Person-Centred 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 ICIC23 to discuss priority issues around interoperability and information data sharing. This year we will advance this conversation by focusing on how we can embed interoperable digital solutions and data systems into caring communities of patients, caregivers, and and decision merks in a way that maximizes fit for purpose and ultimate impact. Aims and Objectives: The SIG will engage participants in a critically important discussion around how and why interoperable data and digital systems need to be embedded in their communities to support person-centred care delivery. Last year’s session included a roundtable discussion about how patients, caregivers, and communities need to be able to access data, interpret and understand information. Three core issues came from our patients and caregiver partners in that session including: 1) the need to standardise information sharing with both patients and caregivers in a way that is meaningful and useful; 2) how we ensure interoperable technologies support, maintain, and grow important interactions and relationships; and 3) building a person-centred lens into policy drivers for interoperable systems. We will build on these and share current and emerging work around these three areas of interest; focusing on how to best collect and integrate the needs of community members into the ongoing development of upcoming and existing digital and integrated care programs. Audience: All existing and newly interested members of the SIG are welcome to join this discussion. Our growing 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) who will talk about each of the three issues in relation to current work and evidence. SIG member and patient partner Hilary Horlock will then provide her perspective on these three issues, to help understand what standardised information can look like, how relationships can be supported and maintained, and building from her experience driving policy in Canada, how digital health policies can maintain service users at the centre. Delegates in the session will then break into working groups to address each of the three topic areas to generate a preliminary set of recommendations that can be applied at organizational and system levels. Facilitators will use a live Google Jamboard to record ideas shared by the individual groups. Structure: 1) Introduction (15 minutes); 2) Hilary’s reflection (15 minutes); 3) Table Discussions (20 minutes); 4) Report back (10 minutes) Summarizing take home messages: In the final 10 minutes of the session the full group will reconvene to review the live Google Jamboards and recommendations. This exercise will help to generate a short report we can share with our networks, while allowing us to broker collaborations between delegates working on shared challenges.
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 | 0,018 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,009 | 0,005 |
| Science ouverte | 0,003 | 0,012 |
| Intégrité de la recherche | 0,007 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,030 | 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 ».