MétaCan
Menu
Retour à la cohorte
Enregistrement W4409337335 · doi:10.5334/ijic.9488

Care Everywhere: Implementing and Evaluating a Network-level Digital Transformation to Improve Coordination and Access across a Health System

2025· article· en· W4409337335 sur OpenAlexaboutno aff
Aurelia Di Fabrizio, Erin Cook, Amath Thiam

Notice bibliographique

RevueInternational Journal of Integrated Care · 2025
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueInnovative Approaches in Technology and Social Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésIntegrated careHealth careDigital healthProcess managementHealthcare systemDigital transformationBusinessKnowledge managementComputer scienceWorld Wide WebEconomic growthEconomics

Résumé

récupéré en direct d'OpenAlex

Context: In the context of reduced capacity through a severely burnt-out workforce, and struggling to provide appropriate care to populations, health systems are turning to innovative approaches to health services. The West-Central Montreal Integrated Health and Social Services University Network (CCOMTL) has adopted an innovation-based philosophy, centered on transformation via digital health solutions with tangible impacts for the population served by the network. A foundational pillar of this transformation is the establishment of a network-level Command Centre (“C4”): the digital “heart” of the system that leverages local and provincial data to generate real-time insights, including predictive algorithms that allow team members to anticipate system capacity. Of note, the C4 extends beyond bed flow management, instead providing an overview of patients across their entire care trajectory. This includes helping people access the right resources to avoid unnecessary hospitalizations, e.g., facilitating primary care and mental health care connections, and making sure patients who are hospitalized can be appropriately directed to the right place. All sectors of the CCOMTL have been engaged as partners in shaping the C4, whether determining data needs for their respective domains, or co-creating roles in response to shared learnings generated through collaboration. Ultimately, the C4 is a digital intervention aimed at re-imagining system-wide coordination. To date, the C4 has resulted in improvements in indicators related to access and flow, such as reduced average length of stay by 0.5 days for hospitalized inpatients and 1.4 days for surgical patients; as well as a reduction of over 50% in the number of people awaiting community mental health services. While some of these indicators arguably represent hospital-centric markers of efficiency, they are accompanied by improved care coordination processes that make sure people don’t fall through the cracks of the system once they leave the hospital. Challenge: As sectors learn to collaborate, and even share physical infrastructure, cultural siloes must be addressed and broken down to ensure the C4's sustainment beyond initial implementation. Furthermore, as C4 itself represents a highly complex intervention, stakeholders must learn which activities help or hinder progress. To address these challenges, we are conducting a two-year developmental evaluation, aimed at providing C4 stakeholders tools and information on the processes, activities, and outputs that support its long-term implementation and sustainability. While C4’s objectives of improving patient trajectories of care and coordination are clear, we also highlight the critical objective of improving the experience of clinicians themselves, who are navigating through incredible duress and often insufficient support. Developmental Evaluation Process and Impacts: The evaluation is presently ongoing, though interim findings are being collected throughout the evaluation period. We will present the impacts of the evaluation across three dimensions: 1)Describing quality improvement activities (e.g., workshops and facilitated training) that have been developed in response to engagement with and evaluation feedback from C4 stakeholders. 2)Quantitative and qualitative insights related to team- and culture-based dimensions known to impact implementation of complex interventions. 3)Process and outcome measures established to date, including a logic model summarizing and expanding upon the above points.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,894
Score d'incertitude au seuil0,798

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,027
Tête enseignante GPT0,345
Écart entre enseignants0,318 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueInternational Journal of Integrated CareMême sujetInnovative Approaches in Technology and Social DevelopmentTravaux en français237 207