Evidence map›Paper›PMID 34623426›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2021

Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic.

Peter Hong, Joshua C Herigon, Colby Uptegraft, Bassem Samuel, D Levin Brown, Jonathan Bickel, Jonathan D Hron

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
–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

12 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  6. Review
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  9. Review
  10. Healthcare personnel interactive pathogen exposure response system.Infection control and hospital epidemiology · 2023
    Article
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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

7 authors.

Peter HongDivision of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.ORCID 0000-0002-1148-8027
Joshua C HerigonDivision of Pediatric Infectious Diseases, Department of Pediatrics, Children's Mercy Kansas City, Kansas City, Missouri, USA.
Colby UptegraftHealth Informatics Branch, Defense Health Agency, Falls Church, Virginia, USA.
Bassem SamuelInformation Services Department, Boston Children's Hospital, Boston, Massachusetts, USA.
D Levin BrownInformation Services Department, Boston Children's Hospital, Boston, Massachusetts, USA.
Jonathan BickelDivision of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.
Jonathan D HronDivision of General Pediatrics, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts, USA.

Funding

RESEARCH TRAINING-MEDICAL INFORMATICS 90T15LM007092 · NLM · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI Nils Gehlenborg · 1992 to 2026
$32.8M
NIH HHSNLM NIH HHS #: T15LM007092NLM NIH HHS T15 LM007092
6 · The paper itself

Abstract

objectiveThis work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019 (COVID-19). MATERIALS AND

methodsWe conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process.

resultsAmong 233 patients with COVID-19, our EHR Report identified 4116 patient-HCW pairs, including 2365 (30.0%) of the 7890 pairs detected by the CTT. The EHR Report also revealed 1751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall.

conclusionsAutomated methods to screen HCWs for potential exposures to patients with COVID-19 using clinical event data from the EHR (1) are likely to improve epidemiological surveillance by contact tracing programs and (2) represent a viable and readily available strategy that should be considered by other institutions.

Indexed as

COVID-19ChildContact TracingCross-Sectional StudiesHealth PersonnelHumansPandemicsSARS-CoV-2clinical informaticscontact tracingCOVID-19electronic health recordsinfection controlinformation technology

Identifiers

PMID34623426
PMCPMC8714290

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.