Optimal Crash Cart Configuration for a Surgical NICU
Bibliographic record
Abstract
BACKGROUND: Neonates admitted to cardiac and surgical neonatal intensive care units (NICUs) are at an increased risk of requiring emergency lifesaving interventions that require the use of both Neonatal Resuscitation Program (NRP) and Pediatric Advanced Life Support (PALS) algorithms. Clinicians working within the surgical NICU must be able to access emergency equipment and medications quickly in order to respond to critical situations. A crash cart that integrates human factors principles and supports both the NRP and PALS algorithms is necessary to promote patient safety for this high-risk population. PURPOSE: A multidisciplinary quality improvement project constructed an optimal crash cart configuration that embedded human factors principles and supported clinical workflow by reflecting both the NRP and the PALS algorithms in an NICU that cares for cardiac and surgical patients. METHODS: A crash cart working group including frontline NICU staff, simulation experts, and a human factors specialist was formed within a surgical NICU. Human factors principles were utilized to align the organization of the cart with the NRP and PALS algorithms to increase the efficiency and intuitiveness of the cart. The new crash cart configuration was usability tested through simulation, revised on the basis of clinical feedback, and then implemented in a clinical setting. Data were collected following implementation of the new crash cart to validate that the new configuration was viewed as a significant improvement. The Plan-Do-Study-Act cycle was used to make improvements and capture outcome indicators. RESULTS: Evaluation data collected both during usability simulation testing and in situ within the NICU clinical environment indicated that the revised crash cart scored higher on Likert scale response questions than the previous crash cart. IMPLICATIONS FOR PRACTICE: Human factors science, in combination with frontline user engagement, should be utilized to create intuitive crash cart configurations, which are then tested in a simulation environment and evaluated in situ in the NICU. IMPLICATIONS FOR RESEARCH: Further research around crash cart design within NICUs that use multiple lifesaving algorithms would add to the paucity of research around the impact of human factors theory in the utilization of lifesaving equipment and medications within this specific population.
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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) | 0.000 | 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".