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Updated: Jul 15, 2026

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale

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Published on: August 25, 2014

Clinical Team's Needs to Inform an Early Warning System for High-Risk Neonatal Conditions.

Yuying Mo¹, Xiaojuan Liu, Fen Zhen

¹Author Affiliation: Peking University Shenzhen Hospital, Shenzhen, China (Mo, Liu, Zhen, Li, Zhao, and He).

Advances in Neonatal Care : Official Journal of the National Association of Neonatal Nurses

|July 13, 2026

Summary

Developing an intelligent early warning system for high-risk newborns is crucial. This system needs to improve data screening and information support to aid clinical decision-making in neonatal intensive care units (NICUs).

Keywords:

clinical team demand disease identification improvement in monitoring quality intelligent early warning system neonatal intensive care

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Area of Science:

Neonatal care
Clinical informatics
Healthcare system design

Background:

High-risk newborns in neonatal intensive care units (NICUs) often experience subtle clinical deterioration.
Current monitoring methods in NICUs have high false alarm rates and operational burdens.
Effective information screening and automated support are needed for early warning systems in neonatology.

Purpose of the Study:

To explore clinical demand for an intelligent early warning system for high-risk neonates.
To identify obstacles faced by clinical teams in recognizing early condition changes in neonates.
To provide insights for improving clinical strategies and system designs for neonatal care.

Main Methods:

A descriptive, qualitative research approach was employed.
Semi-structured interviews were conducted with 13 healthcare professionals in NICUs.
Directed content analysis was used for systematic data analysis.

Main Results:

Key themes identified include high-risk factor profiling and early warning signals.
Obstacles and challenges in identifying early disease changes were detailed.
Participant expectations for an information-intelligent system's functionalities were outlined.

Conclusions:

Enhance staff training on risk identification for neonatal care.
Upgrade information systems for efficient data flow and timely alerts.
Optimize clinical workflows for rapid response to identified neonatal risks.