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Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study

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 es...

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Autores principales: Shields, Lisa B, Davydov, Yevgeniy, Glyder, Angela, Weymouth, Clayton, Udwin, Michael, Eakins, Matt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cureus 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362942/
https://www.ncbi.nlm.nih.gov/pubmed/37485146
http://dx.doi.org/10.7759/cureus.40813
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author Shields, Lisa B
Davydov, Yevgeniy
Glyder, Angela
Weymouth, Clayton
Udwin, Michael
Eakins, Matt
author_facet Shields, Lisa B
Davydov, Yevgeniy
Glyder, Angela
Weymouth, Clayton
Udwin, Michael
Eakins, Matt
author_sort Shields, Lisa B
collection PubMed
description 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.
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spelling pubmed-103629422023-07-23 Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study Shields, Lisa B Davydov, Yevgeniy Glyder, Angela Weymouth, Clayton Udwin, Michael Eakins, Matt Cureus Obstetrics/Gynecology 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. Cureus 2023-06-22 /pmc/articles/PMC10362942/ /pubmed/37485146 http://dx.doi.org/10.7759/cureus.40813 Text en Copyright © 2023, Shields et al. https://creativecommons.org/licenses/by/3.0/This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Obstetrics/Gynecology
Shields, Lisa B
Davydov, Yevgeniy
Glyder, Angela
Weymouth, Clayton
Udwin, Michael
Eakins, Matt
Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study
title Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study
title_full Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study
title_fullStr Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study
title_full_unstemmed Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study
title_short Impact of Technology on Neonatal Intensive Care Unit Admissions and Length of Stay: A Retrospective Study
title_sort impact of technology on neonatal intensive care unit admissions and length of stay: a retrospective study
topic Obstetrics/Gynecology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10362942/
https://www.ncbi.nlm.nih.gov/pubmed/37485146
http://dx.doi.org/10.7759/cureus.40813
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