The Use of Wireless E-Mail to Improve Healthcare Team Communication
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of using wireless e-mail for clinical communication in an intensive care unit (ICU). DESIGN: The authors implemented push wireless e-mail over a GSM cellular network in a 26-bed ICU during a 6-month study period. Daytime ICU staff (intensivists, nurses, respiratory therapists, pharmacists, clerical staff, and ICU leadership) used handheld devices (BlackBerry, Research in Motion, Waterloo, ON) without dedicated training. The authors recorded e-mail volume and used standard methods to develop a self-administered survey of ICU staff to measure wireless e-mail impact. MEASUREMENTS: The survey assessed perceived impact of wireless e-mail on communication, team relationships, staff satisfaction and patient care. Answers were recorded on a 7-point Likert scale; favorable responses were categorized as Likert responses 5, 6, and 7. RESULTS: Staff sent 5.2 (1.9) and received 8.9 (2.1) messages (mean [SD]) per day during 5 months of the 6-month study period; usage decreased after study completion. Most (106/125 [85%]) staff completed the questionnaire. The majority reported that wireless e-mail improved speed (92%) and reliability (92%) of communication, improved coordination of ICU team members (88%), reduced staff frustration (75%), and resulted in faster (90%) and safer (75%) patient care; Likert responses were significantly different from neutral (p < 0.001 for all). Staff infrequently (18%) reported negative effects on communication. There were no reports of radiofrequency interference with medical devices. CONCLUSIONS: Interdisciplinary ICU staff perceived wireless e-mail to improve communication, team relationships, staff satisfaction, and patient care. Further research should address the impact of wireless e-mail on efficiency and timeliness of staff workflow and clinical outcomes.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".