Improving Compliance and Quality of Documentation of Cerebral Function Monitoring in a Neonatal Neurocritical Care Unit
Bibliographic record
Abstract
INTRODUCTION: Neonates admitted to neurocritical care units frequently undergo continuous bedside cerebral function monitoring (CFM). Documentation of CFM findings that are complete and accurate can augment the quality of care through improved communication. We aimed to increase the compliance with and quality of CFM documentation in the electronic medical records by 50% in our neonatal intensive care unit over 6 months. METHODS: We used the Plan-Do-Study-Act methodology, process mapping, and fishbone analysis. We implemented interventions, including the development of standardized EMR templates, face-to-face reminders at staff meetings and clinical handover sessions, and teaching on CFM interpretation. RESULTS: < 0.001). Multimodal reminders to document and educational sessions to increase familiarity with CFM interpretation effectively improved the quality of documentation. CONCLUSIONS: We improved the compliance with and the quality of CFM documentation using targeted quality improvement interventions with case-focused educational sessions, reference tools, and standardized templates. Barriers to compliance with documentation were adverse effects on the workflow that changes in the EMR system may address. A significant challenge to sustainability was the high frequency of rotating trainees. We addressed this challenge by developing mandatory electronic teaching modules that include reminders to document and a case-focused teaching curriculum; to increase awareness of the importance of CFM documentation and increase confidence in CFM interpretation.
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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 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".