Balancing the Need for Clinical Photography With Patient Privacy Issues: The Search for a Secure SmartPhone Application to Take and Store Clinical Photographs
Bibliographic record
Abstract
BACKGROUND: Physicians are increasingly using smartphones to take clinical photographs. This study evaluates a smartphone application for clinical photography that prioritizes and facilitates patient security. METHODS: Ethics approval was obtained to trial a smartphone clinical photography application, PicSafe Medi. Calgary plastic surgeons and residents used the application to obtain informed consent and photograph patients. Surveys gauging the application's usability, consent process, and photograph storage/sharing were then sent to surgeons and patients. RESULTS: Over a 6-month trial period, 15 plastic surgeons and residents used the application to photograph 86 patients. Over half of the patients (57%) completed the survey. The majority of patients (96%) were satisfied with the application's consent process, and all felt their photographs were secure. The majority (93%) of surgeons/residents completed the survey. The application was felt to overcome issues with current photography practices: inadequate consent and storage of photographs (100%), risk to patient confidentiality (92%), and unsecure photograph sharing (93%). Barriers to regular use of the application included need for cellphone service/Internet (54%), sanitary concerns due to the need for patients to sign directly on the phone (46%), inability to obtain proactive/retroactive consent (85%), and difficulty viewing photographs (80%). The majority of surgeons (85%) believe a smartphone application would be suitable for clinical patient photography, but due to its limitations, only 23% would use the trialed application. CONCLUSIONS: A smartphone clinical photography application addresses the patient confidentiality risks of current photography methods; however, limitations of the trialed application prevent its broad implementation.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".