Objective:

To evaluate the effectiveness of an intelligent clinical decision support system (CDSS) for neonatal hypoglycemia management in mother-infant rooming-in settings, and to dissect the differential hypoglycemia risk conferred by individual high-risk factors and their specific combinations under standardized surveillance.

Approach:

• CDSS Development: A multidisciplinary team developed a knowledge-driven CDSS grounded in national expert consensus, integrating automated risk identification, monitoring reminders, and management recommendations.

Key Findings:

• CDSS implementation significantly reduced hypoglycemia incidence in both pre-post (5.76% vs. 11.88%, P < 0.05) and parallel (5.40% vs. 9.25%, P < 0.05) analyses.
• The overall hypoglycemia incidence in the high-risk cohort under CDSS was 6.3%.
• Preterm birth (15.4%) and low birth weight (25.0%) were identified as having the highest independent risks.
• Risk escalated non-linearly with specific factor combinations, reaching 18.8% in neonates with five concurrent factors.
• Serial monitoring demonstrated a sharp decline in hypoglycemia incidence from 6.1% at first measurement to ≤0.4% thereafter.

Interpretation:

Hypoglycemia risk depends more on the specific types and combinations of high-risk factors than on their quantity alone, providing evidence for precise risk stratification.

Limitations:

• The study was conducted in a single healthcare setting, which may limit generalizability.
• Future multicenter studies incorporating machine learning and long-term neurodevelopmental follow-up are warranted to validate findings.

Conclusion:

The intelligent CDSS effectively reduces neonatal hypoglycemia in rooming-in settings, enhancing clinical standardization and patient safety.