Patient and Public Perceptions of 3D Technologies (Models and Images) to Facilitate Health Care Consultations: Exploratory, Mixed Methods Study
Bibliographic record
Abstract
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".