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Assessing Neonatal Intensive Care Unit Structures and Outcomes Before and During the COVID-19 Pandemic: Network Analysis Study.
Mannering, Hannah; Yan, Chao; Gong, Yang; Alrifai, Mhd Wael; France, Daniel; Chen, You.
  • Mannering H; Department of Computer Science, Loyola University, Baltimore, MD, United States.
  • Yan C; Department of Computer Science, Vanderbilt University, Nashville, TN, United States.
  • Gong Y; School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.
  • Alrifai MW; Department of Pediatrics, Vanderbilt University Medical Center, Nashville, TN, United States.
  • France D; Department of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States.
  • Chen Y; Department of Computer Science, Vanderbilt University, Nashville, TN, United States.
J Med Internet Res ; 23(10): e27261, 2021 10 20.
Article in English | MEDLINE | ID: covidwho-1463396
ABSTRACT

BACKGROUND:

Health care organizations (HCOs) adopt strategies (eg. physical distancing) to protect clinicians and patients in intensive care units (ICUs) during the COVID-19 pandemic. Many care activities physically performed before the COVID-19 pandemic have transitioned to virtual systems during the pandemic. These transitions can interfere with collaboration structures in the ICU, which may impact clinical outcomes. Understanding the differences can help HCOs identify challenges when transitioning physical collaboration to the virtual setting in the post-COVID-19 era.

OBJECTIVE:

This study aims to leverage network analysis to determine the changes in neonatal ICU (NICU) collaboration structures from the pre- to the intra-COVID-19 era.

METHODS:

In this retrospective study, we applied network analysis to the utilization of electronic health records (EHRs) of 712 critically ill neonates (pre-COVID-19, n=386; intra-COVID-19, n=326, excluding those with COVID-19) admitted to the NICU of Vanderbilt University Medical Center between September 1, 2019, and June 30, 2020, to assess collaboration between clinicians. We characterized pre-COVID-19 as the period of September-December 2019 and intra-COVID-19 as the period of March-June 2020. These 2 groups were compared using patients' clinical characteristics, including age, sex, race, length of stay (LOS), and discharge dispositions. We leveraged the clinicians' actions committed to the patients' EHRs to measure clinician-clinician connections. We characterized a collaboration relationship (tie) between 2 clinicians as actioning EHRs of the same patient within the same day. On defining collaboration relationship, we built pre- and intra-COVID-19 networks. We used 3 sociometric measurements, including eigenvector centrality, eccentricity, and betweenness, to quantify a clinician's leadership, collaboration difficulty, and broad skill sets in a network, respectively. We assessed the extent to which the eigenvector centrality, eccentricity, and betweenness of clinicians in the 2 networks are statistically different, using Mann-Whitney U tests (95% CI).

RESULTS:

Collaboration difficulty increased from the pre- to intra-COVID-19 periods (median eccentricity 3 vs 4; P<.001). Nurses had reduced leadership (median eigenvector centrality 0.183 vs 0.087; P<.001), and neonatologists with broader skill sets cared for more patients in the NICU structure during the pandemic (median betweenness centrality 0.0001 vs 0.005; P<.001). The pre- and intra-COVID-19 patient groups shared similar distributions in sex (~0 difference), race (4% difference in White, and 3% difference in African American), LOS (interquartile range difference in 1.5 days), and discharge dispositions (~0 difference in home, 2% difference in expired, and 2% difference in others). There were no significant differences in the patient demographics and outcomes between the 2 groups.

CONCLUSIONS:

Management of NICU-admitted patients typically requires multidisciplinary care teams. Understanding collaboration structures can provide fine-grained evidence to potentially refine or optimize existing teamwork in the NICU.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Intensive Care Units, Neonatal / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Humans / Infant, Newborn Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 27261

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Intensive Care Units, Neonatal / COVID-19 Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Topics: Long Covid Limits: Humans / Infant, Newborn Language: English Journal: J Med Internet Res Journal subject: Medical Informatics Year: 2021 Document Type: Article Affiliation country: 27261