Medical Students and Personal Smartphones in the Clinical Environment: The Impact on Confidentiality of Personal Health Information and Professionalism
Bibliographic record
Abstract
BACKGROUND: Smartphones are becoming ubiquitous in health care settings. The increased adoption of mobile technology such as smartphones may be attributed to their use as a point-of-care information source and to perceived improvements in clinical communication and efficiency. However, little is known about medical students' use of personal smartphones for clinical work. OBJECTIVE: The intent of the study was to examine final-year medical students' experience with and attitudes toward using personal mobile technology in the clinical environment, with respect to the perceived impact on patient confidentiality and provider professionalism. METHODS: Cross-sectional surveys were completed by final-year medical students at the University of Toronto. Respondents were asked about the type of personal mobile phone they use, security features on their personal phone, experiences using their personal phone during clinical rotations, and attitudes about using their personal phone for clinical work purposes. RESULTS: The overall response rate was 45.4% (99/218). Smartphone ownership was prevalent (98%, 97/99) with the majority (86%, 85/99) of participants using their personal phones for patient-related communication during clinical rotations. A total of 26% (26/99) of participants reported not having any type of security feature on their personal phone, 94% (90/96) of participants agreed that using their personal phone for clinical work makes them more efficient, and 86% (82/95) agreed that their personal phone allows them to provide better patient care. Although 68% (65/95) of participants believe that the use of personal phones for patient-related communication with colleagues poses a risk to the privacy and confidentiality of patient health information, 22% (21/96) of participants still use their personal phone to text or email identifiable patient data to colleagues. CONCLUSIONS: Our findings suggest that the use of personal smartphones for clinical work by medical students is prevalent. There is a need to more fully address the threat to patient confidentiality posed by the use of unsecured communication devices such as smartphones.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".