Improving symptom screening rates for patients with head and neck cancer.
Notice bibliographique
Résumé
e23246 Background: Effective symptom management in cancer care relies on regular and accurate reporting using validated patient-reported outcome measures (PROMs), such as the Edmonton Symptom Assessment System (ESAS). The ESAS measures nine common cancer symptoms, rating them on a scale from 0 to 10 to improve monitoring and communication regarding patient symptoms and prevent adverse outcomes such as emergency department visits. There is a need to enhance completion rates to ensure that head and neck cancer (HNC) symptoms are effectively addressed. High completion rates provide valuable data for cancer centers and clinicians to intervene early and support patients through their cancer journey. This makes it an important target for quality improvement (QI) with wide-ranging implications for patient outcomes and care quality. Our objectives were to: Investigate underlying reasons for poor ESAS completion rates. Lay the groundwork for strategic interventions aimed at bolstering symptom screening among HNC patients. Methods: This was a QI project targeting the HNC patient population at a regional cancer centre (RCC). Our approach included stakeholder engagement meetings alongside the expertise of QI specialists. Comprehensive data collection was conducted to analyze symptom screening rates and identify potential barriers to ESAS completion. We tracked ESAS completion on weekly HNC clinic days from 2022-2023 via manual and automated chart abstraction. Patient, staff, and volunteer interviews, along with direct clinic observations, provided data for Ishikawa diagrams, facilitating a root cause analysis. Results: We identified a marked decline in ESAS completion rates, steadily decreasing from an average of 29.4% during the 2022-2023 fiscal year to an average of 7.6% between April-November 2023, with rates dropping as low as 3.3% in September 2023. Root cause analysis pinpointed several barriers to ESAS completion, including the transition from paper-based assessments used during the pandemic to electronic formats, low patient awareness of the purpose of symptom screening, perceived lack of value in screening by patients and staff, and the absence of direct guidance from staff in helping patients complete their ESAS. Variable communication practices and inconsistencies in registration staff directing patients to kiosks was also observed. Improving infrastructure and support for volunteer services was identified as a potential solution, which may optimize their effectiveness in symptom screening roles. Conclusions: The project has laid the foundation for targeted QI interventions aimed at improving ESAS screening adherence. By addressing the identified barriers, this work endeavors to re-engage patients in active symptom reporting and to integrate this critical aspect of care into the daily routine of the outpatient hospital setting, thereby enhancing the overall management of HNC symptoms.
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 | 0,004 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».