Validation of a Costing Algorithm and Cost Drivers for Neonates Admitted to the Neonatal Intensive Care Unit

Area of Science:

• Neonatal Medicine
• Health Economics
• Healthcare Management

Background:

• Neonatal intensive care units (NICUs) represent a significant portion of pediatric healthcare expenditures.
• Understanding NICU cost drivers is crucial for optimizing resource allocation and improving financial efficiency.

Purpose of the Study:

• To validate the Canadian Neonatal Network (CNN) costing algorithm against actual costs in a tertiary NICU.
• To identify key factors influencing NICU expenditures across different patient case-mix groups.

Main Methods:

• A retrospective cohort study analyzed data from 1,795 infants admitted to a Level-3 NICU between 2016 and 2019.
• Patient data and predicted costs from the CNN database were compared with actual costs from the hospital's accounting system.
• Spearman correlation was used to assess the agreement between predicted and actual costs.

Main Results:

• The study included diverse patient populations, from very preterm infants (<29 weeks GA) to term infants with specific conditions.
• Median total NICU costs ranged widely, from $6,267 for term infants to $211,103 for infants <29 weeks GA.
• The CNN algorithm demonstrated strong correlations (rho 0.78-0.98) between predicted and actual costs across all case-mix groups.

Conclusions:

• The CNN costing algorithm is a validated tool for accurately predicting total NICU costs for various patient case-mix groups.
• Personnel costs, primarily physician and nursing salaries, constitute the largest component (65-82%) of overall NICU expenditures.
• Very preterm infants (<33 weeks gestation) are the primary consumers of NICU resources.