A novel PA-led urgent care model for medical oncology patients at a large academic cancer center.
Notice bibliographique
Résumé
37 Background: Oncology patients are high users of the emergency department (ED), which often results in hospital admissions for management of cancer symptoms or cancer treatment toxicities. Interventions such as urgent care (UC) models can decrease such visits, and help improve patient management, health care utilization, and patient experience. Sunnybrook Health Sciences Centre, a large tertiary care hospital in Toronto, Canada has a high volume of medical oncology ED visits (average 4 per day) with about 50% admitted for management. Methods: A novel physician-assistant (PA) led and physician supervised UC model was developed to assist in medical oncology patient phone triage, assessment, and management of cancer or treatment related issues that would otherwise have been sent to the ED by the oncology team. There were two phases: 1) due to COVID, the patients were managed in a dedicated stream primarily using space and nursing in the ED, 2) a dedicated UC clinic with nursing support was opened for these patients. Results: In phase 1, there were 424 referrals over 24 months; 84% would have otherwise been sent through the usual ED process. 26% of patients were managed with PA navigation outside the UC program in other hospital settings. Of the 204 patients formally treated in the UC stream, 67.7% were discharged home. At 48 hours, 89% of discharged patients were stable or improved; this was 80% at 14 days, and 17.3% came back to the ED or were admitted within 14 days of the UC visit. In phase 2, there have so far been 214 referrals over 5 months; 83.6% would have otherwise been sent to the ED. Of the patients who were assessed, 77.9% were discharged home. Outcomes of these patients are being collected. The top 3 patient issues managed during both phases were: fever, pain, and dyspnea. Fifteen patient telephone surveys were completed, and 93.3% were either satisfied or highly satisfied with their UC experience. Conclusions: A novel PA-led triage and management model for urgent medical oncology patient issues was found in initial phase to be feasible and effective with streamlined care through the ED. Once a dedicated UC clinic was opened, referral volumes increased, and a high rate of ED diversion, patient discharge, and effective care was continued. Patients were also highly satisfied. Several ongoing process and outcome measures are being evaluated to help expand the scope and impact of this resource.
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 | 0,001 | 0,003 |
| 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 | 0,001 | 0,000 |
| 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) | 0,008 | 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 ».