The InterRAI ED tool for screening older patients in the emergency department: “What am I supposed to do with this?”
Notice bibliographique
Résumé
in Toronto describe the use of the interRAI Emergency Department screener in predicting the trajectories of health care utilization among older patients who presented to the emergency department (ED) in Toronto. 1 Their goal was to determine if the rapid screening tool would be able to predict the health care utilization of older patients seen in the ED.A smartphone app was used by the triage nurse during the presentation of the patient, among a convenience sample.In short, the app was designed to define if an older patient had challenges in basic self-care (basic activities of daily living), cognition, caregiver burden, self-reported health, stability of prior conditions, dyspnea, and depression.The answers to the questions resulted in a low, medium and high risk of additional health care utilization.The authors mapped the trajectories of 755 older patients after their emergency department care.About 40% of the patients were hospitalized after their ED care.A quarter of the 755 patients were identified as high-risk at the time of their triage.Those with a high-risk score on the interRAI were more likely to be admitted to the hospital from the ED, more likely to stay longer in the hospital and receive a geriatric consultation.The tool was not helpful in identifying those who were at high risk of returning to the hospital in 30 days.In a recent discussion, at Advocate Health in Wisconsin, of efforts to improve the care transitions of older patients from the emergency department to home, a nurse posed a straightforward question about the triage tool for which we had been advocating.She asked, "What am I supposed to do with this information?"The emergency nurses and emergency physicians waited for a response from the leaders in the room.Everyone knew that the response would drive the engagement of the nursing staff on further implementation of the screening tool.Our response would determine if the nurses would change their practice and whether their efforts would be followed with improvements in care.Our system was discussing how to use such a tool to improve our ability to identify those older patients who were at highest risk of returning to the emergency department.I wish to capture that moment in our efforts to improve the emergency department care for older patients as I reflect on Dr. Downer and colleagues' paper.I will describe a few caveats of the study and highlight key points we can take from the paper.I will further frame an evidence-based response to the nurse's question and propose some practical steps to consider.First, a few caveats should be noted from the Dr. Matthew Downer et al study.The emergency department which was the setting of the study is a site of best practice in North America for the emergency care of older adults.The systems of care at this site may function better than most
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 distillée sur la base complète
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| 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) | 0,001 | 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 tête enseignante, 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 ».