Identification of best practices for training in remote symptom monitoring using electronic patient-reported outcomes.
Notice bibliographique
Résumé
441 Background: As remote symptom monitoring (RSM) using electronic patient-reported outcomes (ePROs) is increasingly implemented as part of standard-of-care, practices must be prepared to train diverse clinical teams. Little is known about best practices for training multidisciplinary teams to engage effectively with ePROs. Methods: This quality improvement initiative evaluated a training approach for RSM using Plan Do Study Act (PDSA) cycles conducted in oncology practices at the University of Alabama at Birmingham (UAB). Multiple team members participated in training sessions over the duration of implementation scale-up. Training logs, field notes on barriers, and iterations to the training approach were updated using Excel spreadsheets. A recorded training session was utilized as an update to the training approach conducted via Zoom. Results: Overall, 145 providers (nurses, social workers, navigators, clinicians) were trained. Initial training (PDSA Cycle I) included a Zoom lecture for lay navigators, nurses, and physicians conducted by the physician lead, which included rationale for RSM, provider roles, and technical instruction. Barriers identified included limited knowledge retention and difficulty using ePRO technology in practice. In PDSA Cycle II, training included advanced practice providers. In addition, a nurse champion was added to the training team. Content was split into a lecture for rationale and roles (Zoom or in person, based on provider preference) and one on one hands on in-clinic training on technical aspects of ePRO delivery led by the training manager and nurse champion. While this approach increased engagement, provider turnover necessitating multiple trainings and low knowledge sustainment were barriers. In PDSA Cycle III, additional staff were trained including the intake team, nurse navigators, and social workers. The lecture was recorded for delivery by the training manager or independent viewing. In addition to the initial training, written standard operating procedures for providers, and check-ins or repeat sessions with participants were added for longitudinal engagement. Conclusions: Four key provider training elements were: (1) training more provider types to support the use of ePROs in clinical care delivery; (2) emphasizing in-person technical training by an individual with relevant experience; (3) using asynchronous materials to support both scalability and ongoing support; and (4) including additional sessions with longitudinal one-on-one provider training.
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 | 0,011 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».