The Translational Work of Interoperability: Digital Health and Data Enabling Integrated Care Special Interest Group Workshop
Notice bibliographique
Résumé
Introduction: Members of the Digital Health and Data Enabling Integrated Care Special Interest Group came together at ICIC22 to discuss priority issues. One critical international struggle is the challenge of interoperability to support information and data sharing and communication as cornerstones of integrated health and social care systems. Aims and Objectives: The emphasis of SIG work in 2023-2024 will be to deeply engage with the critical challenge of interoperability. From an integrated care perspective what is required is understanding the translational work needed to make the sharing of information and data useful and meaningful to those who need it. In this meeting, delegates will work together to unpack the translation problem of interoperability in terms of translating information between stakeholders (e.g. between patients, families and their care teams), between disciplines (e.g. between different clinicians and providers), and between sectors (e.g. between health and social care sectors), with the aim to share current work and identify knowledge and practice gaps that we can address as a SIG for the next year. Audience: All existing and newly interested members of the SIG are welcome to join for this discussion. Our membership consists of patients and family caregivers, researchers, frontline providers, managers, system leaders and decision-makers, policy makers, informaticians, and industry partners. Structure and engagement: This session will use the hour largely to engage with delegates to meet session objectives. To set up the discussion we will begin with a short introduction from SIG leads (C. Steele Gray, L. Lewis, I. Meyer) followed by SIG member (J. Piera-Jiménez) to present the case example of Catalonia’s Digital Health Strategy and their efforts towards improved interoperability that addresses limitations of current information systems, reduces loss of meaning in information exchange, and advances standardization of care processes. Delegates will next break into small groups (5-6 per table with one facilitator), to discuss what translation needs would be required to implement an interoperable system (like Catalonia’s), and how those needs could be met (e.g. consensus group discussions, technological solutions like Artificial Intelligence and Natural Language Processing). Facilitators will use a live Google Jamboard to record ideas shared by the individual groups. Structure: 1) Introduction (10 minutes); 2) Catalonia Example (10 minutes); 3) Table Discussions (25 minutes); 4) Identification of current work and gaps (15 minutes) Summarizing take home messages: In the final 15 minutes of the session the full group will reconvene to review the live Google Jamboards and engage in priority setting of translation needs. Delegates will be asked to identify where they may already be working on identified needs, and where current gaps might exist. This exercise will allow us to bring together sub-groups to tackle translation problems who would then engage in webinars and/or discussion groups between ICIC23 and ICIC24 with the aim to generate a white paper, report, or journal special issue that would bring together current and new knowledge generated by SIG members of the translational work of interoperability.
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 | 0,030 | 0,013 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,014 | 0,006 |
| Science ouverte | 0,004 | 0,014 |
| Intégrité de la recherche | 0,009 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,009 |
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 ».