Efficacy of Digital Health Tools for a Pediatric Patient Registry: Semistructured Interviews and Interface Usability Testing With Parents and Clinicians
Notice bibliographique
Résumé
BACKGROUND: Acute respiratory infection (ARI) in childhood is common, but more knowledge on the burden and natural history of ARI in the community is required. A better understanding of ARI risk factors, treatment, and outcomes will help support parents to manage their sick child at home. Digital health tools are becoming more widely adopted in clinical care and research and may assist in understanding and managing common pediatric diseases, including ARI, in hospitals and in the community. We integrated 2 digital tools-a web-based discharge communication system and the REDCap (Research Electronic Data Capture) platform-into the Pragmatic Adaptive Trial for Acute Respiratory Infection in Children to enhance parent and physician engagement around ARI discharge communication and our patient registry. OBJECTIVE: The objective of this study is to determine the efficacy and usability of digital tools integrated into a pediatric patient registry for ARI. METHODS: Semistructured interviews and software interface usability testing were conducted with 11 parents and 8 emergency department physicians working at a tertiary pediatric hospital and research center in Perth, Western Australia, in 2019. Questions focused on experiences of discharge communication and clinical trial engagement. Responses were analyzed using the qualitative Framework Method. Participants were directly observed using digital interfaces as they attempted predetermined tasks that were then classified as success, failure, software failure, or not observed. Participants rated the interfaces using the System Usability Scale (SUS). RESULTS: Most parents (9/11, 82%) indicated that they usually received verbal discharge advice, with some (5/11, 45%) recalling receiving preprinted resources from their physician. Most (8/11, 73%) would also like to receive discharge advice electronically. Most of the physicians (7/8, 88%) described their usual practice as verbal discharge instructions, with some (3/8, 38%) reporting time pressures associated with providing discharge instructions. The digital technology option was preferred for engaging in research by most parents (8/11, 73%). For the discharge communication digital tool, parents gave a mean SUS score of 94/100 (SD 4.3; A grade) for the mobile interface and physicians gave a mean usability score of 93/100 (SD 4.7; A grade) for the desktop interface. For the research data management tool (REDCap), parents gave a mean usability score of 78/100 (SD 11.0; C grade) for the mobile interface. CONCLUSIONS: Semistructured interviews allowed us to better understand parent and physician experiences of discharge communication and clinical research engagement. Software interface usability testing methods and use of the SUS helped us gauge the efficacy of our digital tools with both parent and physician users. This study demonstrates the feasibility of combining qualitative research methods with software industry interface usability testing methods to help determine the efficacy of digital tools in a pediatric clinical research setting.
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 | 0,071 | 0,112 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».