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 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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".