The use of smartphones for clinical communication on internal medicine wards
Bibliographic record
Abstract
BACKGROUND: Communication between clinicians is hampered by the frequent difficulty in reaching the most responsible physician for a patient as well as the use of outdated methods such as numeric paging. The aim of this study was to evaluate the use of smartphones to improve communication on internal medicine wards. METHOD: At the Toronto General Hospital, residents were provided with smartphones. To simplify reaching the most responsible resident for a patient, a smartphone designated as "Team BlackBerry" was also carried by each senior resident and then passed to the resident covering the team at night and on weekends. Nurses were able to send email messages or call smartphones directly. RESULTS: There were on average of 9.1 incoming calls, 6.6 outgoing calls, 14.3 received emails, and 2.8 sent emails per day to each Team BlackBerry. Team BlackBerrys received up to 35 calls and 57 emails per day. Residents strongly preferred the smartphones over conventional paging with perceived improvements in all items measured and felt that it improved efficiency and communication. Although nurses perceived a reduction in the time required to contact a physician (27.6 vs. 11 minutes P < 0.001), their overall satisfaction with physician's response time for urgent issues did not improve significantly. DISCUSSION: When smartphones were used for clinical communication, residents perceived an improvement in communication with them. Residents strongly preferred emails as opposed to telephone calls as the prime method of communication. Further objective evaluation is necessary to determine if this intervention improves efficiency and more importantly, quality of care.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".