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Enregistrement W4413358167 · doi:10.5334/ijic.nacic24168

Development and Implementation of a System Accountability Framework for Nova Scotia

2025· article· en· W4413358167 sur OpenAlexaffabout
Kate Melvin, Tanya Penney, Paige Moorhouse

Notice bibliographique

RevueInternational Journal of Integrated Care · 2025
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueEvaluation and Performance Assessment
Établissements canadiensNova Scotia Health Authority
Organismes subventionnairesnon disponible
Mots-clésNova scotiaAccountabilityNova (rocket)Process managementPolitical scienceBusinessPublic administrationGeographyEngineering

Résumé

récupéré en direct d'OpenAlex

Population growth and comorbidity are driving demand for episodic care and extended community supports creating backlogs in access, particularly for emergency care in Nova Scotia. In an effort to optimize access to care, Nova Scotia Health implemented a cross-sectoral framework to help move patients through their acute care journey. The System Accountability Framework is designed to ensure a shared approach with clear accountabilities at all levels of leadership and operations, initially focusing on the acute care sector within three defined zones of accountability: Emergency Department, Inpatient, and Community. Ultimately, the Framework’s purpose is to pull the healthcare system together in a coordinated approach to delivering patient care, creating an awareness of the system as a whole, and building key integrations to ensure that the location of care meets each patient's dynamic needs, with no patient left behind. Nova Scotia Health operations is divided into four geographic zones. The Framework was developed in response to a report commissioned by the Department of Health and Wellness and led by the Integrated Acute and Episodic Clinical Services Network. Implementation success requires close partnerships within Nova Scotia Health (e.g. Access and Flow Network, Primary Health Care Network, Perioperative Network, Performance and Analytics, Research and Innovation, Continuing Care) as well as external partners (e.g. Emergency Health Services, Department of Health and Wellness, Department of Seniors and Long Term Care, Department of Community Services). Representatives from each sector helped to identify metrics and regularly report on progress and barriers. Operational plans were shared across geographies during intensive engagement workshops. Within each zone of accountability, Network and operational teams collaborated to develop and implement shared and local models and policies that can be mapped to each metric. Through engagement with system partners (listed above), initiative leads developed the implementation framework including graduated metrics for improvement as well as tools and checklists for operational leaders within each Zone of Accountability. Ambulance offload time was selected as a state variable that reflects overall flow in the acute care system with a Phase goal of reducing 90th percentile ambulance offload time by 50% (0% per month over 5 months. Tools included models of care, leaning processes, integrating Action for Health Initiatives, policies and reporting templates. Weekly communications and check-in meetings including operational leaders across health sectors provide an opportunity to report on progress and share early successes and opportunities for spread and scale. A dashboard (in development) provides a line of site to weekly performance. The Framework has been integrated as a key support for a larger provincial initiative called Operational Excellence which provides structured reporting and operational grip. The poster will lay out key models and policies that were implemented in each zone of accountability. The framework implementation is currently in week 4 and has shown a great deal of system improvement. This week, there were ,46 inpatient admissions and ,069 discharges. Of the discharges in week 4, 27.8% occurred before 2:00 p.m. Out of ,372 ambulance offloads, only 65 took longer than the designated time cut off at the site, a drop from 3 in 2023. 30.6% of ambulance offloads were completed within 30 minutes (compared with 27.8% last year), and 90% were completed within 46.3 minutes (compared with 237 minutes last year). Over 228 ambulance hours were returned to the communities. Our Emergency Health Service (EHS) partner has reported a decrease in their ambulance response time over the past six months. Current response times for emergency calls are averaging at 20 minutes. Zones are focused on finding efficiencies and optimizing resources through close coordination with frontline teams to improve EHS transfer times between facilities. Efforts continue to address surge capacity, with a focus on implementing strategies and processes to improve provider response times and patient stays. This involves streamlining discharge processes for smoother patient flow. The poster will report the 6-month outcomes for the System Accountability Framework, including run charts and comparisons with data from the same time interval in 2023. The challenges that the System Accountability Framework aims to address are common to many health jurisdictions. This initiative demonstrates a real life implementation of the recommendations supported by the national CAEP EM-POWER report and a view to how providing a system-wide framework can help teams at all levels of care develop and implement improvements that are based on a common shared goal: each patient receives the right care in the right setting, from the right provider. The poster will share challenges and opportunities related to the development and implementation of the Framework.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,037
score de la tête « metaresearch » (Gemma)0,044
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,361
Score d'incertitude au seuil0,726

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0370,044
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0040,002
Études des sciences et des technologies0,0070,004
Communication savante0,0110,003
Science ouverte0,0040,008
Intégrité de la recherche0,0030,003
Charge utile insuffisante (le modèle a refusé de juger)0,0050,001

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 Opus0,098
Tête enseignante GPT0,519
Écart entre enseignants0,421 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

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

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'admission2
Résumé présentoui

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