Streamlining acute oncology (AO) at the Princess Margaret (PM) Cancer Centre: An AO centralized referral and triage.
Notice bibliographique
Résumé
76 Background: Acutely ill cancer patients (pts) often present with medical needs that cannot be managed in a standard oncology clinic. Several AO clinics have been established at the PM to support pts and promote emergency department (ED) avoidance, these include Urgent Care Clinic (UCC), Day Oncology (DO) and Radiation RN clinic (RNC). We recently developed a process to integrate three distinct AO clinics, to optimize pt flow and improve pt/provider experience. Methods: A prospective (2-week) audit was completed to collect key metrics and a survey circulated to collect data on referrer/AO team satisfaction with current process. A multidisciplinary/interprofessional working group was established to review audit data, complete process mapping, and establish a centralized pathway for triage and referral. This included developing a novel EPIC referral form that integrated oncology and ED specific fields (vitals, CEDIS complaints and treatment times) and creating a RN/physician assistant (PA) clinical triage team. Pre-evaluation from AO staff and referrer experience utilized to assess staff perspectives. Post implementation (3-month) audit used to analyze key metrics and post AO staff and referrer surveys were repeated. A pt experience survey has also been developed for longitudinal monitoring of pt satisfaction and creating a RN/PA clinical triage team. Results: The AO centralized process was implemented June 2024 with post-implementation evaluation being completed in Sept 2024. After implementation, both referring providers and AO staff reported improved satisfaction with overall process: referral process (43 to 85% and 36 to 78%); clarity of acceptance criteria (29 to 85% and 21 to 67%), ease of transfer into AO (21 to 76% and 36 to 67%) and communication between OP and AO (50 to 85% and 50 to 89% and 21 to 56%). While referrer reported improvement in necessity to direct to ED transfer (29 to 42%) and AO staff reported ease of transfer out of AO (27 to 44%) and communication between AO clinics (50 to 89%). The average turnaround time from referral to triage was 24 min (mean 19 min) with average monthly volume of 375 referrals within the first 3 months post implementation. The time from AO inpatient bed request to admission remained consistent at approximately 3 hours pre and post implementation. Conclusions: The introduction of a dedicated, interprofessional triage team for AO at the PM has streamlined referrals for AO support, improving staff satisfaction with the referral process amongst referrers and AO providers. Teams report reduced administrative burden and improved ability to focus on direct pt care. We have not observed an increase in pt volumes managed in AO clinics related to limitations in AO clinic capacity due to boarding of admitted pts. Further work is on-going to improve inpatient flow and thereby increase AO clinic capacity, in addition to collecting data on pt experience.
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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 | 0,004 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 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 ».