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

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

Published on: August 25, 2014

Neonatal intensive care unit collaboration to decrease hospital-acquired bloodstream infections: from comparative

Joseph Schulman¹, David D Wirtschafter, Paul Kurtin

¹Division of Newborn Medicine, Department of Pediatrics, Weill Cornell Medical College of Cornell University, The New York Presbyterian Hospital, 525 East 68th Street, Box 106, NY 10065, USA. jos2039@med.cornell.edu

Pediatric Clinics of North America

|August 8, 2009

Summary

This summary is machine-generated.

This guide explains data-driven clinical performance evaluation and improvement networks. It details how to make fair comparisons and reduce central line-associated bloodstream infections in neonatal intensive care units (NICUs).

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Last Updated: Jun 21, 2026

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

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

Published on: August 25, 2014

Area of Science:

Healthcare Management
Quality Improvement Science
Clinical Informatics

Background:

Data-driven clinical performance evaluation is crucial for healthcare quality.
Existing methods for comparing providers can be complex and require refinement.
Collaborative networks show promise for improving patient outcomes.

Purpose of the Study:

To provide a guide for data-driven clinical performance evaluation.
To describe two statewide improvement networks focused on reducing central line-associated bloodstream infections (CLABSIs).
To offer practical tools and insights for developing similar quality improvement networks.

Main Methods:

Part 1: Examination of principles for fair comparisons among healthcare providers.
Part 2: Description of the development and outcomes of two CLABSI reduction collaboratives in neonatal intensive care units (NICUs).
Case study analysis of established and developing improvement networks.

Main Results:

Successful reduction of CLABSIs in neonatal ICUs in California through a data-driven collaborative.
Development of a similar collaborative in New York State to address CLABSIs.
Identification of key concepts and tools for effective provider comparisons and network development.

Conclusions:

Data-driven comparisons and collaborative networks are effective strategies for improving clinical performance.
The described networks offer a practical model for reducing healthcare-associated infections, particularly CLABSIs in neonatal populations.
The findings have broad applicability for pediatric quality improvement initiatives.