87 Validation of a Costing Algorithm in the Neonatal Intensive Care Unit and Identification of Cost Drivers for Neonates. (29th October 2021)

• Record Type: Journal Article
• Authors: Jabbour, Elias; Patel, Sharina; Rios, Juan David; Pechlivanoglou, Petros; Shah, Prakesh; Beltempo, Marc
• Is Part Of: Paediatrics & Child Health. Volume 26: Supplement 1(2021)
• Page Start: e62
• Page End: e64
• Publication Date: 2021-10-29
• Subjects: Pediatrics -- Periodicals; Children -- Health and hygiene -- Periodicals; 618.92
• DOI: 10.1093/pch/pxab061.069
• Languages: English
• ISSNs: 1205-7088

Abstract

Abstract: Primary Subject area: Neonatal-Perinatal Medicine Background: Neonatal Intensive Care Units (NICUs) account for over 35% of pediatric in-hospital clinical costs, thus implying that a better understanding of care expenditures within these units is the first step for improving efficiency of care. The Canadian Neonatal Network (CNN) algorithm is the first to provide case-specific costs based on resource usage among preterm infants born < 37 weeks but has not yet been validated for other populations in the NICU. Objectives: To validate the CNN costing algorithm in six case-mix categories with real-time costs obtained from hospital-specific financial software (CPSS) in a tertiary-level NICU and assess the variations in proportion of cost centers across case-mixes. Design/Methods: A retrospective cohort study of all patients admitted within 24h of birth to a Level 3 medico-surgical NICU 2016-2019. Patient demographics, clinical information and CNN predicted costs were obtained from the CNN database. Real-time costs were obtained from the hospital financial software (CPSS). Total and daily costs were compared between sources using Pearson correlation coefficient (r) and paired Student's t -test. Costs were adjusted to account for inter-institutional and -provincial price variations using the Cost of Standard Hospitalization Stay from the Canadian Institute for Health Information. Proportions of each cost center across the different case-mix categories were compared using Chi-square analyses. Results: Among the 1795 live infants admitted into the NICU, 167 (9.3%) were < 29 weeks gestational age (GA), 193 (11%) were 29-32 weeks GA, 457 (25.5%) were 33-36 weeks GA, 144 (8%) had major congenital anomalies, 179 (10%) were term infants diagnosed with Hypoxic-Ischemic Encephalopathy (HIE) and 672 (37%) were term infants with no HIE or major congenital anomalies. Median NICU costs varied according to each case-mix from $10, 025 for term infants without HIE or congenital anomaly to $180, 145 for infants born < 29 weeks (Figure 1). Despite high variation in total NICU costs, there were small variations in median daily costs (range: $1, 312-$1, 941). Overall, the CNN algorithm strongly correlated with CPSS total costs across all 6 case-mix categories (r range 0.90-1.00, p-value < 0 .01) (Figure 2). We report a consistent strong predictive performance of the algorithm in 5/8 pre-specified cost centers among preterm infants (r range 0.77-0.99, p-value < 0 .01). Unit producing personnel (nurses and physicians) consistently comprised the largest proportion of total costs (64-78%) for all case-mix categories. Conclusion: The CNN algorithm accurately predicts NICU total costs for six case-mix categories. Costs per day were comparable across different case-mix categories, and unit producing personnel represented the highest proportion of costs suggesting that reductions in length of stay would be the most efficient method to reduce NICU costs.