Patient and provider experiences in an Integrated Care Pathway in Toronto, Canada: A realist evaluation
Notice bibliographique
Résumé
Background: The fragmented state of Canada current healthcare systems has resulted in disjointed and siloed care between acute care, and home and community care providers, affecting how patients experience care in the community. An effective solution is the refinement and implementation of existing integrated models of care to fill these gaps, place patients at the center of care decisions, and optimize patient and system outcomes.Our qualitative evaluation incorporates an interactive system within a Realist framework of an existing integrated care program (ICP) which supports patients following a hospital admission in Toronto. Our study design will uncover major factors and causal mechanisms that can be used to understand, and identify facilitators and barriers across key systems, functions, and relationships. The culmination of these findings help inform program refinement and the spread of the ICP with acute care involvement in other provinces. Approach: Situated within a broader mixed-methods evaluation, contextual factors were identified a priori through consultation with project stakeholders and co-investigators, alongside a literature review to reflect consistency with Ontario Health Quality Standards in Care Transitions, and international quintuple aims for optimal health system performance. Program-specific information on different dimensions of care and patient-reported outcomes were obtained through interviews with a) adult patients (8 years old) who consented to be contacted via a post-discharge program survey within a 3-month period following program entry, and b) integrated care leads, and healthcare providers who referred and/or cared for at least 0 patients enrolled in the program from April 2023 onwards.Themes were identified using an iterative constant comparative process with descriptive and interpretive analysis, involving open-coding and identifying themes using NVivo 4. A lead patient partner and a public advisory committee, consisting of patient and caregiver partners, were consulted on a quarterly basis to co-design interview guides, review operational challenges and analyses, and interpret/formulate findings. Results: 44 interviews were conducted with 6 patients, 3 integrated care leads, and 5 healthcare providers between October 2023, and April 2024. Themes will be organized into three levels: a) micro the individual perspectives of patients and healthcare providers on their interactions with the ICP (with attention to health-equity, and related consideration of intersectionality), b) information pertaining to healthcare and community organizations, and their collaboration and coordination within the ICP, and c) macro broader policies and regulations that support or hinder the ICP. Across all levels, social determinants of health will be analyzed to measure their impact on program effectiveness. Implications: Findings from the interviews will help a) guide the refinement of the program for future scale and spread to patients with complexity, frailty, or multimorbidity and b) the development and delivery of interviews for this population, patient caregivers, and primary care providers in subsequent project years to examine whether program maturity impacted patient reported outcomes. Additionally, the findings will be triangulated with the quantitative results to inform ICP development nationally.At the international level, relevant learnings include: a) actively engaging and co-designing integrated care program modifications with patients and caregivers ensures that they remain central to health interventions b) realist frameworks are particularly beneficial for learning health systems, as they uncover key contextual factors impacting the implementation and sustainability of ICPs to enable rapid program improvement and c) the identification of program facilitators and barriers at micro, meso, and macro-levels are essential for the scale and spread of programs involving acute care across different regions, and adapting them to diverse healthcare contexts.
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 | 0,024 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,010 | 0,004 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
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.
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 ».