Usability Evaluation of a Mixed Reality Platform in Pediatric Interventional Cardiology by Specialist Physicians: Mixed Methods Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".