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Accepted for/Published in: JMIR Human Factors

Date Submitted: Nov 13, 2020
Date Accepted: Feb 22, 2021
Date Submitted to PubMed: Feb 23, 2021

The final, peer-reviewed published version of this preprint can be found here:

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study

Yan C, Zhang X, Gao C, Wilfon E, Casey J, France D, Gong Y, Patel M, Malin B, Chen Y

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study

JMIR Hum Factors 2021;8(1):e25724

DOI: 10.2196/25724

PMID: 33621187

PMCID: 7942392

The Collaboration Structure in COVID-19 Critical Care: A Network Analysis

  • Chao Yan; 
  • Xinmeng Zhang; 
  • Cheng Gao; 
  • Erin Wilfon; 
  • Jonathan Casey; 
  • Danniel France; 
  • Yang Gong; 
  • Mayur Patel; 
  • Bradley Malin; 
  • You Chen

ABSTRACT

Background:

Few ICU staffing studies examine collaboration structures among healthcare workers. Knowledge about how healthcare workers (HWs) are connected to care for critically ill COVID-19 (C19) patients provides evidence for characterizing the relationship between team structures, care quality, and patient safety.

Objective:

To discover distinctions of teamwork structures in COVID-19 critical care by comparing healthcare worker collaboration associated with the management of critically ill patients with and without COVID-19.

Methods:

We apply network analysis to the utilization of electronic health records (EHRs) of 76 critically ill patients (38 with and 38 without C19) admitted to a large academic medical center to learn HW collaboration. We use the EHRs for adult patients admitted to the C19 ICU at Vanderbilt University Medical Center (Nashville, Tennessee, USA) between March 17, 2020 and May 31, 2020. We matched each C19 patient on age, gender, and length of stay, with NC19 patients admitted to the Medical ICU (MICU) between December 1, 2019 and February 29, 2020. Then we use two sociometric measurements, including eigencentrality and betweenness, to quantify the status of each HW in the networks, respectively. Eigencentrality characterizes the degree to which a HW is likely to be a core person in the collaboration. Betweenness centrality refers to whether a HW lies on the path of others who are not directly connected. We further measure patient staffing intensity in terms of the number of HWs interacting with the EHR of a patient. We assess the extent to which the core and betweenness status of HWs, as well as patient staffing intensity, in C19 and Non-C19 (NC19) critical care are statistically different using Mann-Whitney U tests at the 95% confidence level.

Results:

HWs are likely to more frequently work with each other in C19 than NC19 critical care (median eigencentrality values of 0.096 vs. 0.057, respectively; p = 1.5×10-9). Internal medicine physicians exhibit a higher core status in the C19 critical care than NC19 (p = 1.2×10-3). Nurse practitioners exhibit a more betweenness status in the C19 than NC19 care (p = 3.10 × 10-4). In comparison to the NC19 setting, the EHRs of C19 critically ill patients were utilized by a larger number of internal medicine nurse practitioners (p = 1.27 × 10-5), cardiovascular nurses (p = 8.48 × 10-6) and surgical ICU nurses (p = 1.62  10-3), as well as a smaller number of resident physicians (p = 5.96 × 10-4).

Conclusions:

Network analysis methodologies and electronic health record utilization data provide a novel way to learn distinctions of collaboration structures in C19 critical care, which can be leveraged by healthcare organizations to understand the novel additions the C19 brings to the collaboration structure in urgent care.


 Citation

Please cite as:

Yan C, Zhang X, Gao C, Wilfon E, Casey J, France D, Gong Y, Patel M, Malin B, Chen Y

Collaboration Structures in COVID-19 Critical Care: Retrospective Network Analysis Study

JMIR Hum Factors 2021;8(1):e25724

DOI: 10.2196/25724

PMID: 33621187

PMCID: 7942392

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