ABSTRACT
Background Neonatal intensive care units (NICU) provide essential medical care to neonates; however, they are associated with hospital-acquired infections, less maternal-newborn bonding, and high costs. Implementing strategies to lower NICU admission rates and shorten NICU length of stay (LOS) is essential. This study uses causal-inference methods to evaluate the impact of care managers using new technology to identify and risk stratify pregnancies on NICU admissions and NICU LOS. The NICU LOS will decrease as a result of the use of new technology by care managers. Study design This retrospective study utilized delivery claims data of pregnant women from the CareFirst BlueCross BlueShield Community Health Plan District of Columbia from 2013 to 2022, which includes the pre-intervention period before the use of new technology by care managers and the post-intervention period with the use of new technology by care managers. Our sample had 4,917 deliveries whose maternal comorbidities were matched with their neonate's outcomes. Methods To evaluate the impact of the technological intervention, both Generalized Linear Models (GLMs) and Bayesian Structural Time-Series (BSTS) models were used. Results Our findings from the GLM models suggest an overall average reduction in the odds of NICU admissions of 29.2% and an average decrease in NICU LOS from 7.5%-58.5%. Using BSTS models, we estimate counterfactuals for NICU admissions and NICU LOS, which suggest an average reduction in 48 NICU admissions and 528 NICU days per year. Conclusion Equipping care managers with better technological tools can lead to significant improvements in neonatal health outcomes as indicated by a reduction in NICU admissions and NICU LOS.
ABSTRACT
INTRODUCTION: Preterm birth poses a significant challenge. This study evaluated a real-time scoring algorithm to identify and stratify pregnancies to indicate preterm birth. METHODS: All claims data of pregnant women were reviewed between 1 January 2014 and 31 October 2018 in Kentucky. RESULTS: A total of 29,166 unique women who were matched to a live newborn were documented, with the pregnancy identified during the first trimester in 54.1% of women. Negative predictive values, sensitivity, and positive likelihood ratios increased from the first to third trimesters as pregnant women who were matched to a live newborn had more visits with their physicians. The area under the receiving-operating characteristics curve on test data classifying preterm birth was 0.59 for pregnancies identified during the first trimester, 0.62 for pregnancies identified in the second trimester, and 0.73 for pregnancies identified in the third trimester. CONCLUSIONS: This study presents a real-time scoring algorithm of indicating preterm birth in the first trimester of gestation which permits stratification of pregnancies to provide more efficient early care management.
