A Matter of Urgency: Reducing Clinical Text Message Interruptions During Educational Sessions
Bibliographic record
Abstract
BACKGROUND: Text messaging is increasingly replacing paging as a tool to reach physicians on medical wards. However, this phenomenon has resulted in high volumes of nonurgent messages that can disrupt the learning climate. OBJECTIVE: Our objective was to reduce nonurgent educational interruptions to residents on general internal medicine. DESIGN, SETTING, PATIENTS: This was a quality improvement project conducted at an academic hospital network. Measurements and interventions took place on 8 general internal medicine inpatient teaching teams. INTERVENTION: Interventions included (1) refining the clinical communication process in collaboration with nursing leadership; (2) disseminating guidelines with posters at nursing stations; (3) introducing a noninterrupting option for message senders; (4) audit and feedback of messages; (5) adding an alert for message senders advising if a message would interrupt educational sessions; and (6) training and support to nurses and residents. MEASUREMENTS: Interruptions (text messages, phone calls, emails) received by institution-supplied team smartphones were tracked during educational hours using statistical process control charts. A 1-month record of text message content was analyzed for urgency at baseline and following the interventions. RESULTS: The interruption frequency decreased from a mean of 0.92 (95% CI, 0.88 to 0.97) to 0.59 (95% CI, 0.51 to0.67) messages per team per educational hour from January 2014 to December 2016. The proportion of nonurgent educational interruptions decreased from 223/273 (82%) messages over one month to 123/182 (68%; P < .01). CONCLUSIONS: Creation of communication guidelines and modification of text message interface with feedback from end-users were associated with a reduction in nonurgent educational interruptions. Continuous audit and feedback may be necessary to minimize nonurgent messages that disrupt educational sessions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".