What should we consider when implementing digital assessments in integrated health and social care? Ten action areas for implementation planning 
Notice bibliographique
Résumé
Background: Health system fragmentation contributes to poor health and social outcomes for older adults with multimorbidity and their care partners. Integrated team-based models of care are recommended to improve health service delivery to older adults with complex needs. Standardized assessment instruments deployed on digital platforms are considered a necessary component of integrated care, as they can facilitate information sharing and development of a shared care plan. Previous research in community services and geriatric medicine settings found that using standardized self-report instruments is feasible and has the potential to support system integration, however suboptimal collaboration between sectors persists, highlighting the need for intentional planning around instrument use. The aim of this study was to develop implementation strategies for a digital wellness instrument, the interRAI Check Up Self Report, to support integrated health and social care for older adults and their care partners in a community in Ontario, Canada. Methods: A steering committee of older adults, co-investigators, and primary care, home care, and community service providers provided study oversight. Group concept mapping, a participatory mixed-methods approach, was conducted. Participants included older adults, care partners, and representatives from: home care, community services, specialized geriatric services, primary care, and health informatics. In a series of virtual meetings, participants generated ideas to support the use of the interRAI Check Up as part of a care approach with older adults and rated the relative importance of these ideas. Hierarchical cluster analysis was used to map the ideas into clusters of similar statements. Participants reviewed and interpreted the map to co-create an action plan. Results: Forty-one participants contributed to creation of a cluster map of ten action areas: engagement of older adults and care partners, instrument’s ease of use, accessibility of the assessment process, person-centred process, how to use the Check Up, training and education for providers, provider coordination, health information integration, health system decision support and quality improvement, and privacy and confidentiality. Health system decision support was rated as the lowest relative importance and health information integration was rated as the highest relative importance. The action plan included implementation strategies for the optimal use of the Check Up, for example, developing workflow plans for using the Check Up in care processes, including providers’ roles and responsibilities. Strategies were also developed to foster health and social care provider coordination, such as providing education on how to work as a team on a shared care plan, informed by a standardized self-report instrument. Conclusion: The introduction of digital instruments requires preparation, intervention, and evaluation at person, provider, and system levels. An instrument’s thoughtful selection is as important as developing an implementation plan that supports development of necessary processes and skills for older adults and care providers to adopt the tool into routine practice and evaluate the impact. Next steps: As communities implement digital instruments, such as the interRAI Check Up, this co-designed plan of ten action areas can be used to inform implementation planning and evaluation of practical strategies to support community, team-based integrated care.
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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 | 0,163 | 0,128 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,011 | 0,009 |
| Communication savante | 0,017 | 0,013 |
| Science ouverte | 0,007 | 0,018 |
| Intégrité de la recherche | 0,009 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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.
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 ».