Evidence map›Paper›PMID 33621187›Full record

ArticleJMIR human factors2021

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

Chao Yan, Xinmeng Zhang, Cheng Gao, Erin Wilfong, Jonathan Casey, Daniel France, Yang Gong, Mayur Patel, Bradley Malin, You Chen

Abstract read
In one paragraph

Article in JMIR human factors, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Perioperative Care Structures and Non-Routine Events: Network Analysis.Studies in health technology and informatics · 2022
    Article
  11. Article
  12. Inferring EHR Utilization Workflows through Audit Logs.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022
    Article
  13. Predicting next-day discharge via electronic health record access logs.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  14. Article
  15. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Chao YanDepartment of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID https://orcid.org/0000-0002-6719-1388
Xinmeng ZhangDepartment of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID https://orcid.org/0000-0001-7876-0753
Cheng GaoDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID https://orcid.org/0000-0002-5830-3739
Erin WilfongDivision of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID https://orcid.org/0000-0002-6584-1578
Jonathan CaseyDivision of Allergy, Pulmonary and Critical Care Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID https://orcid.org/0000-0002-0977-290X
Daniel FranceDepartment of Anesthesiology, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID https://orcid.org/0000-0002-5648-8223
Yang GongSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, United States.ORCID https://orcid.org/0000-0002-0864-8368
Mayur PatelDepartment of Hearing & Speech Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.ORCID https://orcid.org/0000-0001-5230-0871
Bradley MalinDepartment of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID https://orcid.org/0000-0003-3040-5175
You ChenDepartment of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, United States.ORCID https://orcid.org/0000-0001-8232-8840

Funding

Institutional Career Development CoreKL2TR002245 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Julie A. Bastarache · 2017 to 2026
$9.3M
Discovering Care Coordination Practice Patterns in the EMR: Interpretation and Impact on Patient OutcomesR01LM012854 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CHEN, YOU · 2019 to 2022
$1.5M
NCATS NIH HHS KL2 TR002245NLM NIH HHS R01 LM012854
6 · The paper itself

Abstract

backgroundFew intensive care unit (ICU) staffing studies have examined the collaboration structures of health care workers (HCWs). Knowledge about how HCWs are connected to the care of critically ill patients with COVID-19 is important for characterizing the relationships among team structures, care quality, and patient safety.

objectiveWe aimed to discover differences in the teamwork structures of COVID-19 critical care by comparing HCW collaborations in the management of critically ill patients with and without COVID-19.

methodsIn this retrospective study, we used network analysis methods to analyze the electronic health records (EHRs) of 76 critically ill patients (with COVID-19: n=38; without COVID-19: n=38) who were admitted to a large academic medical center, and to learn about HCW collaboration. We used the EHRs of adult patients who were admitted to the COVID-19 ICU at the Vanderbilt University Medical Center (Nashville, Tennessee, United States) between March 17, 2020, and May 31, 2020. We matched each patient according to age, gender, and their length of stay. Patients without COVID-19 were admitted to the medical ICU between December 1, 2019, and February 29, 2020. We used two sociometrics-eigencentrality and betweenness-to quantify HCWs' statuses in networks. Eigencentrality characterizes the degree to which an HCW is a core person in collaboration structures. Betweenness centrality refers to whether an HCW lies on the path of other HCWs who are not directly connected. This sociometric was used to characterize HCWs' broad skill sets. We measured patient staffing intensity in terms of the number of HCWs who interacted with patients' EHRs. We assessed the statistical differences in the core and betweenness statuses of HCWs and the patient staffing intensities of COVID-19 and non-COVID-19 critical care, by using Mann-Whitney U tests and reporting 95% CIs.

resultsHCWs in COVID-19 critical care were more likely to frequently work with each other (eigencentrality: median 0.096) than those in non-COVID-19 critical care (eigencentrality: median 0.057; P<.001). Internal medicine physicians in COVID-19 critical care had higher core statuses than those in non-COVID-19 critical care (P=.001). Nurse practitioners in COVID-19 care had higher betweenness statuses than those in non-COVID-19 care (P<.001). Compared to HCWs in non-COVID-19 settings, the EHRs of critically ill patients with COVID-19 were used by a larger number of internal medicine nurse practitioners (P<.001), cardiovascular nurses (P<.001), and surgical ICU nurses (P=.002) and a smaller number of resident physicians (P<.001).

conclusionsNetwork analysis methodologies and data on EHR use provide a novel method for learning about differences in collaboration structures between COVID-19 and non-COVID-19 critical care. Health care organizations can use this information to learn about the novel changes that the COVID-19 pandemic has imposed on collaboration structures in urgent care.

Indexed as

collaborationcollaboration structureCOVID-19critical carecritically ill patientelectronic health recordhealth care workerintensive care unitnetwork analysisrelationshipsafetyteamwork

Identifiers

PMID33621187
PMCPMC7942392

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.