Pilot to scale: Implementing a digital triage solution across a large comprehensive cancer centre.
Notice bibliographique
Résumé
225 Background: Princess Margaret Cancer Centre (PM) sees over 80,000 patients annually and primarily operates using an outpatient model. Historically patients use a phone/voicemail triage service to communicate with their care team between scheduled visits. Many patient queries lack critical information necessary for effective screening, assessment, and processing. In turn leading to delays or missed calls that negatively impact patient experience, system efficiency, and safety. To address these challenges, we piloted a digital triage service and subsequently implemented in sixteen clinics. This project evaluated initial adoption, usage, care patterns and satisfaction in the first 5 months post-implementation. Methods: We used human-centred service design to develop the digital triage service within the EMR (Epic). Implementation strategies to scale across all outpatient clinics included leadership buy-in, co-design with key stakeholders, a phased implementation approach, and improvement cycles. To evaluate initial adoption, usage and care patterns, we used EMR system data. To assess patient satisfaction, we surveyed patients who used the service during the first three months. We conducted semi-structured interviews with staff to understand their experience integrating digital triage into their workflow. Results: Following the go-live period, patients sent an average of 1096 digital requests per month, representing 16% of all triage requests (phone + digital). The most common requests were symptom management (36% of nursing requests) and appointment change requests (40% of administrative requests). The algorithm automatically routed 93% of requests to the most appropriate care team member, compared to 74% pre-implementation. When asked if they were satisfied with their experience, most patients agreed or were neutral (69% and 10%, respectively), while 21% disagreed. In semi-structured interviews, nurses and administrative staff described improved efficiency by having triage requests integrated within the EMR, while have challenges related to prioritizing requests across multiple channels (e.g. digital, phone, others). Conclusions: Digital triage is foundational for better cataloguing and understanding our patients' unmet needs at home and between visits, enabling a continuous learning process to understand when and why people struggle throughout their care journey. Ongoing analyses will explore disease site-specific trends, compare phone to digital triage, and identify differences between users of each method. The data will inform further improvement cycles on the build and workflows, while enabling appropriate resourcing of this critical service and shifts towards proactive care.
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 | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 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 | 0,004 |
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 ».