Is it time to rethink how we page physicians? Understanding paging patterns in a tertiary care hospital
Bibliographic record
Abstract
BACKGROUND: Frequent pages can disrupt workflow, interrupt patient care, and may contribute to physician burnout. We hypothesized that paging volumes followed consistent temporal trends, regardless of the medical or surgical service, reflecting systems based issues present in our hospitals. METHODS: A retrospective review of the hospital paging systems for 4 services at The Ottawa Hospital was performed. Resident paging data from April 1 to July 31, 2018 were collected for services with a single primary pager number including orthopaedic surgery, general surgery, neurology, and neurosurgery. Trends in paging volume during the 4-month period were examined. Variables examined included the location of origin of the page (emergency room vs. inpatient unit), and day/time of the page. RESULTS: During the study period, 25,797 pages were received by the 4 services, averaging 211 (± Standard Deviation (SD) 12) pages per day. 19,371 (75%) pages were from in-patient hospital units, while 6426 (24%) were pages from the emergency room. The median interval between pages across all specialties was 22:30 min. Emergency room pages peaked between 16:30 and 20:00, while in-patient units peaked between 17:30 and 18:30. CONCLUSIONS: Each service experienced frequent paging with similar patterns of marked increases at specific times. This study identifies areas for future study about what the factors are that contribute to the paging patterns observed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".