Restructuring Care Teams Within a Neonatal Intensive Care Unit
Bibliographic record
Abstract
Abstract BACKGROUND: Organizing care teams in a large neonatal intensive care unit (NICU) is a challenge. In our pod-based model, babies were assigned a care team based on acuity and bed location. They were frequently moved between teams to accommodate nursing assignments, causing an imbalance in patient census and acuity across teams. As part of a larger process improvement project, we implemented and studied an alternate model for assigning patients to a care team. OBJECTIVES: The objective of this project was to improve consistency of patient care and to balance the workload across the three care teams in the NICU. DESIGN/METHODS: The setting is a 69 bed tertiary teaching NICU with approximately 1300 admissions a year. Three clinical teams share day to day assignment of a combination of these level III and level II pods. A multidisciplinary subgroup conducted a two hour Kaizen (brain storming) event with a larger group of stake-holders during which the decision was made to assign babies to a care team based on current workload of each team. The care teams follow each patient from admission to discharge, regardless of the baby’s location within the unit instead of moving babies between teams. Education communication, feedback strategies regarding the process change were formulated and executed by the sub-group. The new method was piloted for a period of three months. Objective data was collected regarding patient movement, patient acuity, census balance, and rounds time. Qualitative data was collected through staff and family surveys. ignments, causing an imbalance in patient census and acuity across teams. As part of a larger process improvement project, we implemented and studied an alternate model for assigning patients to a care team. RESULTS: Forty percent of babies admitted to the NICU crossed care teams during their stay prior to the process change while 0.3% changed teams after the change. The number of moves per patient decreased from 1.4 to 1.27. The variability in both census and acuity was diminished following implementation of the changes. The daily average number of man-hours to complete daily rounds decreased from 47.5 before the change to 40.5 after the change. There was a 35% response rate to the staff survey with an overall positive response to the changes with regards to improving the patient and family experience. The family satisfaction survey showed a trend toward increased satisfaction following the change. CONCLUSION: Process improvement methods can be used to successfully change how care teams are structured in a tertiary NICU.
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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 | 0.001 |
| 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 | 0.001 |
| 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".