Facilitating Teamwork Through Minimal Interaction via a Mesh-based Communication Device in a Clinical Nursing Setting: intervention study (Preprint)
Bibliographic record
Abstract
Background: The prevention of medical errors depends on effective communication and teamwork between nurses, yet traditional nurse call systems create inefficiencies that result in higher staff workloads. Further research is needed to evaluate how digital health care technologies affect nursing workflows and whether they contribute to staff workload. Objective: This study aimed to evaluate whether a smartphone app using Bluetooth Low Energy (BLE) Mesh-based signal communication could enhance nurse-to-nurse communication, decrease response times, and improve teamwork as measured by the TeamSTEPPS Teamwork Perceptions Questionnaire (T-TPQ). Methods: We conducted a between-subjects (independent groups) interventional study on an acute care ward, with 3 intervention days (BLE Mesh app use) and 3 nonintervention days administered on alternating days. Thirty nurses (15 per condition) participated, with no nurse contributing observations to both conditions in the final analysis. Response times were tracked, and the T-TPQ was used to evaluate Situation Monitoring and Mutual Support perceptions at the end of each shift. Results: The app shortened the total response duration from a per-nurse median of 38.5 (IQR 31.0-58.5) seconds in the control condition to 19.5 (IQR 18.5-23.5) seconds in the intervention condition (n=15 per condition; Mann-Whitney U test=217.5; P<.001; rank-biserial r=-0.93). The app reduced the time it took for staff to confirm nursing actions, search for items, and request care assistance. A total of 40 nurses participated, generating 48 questionnaire responses; after exclusions, 30 nurses (15 per condition) were analyzed. T-TPQ confirmed significant improvements in Situation Monitoring (3.04 vs 3.39; P=.001) and Mutual Support (3.01 vs 3.31; P=.003). Post hoc power ranged from 87% to 92% (Cohen d>1.0), supporting the interpretability of these results. Conclusions: BLE Mesh technology reduces basic signal communication delays, which shortens nurse response times and enhances team performance through improved Situation Monitoring and Mutual Support, according to our research. The study demonstrates how this technology delivers functional benefits that enhance nursing communication to produce superior team-based care 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".