Improving Medical Photography in a Level 1 Trauma Center by Implementing a Specialized Smartphone-Based App in Comparison to the Usage of Digital Cameras: Prospective Panel Study
Bibliographic record
Abstract
BACKGROUND: Medical photography plays a pivotal role in modern health care, serving multiple purposes ranging from patient care to medical documentation and education. Specifically, it aids in wound management, surgical planning, and medical training. While digital cameras have traditionally been used, smartphones equipped with specialized apps present an intriguing alternative. Smartphones offer several advantages, including increased usability and efficiency and the capability to uphold medicolegal standards more effectively and consistently. OBJECTIVE: This study aims to assess whether implementing a specialized smartphone app could lead to more frequent and efficient use of medical photography. METHODS: We carried out this study as a comprehensive single-center panel investigation at a level 1 trauma center, encompassing various settings including the emergency department, operating theaters, and surgical wards, over a 6-month period from June to November 2020. Using weekly questionnaires, health care providers were asked about their experiences and preferences with using both digital cameras and smartphones equipped with a specialized medical photography app. Parameters such as the frequency of use, time taken for image upload, and general usability were assessed. RESULTS: A total of 65 questionnaires were assessed for digital camera use and 68 for smartphone use. Usage increased significantly by 5.4 (SD 1.9) times per week (95% CI 1.7-9.2; P=.005) when the smartphone was used. The time it took to upload pictures to the clinical picture and archiving system was significantly shorter for the app (mean 1.8, SD 1.2 min) than for the camera (mean 14.9, SD 24.0 h; P<.001). Smartphone usage also outperformed the digital camera in terms of technical failure (4.4% vs 9.7%; P=.04) and for the technical process of archiving (P<.001) pictures to the picture archiving and communication system (PACS) and display images (P<.001) from it. No difference was found in regard to the photographer's intent (P=.31) or reasoning (P=.94) behind the pictures. Additionally, the study highlighted that potential concerns regarding data security and patient confidentiality were also better addressed through the smartphone app, given its encryption capabilities and password protection. CONCLUSIONS: Specialized smartphone apps provide a secure, rapid, and user-friendly platform for medical photography, showing significant advantages over traditional digital cameras. This study supports the notion that these apps not only have the potential to improve patient care, particularly in the realm of wound management, but also offer substantial medicolegal and economic benefits. Future research should focus on additional aspects such as patient comfort and preference, image resolution, and the quality of photographs, as well as seek to corroborate these findings through a larger sample size.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".