Referral of community-dwelling older adults from eight emergency departments in Canada: A secondary analysis of cross-sectional data from the interRAI multinational emergency department (ED) cohort study
Notice bibliographique
Résumé
Background: Emergency Department (ED) overcrowding, unnecessary hospitalization, and alternate level of care has been identified as a major issue in Canada and around the world. This problem especially impacts older adult populations, who are at elevated risk of functional decline and adverse events in hospital-centric systems. This study uses data from the interRAI ED Contact Assessment (ED-CA)[1], a comprehensive geriatric assessment used in the ED, to improve our understanding of ED systems of referral to community resources. Target Audience: ED staff in clinical and leadership positions interested in better understanding community referral processes to improve care for community-dwelling older adults. Methods: This study is a secondary analysis of cross-sectional Canadian data from a cohort study of elderly ED patients. The cohort of community-dwelling patients aged 65 and older presented to the ED between April 2017 and July 2018. They were screened and recruited in 10 EDs across Ontario, Quebec, and Newfoundland. The data were analyzed using frequency and logistic regression analyses, then the results were interpreted in collaboration with two geriatricians and one physiotherapist. What was done: This study explored referral patterns and identified predictors of referral from the ED to five different community health services (occupational therapy, physiotherapy, home care, social work, and/or specialized geriatric services) for supporting community-dwelling older adults. Results: We found that the sample (n=1055) was frail, with high needs and a high risk of ED revisit and institutionalization. Over half of the sample was referred to home care, occupational therapy, and/or physiotherapy, while only 16% were referred to specialized geriatric services. Most patients received multiple referrals from the ED. Province was the most impactful predictor for referral to occupational therapy or physiotherapy (OR for Ontario vs. Quebec=62.12, 95% CI [19.04, 202.70]) and home care (OR for Ontario vs. Quebec=12.09, 95% CI [5.81, 25.17]), while having an unstable condition was the most impactful predictor for social work referral (OR=5.51, 95% CI [3.78, 8.02]) and weight loss was the most impactful predictor for specialized geriatric service referral (OR=8.13, 95% CI [5.49, 12.04]). Other notable predictors included having overwhelmed family members, self-rated health, and having had a fall in the last 90 days. Key Learnings: Occupational therapy, physiotherapy, home care, social work, and specialized geriatric services are distinct services, specialized in addressing a specific set of care needs. We learned that specialized geriatric services are underutilized and may be poorly understood. Next Steps: To improve the quality of ED care, we have provided recommendations for restructuring care processes to promote shifts in clinical culture, evidence-informed decision-making, and proactive referral to community health services. References: Costa A, Hirdes JP, Ariño-Blasco S, Berg K, Boscart V, Carpenter CR, et al. interRAI Emergency Department (ED) Assessment System Manual: For use with the interRAI ED Screener (EDS) and ED Contact Assessment (ED-CA). Version 9.3. Washington, D.C.: interRAI; 2017.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».