be presented (~0 min), serving as the foundation for hands-on activities throughout the workshop. Two small group activities will be facilitated, each with 20 minutes for engagement in the activity and 0 minutes for sharing small group insights with all workshop attendees. Activity focuses on data use at the micro- or practice level, with participants leveraging the Holistic Health Needs Report to engage in goal-oriented care planning. In Activity 2, participants will use data at the meso- or organizational level to plan data-informed workforce development initiatives based on unit-level reports of patient experience and population health needs. A final presentation (~0m) will focus on moving knowledge into practice, summarizing results of a recent scoping review of best practices in case-based learning and providing examples of how person-level data can support both micro and meso-level education and training initiatives. Outcomes: After attending this workshop, participants will be able to ) describe key considerations for the collection, presentation, and use of data at the micro- and meso- level, 2) explain how to apply summaries of point-of-care data to support holistic, goal-oriented collaborative care planning through case-based learning and 3) identify how data from point-of care activities can generate meaningful insights to support operational management and workforce development. Take home messages will be summarized by workshop facilitators using the three steps of the learning health system cycle as a framework. Participants will have an opportunity to take away research summaries, workshop handouts and copies of the materials presented for further sharing and reflection in their own practice contexts and organizations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,128 | 0,156 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,010 | 0,015 |
| Communication savante | 0,018 | 0,014 |
| Science ouverte | 0,006 | 0,026 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,003 |
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 ».