The Double-Edged Sword of Clinical Decision Support in Labor & Delivery: A Systematic Review of its Impact on Nursing Judgment
Bibliographic record
Abstract
Background: The integration of Clinical Decision Support (CDS) systems in Electronic Health Records (EHRs) has become the cornerstone of modern obstetric practice, aimed at standardizing and improving patient safety. In the high-stakes environment of Labor and Delivery (L&D), CDS tools, specifically for fetal heart rate (FHR) interpretation and oxytocin administration, are widely used in practice. These systems exert a profound influence on L&D nurses' workflow and clinical decision-making, as they are the primary agents of continuous patient monitoring. Aim: This review synthesizes the literature from 2015 to 2024 to explore the multifaceted impact of EHR-embedded CDS on nursing judgment, specifically on its effect on nursing autonomy, patient safety, and the phenomenon of alert fatigue. Methods: A narrative review was conducted by searching the databases PubMed, CINAHL, and Web of Science. Search terms were "clinical decision support," "nursing," "labor and delivery," "fetal heart rate," "oxytocin," "patient safety," "autonomy," and "alert fatigue." Results: The findings show a complex and often conflicting interplay between CDS and nursing practice. CDS systems can enhance safety by providing an organized framework for FHR assessment and imposing evidence-based oxytocin protocols, thus leading to a reduction in adverse events. They can, at the same time, erode nursing autonomy by promoting algorithmic thought, deskilling, and replacing holism in clinical judgment. Furthermore, high levels of non-actionable or excessively sensitive alerts are one of the biggest contributors to alert fatigue, which consequently leads to workarounds, desensitization, and safety issues that eliminate the intended benefits. Conclusion: CDS in L&D is a double-edged sword. Its optimal application depends on a human-factors design that produces systems to support, rather than supplant, the nurse's critical thinking. Strategies need to address escalating alert specificity, smoothly integrating CDS into nursing workflow, and building a culture in which technology supplements, but never substitutes for, expert nursing judgment. Safe obstetric care in the future hinges on a complementary partnership of nurse intuition and computerized intelligence.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".