Engaging patients as healthcare partners through the meaningful use of Voxe: A digital patient-reported outcome platform
Notice bibliographique
Résumé
Background: As health services shift towards more patient-centred care, the importance of patient-reported outcome measures (PROMs) is increasingly recognized. PROMs can effectively capture patients’ perspectives and enable meaningful engagement. This research program aims to improve health outcomes for pediatric patients by systematically implementing PROMs into clinical practice. We have targeted methodological and practical decisions needed to guide effective integration of PROMs into care settings with a phased approach, including a systematic review (Phase 1), key stakeholder interviews (Phase 2), and a consensus workshop (Phase 3). The preliminary evidence that informed this project addressed critical elements within implementation science, including assessing fit and readiness for change, establishing stakeholder buy-in and fostering a supportive environment. In this study, we designed (Phase 4) and tested the usability (Phase 5) of an electronic PROM (ePROM) platform called Voxe. Methods: A user-centred approach, in which end-users (i.e., patients and healthcare providers (HCPs)) are central to the design process and usability testing, guided Voxe platform creation. Iterative testing sessions involved participants from The Hospital for Sick Children (SickKids) and Children’s Hospital of Eastern Ontario (CHEO) completing (1) tasks on design wireframes and prototypes to evaluate effectiveness and efficiency, (2) the Microsoft Desirability Toolkit, a system usability scale, and (3) a semi-structured interview to assess satisfaction and gather user feedback. This methodology was implemented to ʻtest, learn and improveʼ Voxe prior to full development and launch. Results: Forty-nine patients aged 8-17 years (n=25 solid organ transplant patients receiving care at SickKids; n=24 hematology and oncology patients receiving care at CHEO) and 38 of their HCPs (n=22 HCPs from SickKids; n=16 HCPs from CHEO) participated. Iterative and sequential testing rounds demonstrated improved effectiveness as the proportion of successfully completed tasks increased from 74% to 85%. Efficiency improved as time-to-task decreased from 23.2 to 15.8 seconds. Patients described Voxe as “fun”, “friendly”, “helpful”, “easy”, “calm”, “clear” and “creative”. Patients shared “[Voxe] makes you feel like you’re welcome in the hospital” and “…it feels like you can get better with this app”. HCPs highlighted that Voxe is “intuitive” and enables “a more patient-centered model of care”. HCPs also remarked “it [Voxe] is very user friendly”, “it [Voxe] is pretty clear and easy to use”, and “I can see Voxe naturally fitting into what we do already”. Conclusion: Findings will influence how Voxe looks and operates to drive successful and sustainable adoption and the meaningful use of digital solutions and shared data for information and care management. Although solid organ transplant patients, hematology and oncology patients, and their HCPs participated in the design and testing, Voxe could be implemented with any pediatric population as it was built to accommodate any ePROM. Voxe acknowledges and supports patients as partners in their health and healthcare and fosters meaningful patient engagement. Future research will assess the implementation effectiveness of the Voxe ePROM platform. Ultimately, Voxe leverages eHealth technology as an innovative approach to meaningfully capture and integrate patients’ voices and transform their care experiences.
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 | 0,050 | 0,076 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».