Effect of Collaborative Review of Electronic Patient-Reported Outcomes for Shared Reporting in Breast Cancer Patients: Descriptive Comparative Study
Notice bibliographique
Résumé
BACKGROUND: Digital monitoring of treatment-related symptoms and self-reported patient outcomes is important for the quality of care among cancer patients. As mobile devices are ubiquitous nowadays, the collection of electronic patient-reported outcomes (ePROs) is gaining momentum. So far, data are lacking on the modalities that contribute to the quantity and quality of ePROs. OBJECTIVE: The objective of our study was to compare the utilization of two versions of a subsequently employed mobile app for electronic monitoring of PROs and to test our hypothesis that a shared review of symptoms in patient-physician collaboration has an impact on the number of data entries. METHODS: The Consilium Care app engages cancer patients to standardize reporting of well-being and treatment-related symptoms in outpatient settings. For descriptive comparison of the utilization of two slightly different app versions, data were obtained from an early breast cancer trial (version 1 of the app, n=86) and an ongoing study including patients with advanced disease (version 2 of the app, n=106). In both app versions, patients and doctors were allowed to share the information from data entries during consultations. Version 2 of the app, however, randomly selected symptoms that required a detailed and shared regular patient-doctor review in order to focus on the collection and appropriate interpretation regarding awareness and guidance for severity grading. The numbers and types of symptom entries, satisfaction with both app versions, and patients' perceived effects during consultations were included for analysis. RESULTS: Symptom severity grading was performed according to the Common Terminology Criteria for Adverse Events (CTCAE) using a horizontal slider and was indicated in descriptive terminology in both apps, while a graphical display facilitated the illustration of symptom history charts. In total, 192 patients electronically reported 11,437 data entries on well-being and 33,380 data entries on individual symptoms. Overall, 628 (of 872 intended) requested patient-doctor symptom reviews were performed in version 2 of the app. Both the amount of data entries per patient and day for well-being (version 1 vs version 2: 0.3 vs 1.0; P<.001) and symptoms (version 1 vs version 2: 1.3 vs 1.9; P=.04) appeared significantly increased in version 2 of the app. Overall satisfaction with both app versions was high, although version 2 of the app was perceived to be more helpful in general. CONCLUSIONS: Version 2 of the app showed much better results than version 1 of the app. A request for collaborative patient-doctor symptom review is likely to affect the number of digital symptom data entries. This app shows high potential to improve the patient-doctor experience. TRIAL REGISTRATION: ClinicalTrials.gov NCT02004496; https://clinicaltrials.gov/ct2/show/NCT02004496 and ClinicalTrials.gov NCT03578731; https://clinicaltrials.gov/ct2/show/NCT03578731.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».