Improving Efficiency of Multidisciplinary Bedside Rounds in the NICU: A Single Centre QI Project
Bibliographic record
Abstract
INTRODUCTION: Inconsistent workflow, communication, and role clarity generate inefficiencies during bedside rounds in a neonatal intensive care unit. These inefficiencies compromise the time needed for essential activities and result in reduced staff and family satisfaction. This study's primary aim was to reduce the mean duration of bedside rounds by 25% within 3 months by redesigning the rounding processes and applying QI principles. The secondary aims were to improve staff and family experience. METHODS: We conducted this work in an academic 50-bed neonatal intensive care unit involving 350 staff members. The change interventions included: (i) reinforcing essential value-added activities like standardizing rounding time, the sequencing of patients rounded, sequencing each team member rounding presentations, team preparation, bedside presentation content, and time management; (ii) reducing non-value-added activities; and (iii) moving value-added nonessential activities outside of the rounds. RESULTS: The mean duration of rounds decreased from 229 minutes in the pre-implementation to 132 minutes in the postimplementation phase. The proportion of staff showing satisfaction regarding various components of the rounds increased from 5% to 60%, and perceived staff involvement during the rounds increased from 70% to 77%. Ninety-three percent of family experience survey respondents expressed satisfaction at being invited for bedside reporting and being involved in decision-making or care planning. The staff did not report any adverse events related to the new rounds process. CONCLUSION: Redesigning bedside rounds improved staff engagement and workflow, resulting in efficient rounds and better staff experience.
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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) | 0.000 | 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) | 0.000 | 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".