Avoiding Breach of Patient Confidentiality: Trial of a Smartphone Application That Enables Secure Clinical Photography and Communication
Bibliographic record
Abstract
BACKGROUND: To evaluate a smartphone application for clinical photography that prioritizes and facilitates patient security. METHODS: Ethics was obtained to trial the application Sharesmart. Calgary plastic surgeons/residents used the application for clinical photography and communication. Surveys gauging the application usability, incorporated consent process, and photograph storage/sharing were then sent to surgeons and patients. RESULTS: Over a 1-year trial period, 16 Calgary plastic surgeons and 24 residents used the application to photograph 84 patients. Half (56%) of the patients completed the survey. The majority of patients found the applications consent process acceptable (89%) and felt their photograph was secure (89%). Half (51%) of the surgeons/residents completed the survey and would use the application as is (67%) or with modifications (33%). The consent process was felt to be superior (73%) or equivalent (23%) to participant's prior methods and was felt to resolve issues present with current photography practices of secure transmission and storage of photographs by 100% and 95% of respondents, respectively. Perceived limitations of the application included difficulties in use with poor cellphone service or Internet, decreased speed compared to current practices, the lack of a desktop platform, video capability, and ability to transmit the photograph directly to the patient's medical record. CONCLUSIONS: A smartphone clinical photography application addresses the risks of patient confidentiality breach present with current photography methods; broad implementation should be considered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".