Patient and Public Perceptions of 3D Technologies (Models and Images) to Facilitate Health Care Consultations: Exploratory, Mixed Methods Study
Notice bibliographique
Résumé
Background: 3D technology, including models and images, can facilitate health care consultations by promoting a better understanding of information by patients and shared decision-making. However, little is yet known about the general public's perspectives about the acceptability of such innovative technology and how it can best be adopted into routine health care consultations. There is a need to explore both public and patient perceptions to avoid the risk of implementing 3D technologies that may not be acceptable or fit-for-purpose. Objective: This paper aimed to explore the patient and public perceptions of the use of 3D technology during health care consultations. Methods: This study adopted a citizen science approach using mixed methods to conduct (1) a short web-based survey with members of the public to gather a wide range of opinions regarding the use of various technologies for health care consultations; (2) a longer web-based survey to explore perceived barriers and opportunities people report specifically on the use of 3D technology; and (3) telephone interviews with patients who recently used 3D technology as part of their health care consultations. Results: A total of 211 participants completed the short survey, of which 25 went on to complete the longer survey. While members of the public were familiar with using various types of technologies during remote consultations, most participants did not have experience with using 3D technology. However, people reported that they could see the potential benefits of such technology to facilitate health care consultations. They expressed positive perceptions toward how this might assist in comprehension of a diagnosis and discussion of alternative treatment plans. They also mentioned potential benefits in relation to communication and shared decision-making either with their health care provider or with their friends and family. These potential benefits were confirmed through telephone interviews with 4 patients who also stressed potential barriers such as emotional distress caused by an overload of information as important considerations for wider implementation. Overall, there was a strong interest and willingness to use 3D technology in future health care consultations. Conclusions: The use of 3D technology in health care settings is now an option, but there is little research to date on how patients and the wider public might benefit from this. This mixed methods study has shown that people are accepting of 3D technology being used in health care consultations and that there might be real benefits to the patient. These include improved individual and shared decision-making around their treatment through the technology, making disease and treatment options easier to understand for patients. Since 3D technology can still be expensive, the benefits to the patient and health care professionals need to be captured and quantified in terms of reduced travel, efficient use of time, and overall better quality of care and clinical outcomes.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».