Comprehensive Standardized Assessment for Information Continuity: What Does the Workforce Need
Notice bibliographique
Résumé
Introduction: Older adults living with frailty and multimorbidity interact with multiple care providers across different health settings increasing the risk for fragmented care and information discontinuity. Information discontinuity results in workforce inefficiencies and adverse health events, including duplication of assessment and diagnostics, medication errors and increased health service use. Standardized assessments potentiate integrated care by communicating consistent measures of health information between health care sectors and providers. InterRAI assessments facilitate integration through promoting a common language and aligning successive assessments across the care continuum. Description: We used a pragmatic case example of a theoretical medically-complex older adult to illustrate effective use of interRAI standardized assessments throughout the health care journey. The interRAI suite of instruments spans across the age continuum, from pediatrics to geriatrics, and is designed to be used across diverse care settings, including community services, primary care, home care, long-term care, acute care, inpatient and community mental health, and palliative services. The case example represents one patient’s assessment findings, derived from standardized assessment instruments, such as the contact assessment, home care assessment and long-term care facility assessment. Automated and embedded risk algorithms are generated as outputs from the assessment, acting as decision support tools to inform care planning for clinical, functional, and social support needs. Process schematics depict potential workflows, where instruments can guide care strategies and facilitate the flow of information between the care team members. Discussion: Integrating elements such as using a common language, standardized assessment items, and embedded decision support algorithms, can support effective communication and collaboration in the care of older adults between clinical settings. Risk algorithms and scales support real-time identification of care issues, with standardized assessment items allowing for changes in health status to be easily recognized. Operationalizing a suite of standardized assessment instruments across the health system offers advantages for the individual including improved continuity of care, as well as for organizations and the system through use of a consistent measurement of health metrics between health providers and sectors, and evaluating health system performance. Successful adoption of comprehensive assessment tools to support integration requires training, stakeholder engagement and time to embed work and care processes into practice. Conclusion: Standardized language and algorithms used in interRAI comprehensive assessments can increase capacity for integration and continuity of care across the full spectrum of health sectors and settings. Findings from this pragmatic case example demonstrate real-world application and utility of standardized assessments to support an integrated workforce.
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 | 0,073 | 0,227 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,007 | 0,005 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,008 | 0,023 |
| Science ouverte | 0,005 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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 ».