20 Eat Sleep Console: A Quality Improvement Initiative to Improve the Care of Opioid Exposed Newborns at a Community Hospital
Bibliographic record
Abstract
Abstract Background As the opioid crisis continues in B.C., more infants are born to mothers using opioids during pregnancy. Many of these newborns experience neonatal opioid withdrawal syndrome (NOWS) and require specialized medical care and prolonged hospital stays. Objectives We sought to improve the care of opioid exposed newborns by replacing the Finnegan Neonatal Abstinence Scoring System (FNASS) with the Eat Sleep Console (ESC) protocol. Our aim was to reduce infant NICU length of stay (LOS) and morphine dosing required for withdrawal management. Design/Methods A multi-disciplinary team was formed in 2019 and standard quality improvement methodology was implemented using plan-do-study-act cycles. Interventions included NOWS education for maternity and newborn health care providers, pediatric antenatal consults, introduction of as needed morphine, and implementation of the Eat Sleep Console (ESC) assessment tool. Primary outcomes were number of NICU admission days and cumulative morphine dosing required for NOWS treatment. Data from chart reviews was obtained from 2017 to 2021. Qualitative survey data was collected from nursing staff pre and post ESC implementation. Results ESC successfully replaced the FNASS, and nursing staff reported overall satisfaction and confidence with the practice change. Forty-four infants were captured in our data analysis, including 21 from the baseline period (FNASS), 12 from intervention group 1 (PRN morphine and FNASS), and 9 from intervention group 2 (PRN morphine and ESC). Cumulative morphine dose decreased by 55% when comparing the two intervention groups (19.45 mg intervention group 1 vs 10.71 mg intervention group 2). Two infants were managed exclusively with ESC non-pharmacological strategies and did not require any morphine. Although NICU LOS increased over time in our project (10.79 days in the baseline group vs 19.64 in the intervention group 2), there were no differences in the LOS between the two intervention groups (18.49 days vs 19.64 days). Unstable maternal opioid use, defined as illicit opioid use two weeks prior to delivery increased as the project progressed and was unbalanced between groups, possibly affecting our overall outcomes (38% unstable mothers in the baseline period vs 78% intervention group 2). Stratified analysis between stable and unstable mothers showed a trend of increased LOS and morphine requirements for infants born to unstable mothers. In our center, the ability to provide 1:1 care by nurse or parent was the most prominent challenge. Conclusion ESC successfully replaced the FNASS at our community hospital and has shown a clinically significant decrease in morphine requirements for infants experiencing opioid withdrawal.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".