34: Nursing Overtime Increases the Risk of Medical Incidents in the NICU
Bibliographic record
Abstract
Adult studies have shown that increased fatigue in workers is associated with a higher risk of error. Medical incidents are preventable causes of adverse events in the hospital setting. To our knowledge, no study has assessed the impact of nurse overtime on the risk of medical incidents in the neonatal intensive care unit (NICU). The objective of this study was to assess the impact of nurse overtime on the risk of medical incidents on all infants hospitalised in the NICU. We conducted a retrospective study on all infants (n=7473) admitted in the CHU de Québec NICU (capacity of 51 beds) from April 1, 2008 to March 31, 2013. Administrative data (overtime hours per day) was obtained from the database Logibec, patient information was obtained from Med-Echo and information on medical incidents was obtained from the local incident reporting database Gesrisk. We assessed the association between administrative data and patient outcomes by using logit and probit models. Two-sample test of proportions and t test were used to assess risk factors. The mean total of worked overtime was 22.7±20.7 h. The average overtime as percentage of total daily hours of work was 4.0±3.4%. There were a total of 601 medical incidents that were reported during the study period. The most common categories of incidents were related to medication (78.9%), feeding (7.7%) and treatment (7.1%). On average, incidents happened on day of life 9.5±1.1. A total of 428 (5.7%) patients had at least one medical incident. Patients who had a medical incident had significantly smaller gestational age (32.6±0.25 weeks compared to 36.3±0.1 weeks; P<0.001)) and had a smaller birth weight (2022.2±55.3 g compared to 2786.7±10.7 g; P<0.001). Days of higher overtime (expressed as percentage of total worked hours) were significantly associated with an increased risk of medical incidents (P=0.02). Adjusted risk of suffering from a medical incident was significantly higher on days of high overtime (>8% of all hours worked) (OR=1.34; P=0.03). On days of very high overtime (>12% of all hours worked), the risk of medical incidents was greatest (OR= 1.62; P=0.045). In our study, periods of high overtime were significantly associated with an increased risk of medical incidents in the NICU. Preterm infants had the highest risk of having a medical incident. This suggests that re-organising the medical workforce to reduce nursing overtime should become an integral part in improving patient care and reducing risk of medical errors in the NICU.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".