Person-centered integrated care for people living with kidney disease and multimorbidity: exploring challenges and opportunities
Notice bibliographique
Résumé
Background: People with chronic kidney disease (CKD) and other chronic conditions commonly experience fragmented care. A person-centered integrated care (PC-IC) approach has shown to effectively address care delivery and health outcomes for other populations. However, evidence to guide the application of PC-IC in patients with early-stage CKD and multimorbidity remains limited.To understand challenges and opportunities to address PC-IC for early-stage CKD and multimorbidity we engaged people with lived experience, including patients, caregivers, healthcare providers, and organizational leaders in Alberta, Canada. Approach: Our diverse multidisciplinary team includes two patient partners with lived experience (a patient and caregiver). They supported the grant applications, project planning, material development, and recruitment. The patient partners continue to provide guidance on interpretation of results and knowledge mobilization. We partnered with Essenburgh, leaders in the study of integrated care who developed the Rainbow Model of Integrated Care (RMIC) Framework and continue to collaborate, incorporating their international experience. Various organizations including Alberta Strategy for Patient Oriented Research, Can-SOLVE CKD Network, Primary Health Care Integrated Network, and Alberta Health Services (via the Medicine Strategic Clinical Network) supported the project including recruitment and facilitating community connections.This is a multi-phase, multi-methods patient-oriented study to co-design a PC-IC model of care. Our initial phases of work included conducting an online survey, using the validated RMIC Measurement Tool (RMIC-MT; May to November 2023) to measure the delivery of integrated care. We invited patients, caregivers, healthcare providers (HCPs), and decision-makers over the age of 8 and living in Alberta, Canada. We conducted descriptive and comparative analyses based on background characteristics (e.g., roles, settings, health status complexity). Next, we interviewed this population to contextualize our survey findings (9 completed, anticipating total of 30 interviews). We will use the Framework Method of analysis based on the RMIC Framework. Results: A total of 97 participants completed the survey. Participants included people with early-stage CKD and at least one comorbidity (n=24, 25%), caregivers (n=2, 2%), and HCPs (n=6, 63%). Many participants live in urban (n=62, 70%) compared to rural settings (n=26, 30%), and most HCPs were nephrologists (n=23, 4%) and primary care physicians (n=2, 38%). Overall, patients/caregivers and HCPs rated their experience with person-centered care moderately (3.98/5 and 4.8/5, respectively). The lowest scored items were care coordination between different providers (patients/caregivers; 3.29/5) and regional healthcare laws and regulations (HCPs; 2.94/5). Overall, Alberta received lower integrated care scores than the international benchmark.Preliminary interview results indicate current successes (e.g., multidisciplinary teams, provider to patient/caregiver communication) and challenges (e.g., information sharing between specialty and primary care) to implementing PC-IC. Implications: This research adds to existing knowledge on understanding the current state of PC-IC for early-stage CKD and multimorbidity management through a validated measurement tool and supplementary interviews. Our findings demonstrate opportunities to co-create an innovative PC-IC approach that would align services and resources to meet patient needs.Simultaneously, we are conducting a scoping review to identify existing PC-IC strategies at the micro-, meso-, and macro-levels employed internationally for this population. We will use these findings to inform a pan-Canadian survey aimed at identifying feasible PC-IC strategies.Our results from this current work inform the next phases of work including prioritizing barriers to PC-IC and identifying and leveraging feasible strategies to reimagine a model of care that addresses identified gaps.
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 | 0,021 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,010 | 0,006 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,003 | 0,015 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».