Developing a process of risk-stratified care coordination for older adults in primary care
Notice bibliographique
Résumé
BACKGROUND \nOlder Canadians with chronic diseases are the highest users of the health care system. Primary health care (PHC) could play a central, coordinating role in assessing older adults and managing their care, but at present lacks specific strategies to fulfil this role. Priorities for enhanced care coordination in PHC include: 1) consistent processes to identify and assess older persons and create individual care plans aligned with risk levels; 2) improved care coordination and system navigation; 3) improved access to appropriate services; and 4) improved patient and caregiver engagement (Heckman et al., 2013; World Health Organization, 2008; Wagner, 2000; Goodwin et al., 2013). This dissertation project aims to understand how a process of risk-stratified care coordination for older adults can be developed and implemented in primary care. Information gathered to answer this question will provide an in-depth understanding of: i) the local context where the process is implemented, including available health and support services; ii) the process of implementing a screening and referral process in primary care, and iii) the experiences of providers, patients and caregivers with implementation to see how the process might be modified and to understand what factors are important for future spread. \nMETHODS \nThe Chronic Care Model (Wagner et al., 1999), a framework to guide care improvements and a multi-level (environmental, organizational, patient, provider, and program) framework for implementation of health innovations (Chaudoir et al., 2013) were used to guide the three study phases. Overalldata collection and analysis followed a mixed methods design, within a developmental evaluation approach. Data were collected using ethnographic observations (phases 1,2,3), informal feedback (phase 2), individual and focus group interviews (phases 1 and 3), and survey (phases 1 and 3) and tracking forms (phase 3). Data were analyzed using appropriate qualitative and quantitative techniques. Patients, family caregivers, and health care providers were purposefully sampled from two Family Health Teams in Ontario (rural and urban). \nRESULTS \nThrough focus group interviews with health care providers, lack of care coordination, information sharing, patient engagement, and service awareness were identified. To address these concerns, a process of risk-screening and care coordination for patients 70 + years of age was developed and implemented through an iterative process, in two primary care clinics. 512 patients were screened for level of risk using the interRAI Assessment Urgency Algorithm (AUA) and care was coordinated for individuals based on level of need. Among those screened, 70% of individuals screened as low risk, 25% were screened as moderate risk, and 5% were screened as high risk. As a result, service referrals were made to self-management, community programs, and specialized geriatric services using an online referral mechanism. Although the screening and referral process is time consuming, health care providers, patients and caregivers identified many benefits including early identification of service need, greater awareness of services available in the community, and improved relationships between patients and providers. \nCONCLUSIONS \nA process of risk-stratified care coordination was developed and implemented in primary care through an ongoing, iterative process with older adults, caregivers, and health care providers. Future research activities should focus on testing these findings in other models of care (e.g. solo-physician practice) and in other regions.
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Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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