Patients’ Perceptions of Using a Digital Previsit Tool in Outpatient Settings (Part 2): Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Patients with long-term conditions, such as stroke, require regular follow-up visits to health care professionals to identify changes in symptoms. The digital previsit tool Strokehälsa (Strokehealth) has been designed to encourage individuals with stroke to reflect on stroke-related health concerns before a follow-up visit, thereby potentially enhancing their engagement during the visit. Strokehealth has previously been evaluated using a patient satisfaction survey (part 1), but there remains a need to further explore patients' perceptions and needs to optimize its functionality before broader implementation. OBJECTIVE: The overall aim was to attain deeper insights into patients' views and experiences of using the digital previsit tool Strokehealth before a follow-up visit. A secondary aim was to identify potential improvements to the tool based on these insights. METHODS: For this qualitative study, patients who had used Strokehealth version 1.0 before a follow-up visit were recruited through the previous survey between November 2020 and June 2021. Individual semistructured interviews were conducted, and data were analyzed using reflexive thematic analysis. Subsequent workshops were held with people with firsthand experiences of stroke, other stakeholders (including health professionals and researchers), and a web consultant to finalize decisions regarding adjustments to be implemented in Strokehealth version 2.0. RESULTS: Interviews were conducted with 33 participants (23 men and 10 women), with a median age of 67 (IQR 55-76) years. Analysis of the data regarding participants' experiences of using Strokehealth revealed three overarching themes: (1) a supporting tool for preparing dialogue and identifying needs, (2) how Strokehealth is introduced and communicated affects perceived usability, and (3) the wording and structure of Strokehealth influences the response process. The findings captured various aspects of receiving and using the digital previsit tool, highlighting its simplicity and purpose. Overall, Strokehealth was well received and contributed to a sense of being well cared for. Participants generally not only found Strokehealth easy to use but also shared suggestions on how to better address stroke-related issues, such as mental fatigue or pain. Examples of changes that have been implemented in Strokehealth version 2.0, based on participant feedback, include improved explanatory texts and expanded opportunities for free text. CONCLUSIONS: The findings indicate that the freely available digital previsit tool Strokehealth was generally well received by patients with stroke who were scheduled for follow-up visits in outpatient settings. TRIAL REGISTRATION: Researchweb 275135; https://www.researchweb.org/is/vgr/project/275135.
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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 | 0,011 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 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 ».