Impact of Structured Rounding Tools on Time Allocation During Multidisciplinary Rounds: An Observational Study
Bibliographic record
Abstract
BACKGROUND: Recent research has shown evidence of disproportionate time allocation for patient communication during multidisciplinary rounds (MDRs). Studies have shown that patients discussed later during rounds receive lesser time. OBJECTIVE: The aim of our study was to investigate whether disproportionate time allocation effects persist with the use of structured rounding tools. METHODS: Using audio recordings of rounds (N=82 patients), we compared time allocation and communication breakdowns between a problem-based Subjective, Objective, Assessment, and Plan (SOAP) and a system-based Handoff Intervention Tool (HAND-IT) rounding tools. RESULTS: We found no significant linear dependence of the order of patient presentation on the time spent or on communication breakdowns for both structured tools. However, for the problem-based tool, there was a significant linear relationship between the time spent on discussing a patient and the number of communication breakdowns (P<.05)--with an average of 1.04 additional breakdowns with every 120 seconds in discussion. CONCLUSIONS: The use of structured rounding tools potentially mitigates disproportionate time allocation and communication breakdowns during rounds, with the more structured HAND-IT, almost completely eliminating such effects. These results have potential implications for planning, prioritization, and training for time management during MDRs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".