Usability Evaluation of a Mixed Reality Platform in Pediatric Interventional Cardiology by Specialist Physicians: Mixed Methods Study
Notice bibliographique
Résumé
Background: Immersive technologies such as virtual reality, augmented reality, and mixed reality are used in pediatric interventional cardiology (IC) to improve anatomical visualization and spatial understanding and support procedural precision. These technologies offer 3D representations of complex cardiac structures, which can aid in preprocedural planning, intraoperative navigation, and team communication. As these technologies gain traction in IC, understanding how medical specialists perceive their value is vital. Objective: This study assesses what IC specialist physicians consider valuable in immersive technologies by evaluating their experiences with CardioVision, a software designed for pediatric IC procedures. Methods: A mixed methods design was used, combining questionnaires and semi-structured interviews. Purposeful and snowball sampling was used to recruit specialist physicians with experience in IC. The recruited specialist physicians were asked to submit IC cases for software testing. These were uploaded to CardioVision by the developer, who also conducted the testing in person with the physicians at their designated clinical stations. A total of eight specialist physicians completed a pretest questionnaire, used the software, and thereafter completed a posttest questionnaire, which included System Usability Scale and Likert-scale questions on experience with the software. The interviews explored the specialist physicians' experience with the software's performance. Of the 8 participants, 6 further participated in the interviews. The interviews were conducted by two2 of the authors. The qualitative data were analyzed using inductive content analysis, involving subjective interpretation of textual material from the interviews. Following the approach outlined by Elo and Kyngäs, the process comprised three phases: preparation, organization, and reporting. In the preparation and organization phases, one author organized and categorized the interview data in an Excel sheet. The two2 authors then reviewed the material, compared emerging codes and themes, and reached consensus on the final main theme and subthemes for reporting. Results: In total, 8 questionnaires and 6 interviews were obtained from specialist physicians from Belgium, France, Germany, and Italy. Quantitative data from mean System Usability Scale scores showed that users felt confident using the system (mean 4.25). When using the software, the highest positive feedback was for visualization of structures, where all users (8/8, 100%) agreed. Overall, most participants (6/8, 75%) rated the system positively for decision-making support and 5/8 (63%) for complex case preparation. One participant (1/8, 12%) disagreed with its effectiveness in preparing complex cases, indicating mixed opinions in this area. In the interviews, emerging themes aligned with quantitative data to show that participants valued the software's ease of use, high-quality visualization, and decision-making support. Additional benefits included use in medical education, interprofessional collaboration, and patient communication. Developer-clinician collaboration was emphasized as vital for effective integration into routine practice. Conclusions: Immersive technologies offer clinical value when aligned with user needs and integrated through collaborative development.
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 | 0,023 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».