Do Emergency Physicians and Medical Students Find It Unethical to ‘Look up’ Their Patients on Facebook or Google?
Bibliographic record
Abstract
INTRODUCTION: The use of search engines and online social media (OSM) websites by healthcare providers is increasing and may even be used to search for patient information. This raises several ethical issues. The objective of this study is to evaluate the prevalence of OSM and web-searching for patient information and to explore attitudes towards the ethical appropriateness of these practices by physicians and trainees in the emergency department (ED). METHODS: We conducted an online survey study of Canadian emergency physicians and trainees listed under then Canadian Association of Emergency Physicians (CAEP) and senior medical students at the University of Toronto. RESULTS: We received 530 responses (response rate 49.1%): 34.9% medical students, 15.5% residents, 49.6% staff physicians. Most had an active Facebook account (74%). Sixty-four participants (13.5%) had used Google to research a patient and 10 (2.1%) had searched for patients on Facebook. There were no differences in these results based on level of training, and 25% of physicians considered using Facebook to learn about a patient "very unethical." The most frequent ethical concerns were with violation of patient confidentiality, dignity, and consent. The practice was usually not disclosed to patients (14%), but often disclosed to senior colleagues (83%). CONCLUSION: This is the first study examining the prevalence of and attitudes towards online searching for obtaining patient information in the ED. This practice occurs among staff physicians and trainees despite ethical concerns. Future work should explore the utility and desirability of searching for patient information online.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".