A smartphone‐enabled communication system to improve hospital communication: Usage and perceptions of medical trainees and nurses on general internal medicine wards
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in the use of information and communication technologies to improve how clinicians communicate in hospital settings. METHODS: We implemented a communication system with support for physician handover and secure messaging on 2 general internal medicine wards. We measured usage and surveyed physicians and nurses on perceptions of the system's effects on communication. RESULTS: Between May 2011 and August 2012, a clinical teaching team received, on average, 14.8 messages per day through the system. Messages were typically sent as urgent (69.1%) and requested a text reply (76.5%). For messages requesting a text reply, 8.6% did not receive a reply. For those messages that did receive a reply, the median response time was 2.3 minutes, and 84.5% of messages received a reply within 15 minutes. Of those who completed the survey, 95.3% were medical residents (82 of 86) and 81.7% were nurses (83 of 116). Medical trainees (82.8%) and nursing staff (78.3%) agreed or strongly agreed that the system helped to speed up their daily work tasks. Overall, 67.1% of the trainees and 73.2% of nurses agreed or strongly agreed that the system made them more accountable in their clinical roles. Only 35.8% of physicians and 26.3% of nurses agreed or strongly agreed that the system was useful for communicating complex issues. CONCLUSIONS: In summary, with a system designed to improve communication, we found that there was high uptake and that users perceived that the system improved efficiency and accountability but was not appropriate for communicating complex issues.
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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 | 0.003 | 0.010 |
| 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) | 0.002 | 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".