The Structure of Critical Care Nursing Teams and Patient Outcomes: A Network Analysis

Costa, Deena Kelly; Liu, Haiyin; Boltey, Emily M; Yakusheva, Olga

doi:10.1164/rccm.201903-0543le

Cited by 19 publications

(10 citation statements)

References 7 publications

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“…In addition, in studies by our team, when nurse assignments were nonrandom, better-prepared, qualified nurses tended to be assigned to sicker patients ( 8 )—a negative bias that works to weaken the results of our findings. However, we agree that unobserved selection could be confounding our findings, particularly considering the exploratory nature of our study; we acknowledged unobserved selection bias in our limitations section ( 1 ). In addition, we are unable to adjust or account for patient acuity measures, such as the Acute Physiology and Chronic Health Evaluation score ( 9 ), and therefore we are unfortunately unable to examine mortality risk and nurse network positions, as suggested by Chen and colleagues.…”

supporting

confidence: 55%

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Reply to Chen et al.: Network Analysis Subtleties in ICU Structures and Outcomes

Costa

Liu

Boltey

et al. 2020

Am J Respir Crit Care Med

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supporting

confidence: 55%

“…Thank you to Dr. Chen and colleagues for their thoughtful letter in response to our recent paper “The Structure of Critical Care Nursing Teams and Patient Outcomes: A Network Analysis” ( 1 ). We conducted an exploratory, hypothesis-generating study using network analysis methods to more deeply understand and examine ICU nurse staffing.…”

mentioning

confidence: 99%

Reply to Chen et al.: Network Analysis Subtleties in ICU Structures and Outcomes

Costa

Liu

Boltey

et al. 2020

Am J Respir Crit Care Med

Self Cite

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“…Therefore, the volume of the EHR system utilization data has been increasing exponentially in recent years, providing abundant resources to identify connections between clinicians. Recent studies applied network analysis to EHR utilization data to measure connections among clinicians [12][13][14][15]. They found EHR system utilization data to potentially be a rich resource to be leveraged to model relationships among clinicians.…”

Section: Introductionmentioning

confidence: 99%

Assessing Neonatal Intensive Care Unit Structures and Outcomes Before and During the COVID-19 Pandemic: Network Analysis Study (Preprint)

Mannering¹,

Yan²,

Gong³

et al. 2021

Preprint

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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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“…Effective interprofessional interactions are critical to the complex clinical environments involving multidisciplinary collaborations typical of intensive care units (ICUs) [8][9][10]. We recently performed a feasibility study [6] using wearable sociometric sensor badges in a health care setting to collect human behavioral interaction datasets.…”

Section: Introductionmentioning

confidence: 99%

Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study (Preprint)

Kawamoto¹,

Ito-Masui²,

Esumi³

et al. 2020

Preprint

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BACKGROUND Use of wearable sensor technology for studying human teamwork behavior is expected to generate a better understanding of the interprofessional interactions between health care professionals. OBJECTIVE We used wearable sociometric sensor badges to study how intensive care unit (ICU) health care professionals interact and are socially connected. METHODS We studied the face-to-face interaction data of 76 healthcare professionals in the ICU at Mie University Hospital collected over 4 weeks via wearable sensors. RESULTS We detail the spatiotemporal distributions of staff members’ inter- and intraprofessional active face-to-face interactions, thereby generating a comprehensive visualization of who met whom, when, where, and for how long in the ICU. Social network analysis of these active interactions, concomitant with centrality measurements, revealed that nurses constitute the core members of the network, while doctors remain in the periphery. CONCLUSIONS Our social network analysis using the comprehensive ICU interaction data obtained by wearable sensors has revealed the leading roles played by nurses within the professional communication network.

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The Structure of Critical Care Nursing Teams and Patient Outcomes: A Network Analysis

Cited by 19 publications

References 7 publications

Reply to Chen et al.: Network Analysis Subtleties in ICU Structures and Outcomes

Reply to Chen et al.: Network Analysis Subtleties in ICU Structures and Outcomes

Assessing Neonatal Intensive Care Unit Structures and Outcomes Before and During the COVID-19 Pandemic: Network Analysis Study (Preprint)

Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study (Preprint)

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