Analysis of Smartphone Interruptions on Academic General Internal Medicine Wards
Bibliographic record
Abstract
INTRODUCTION: Hospital-based medical services are increasingly utilizing team-based pagers and smartphones to streamline communications. However, an unintended consequence may be higher volumes of interruptions potentially leading to medical error. There is likely a level at which interruptions are excessive and cause a 'crisis mode' climate. METHODS: We retrospectively collected phone, text messaging, and email interruptions directed to hospital-assigned smartphones on eight General Internal Medicine (GIM) teams at two tertiary care centres in Toronto, Ontario from April 2013 to September 2014. We also calculated the number of times these interruptions exceeded a pre-specified threshold per hour, termed 'crisis mode', defined as at least five interruptions in 30 minutes. We analyzed the correlation between interruptions and date, site, and patient volumes. RESULTS: A total of 187,049 interruptions were collected over an 18-month period. Daily weekday interruptions rose sharply in the morning, peaking between 11 AM to 12 PM and measuring 4.8 and 3.7 mean interruptions/hour at each site, respectively. Mean daily interruptions per team totaled 46.2 ± 3.6 at Site 1 and 39.2 ± 4.2 at Site 2. The 'crisis mode' threshold was exceeded, on average, 2.3 times/day per GIM team during weekdays. In a multivariable linear regression analysis, site (β6.43 CI95% 5.44 - 7.42, p<0.001), day of the week (with Friday having the most interruptions) (β0.481 CI95% 0.236 - 0.730, p<0.05) and patient census (β1.55 CI95% 1.42 - 1.67, p<0.05) were all predictive of daily interruption volume although there was a significant interaction effect between site and patient census (β-0.941 CI95% -1.18 - -0.703, p<0.05). CONCLUSION: Interruptions were related to site-specific features, including volume, suggesting that future interventions should target the culture of individual hospitals. Excessive interruptions may have implications for patient safety especially when exceeding a maximal threshold over short periods of time.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".