Evidence map›Paper›PMID 37848896›Full record

ArticleBMC medical informatics and decision making2023

Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness.

Jeffrey A Kline, Brian Reed, Alex Frost, Naomi Alanis, Meylakh Barshay, Andrew Melzer, James W Galbraith, Alicia Budd, Amber Winn, Eugene Pun and 1 more

Abstract read
In one paragraph

Article in BMC medical informatics and decision making, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Jeffrey A KlineDepartment of Emergency Medicine, Wayne State University, Detroit, MI, USA. jkline@wayne.edu.
Brian ReedDepartment of Emergency Medicine, Wayne State University, Detroit, MI, USA.
Alex FrostNewton Massachusetts, Newton, MA, USA.
Naomi AlanisDepartment of Emergency Medicine, John Peter Smith Hospital, Ft. Worth, TX, USA.
Meylakh BarshayDepartment of Emergency Medicine, George Washington University School of Medicine, Washington, DC, USA.
Andrew MelzerDepartment of Emergency Medicine, George Washington University School of Medicine, Washington, DC, USA.
James W GalbraithDepartment of Emergency Medicine, University of Mississippi Medical Center, Jackson, MS, USA.
Alicia BuddInfluenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, USA.
Amber WinnCoronavirus and Other Respiratory Viruses Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, USA.
Eugene PunGeneral Dynamics Contractor to the Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, USA.
Carlos A CamargoDepartment of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFor surveillance of episodic illness, the emergency department (ED) represents one of the largest interfaces for generalizable data about segments of the US public experiencing a need for unscheduled care. This protocol manuscript describes the development and operation of a national network linking symptom, clinical, laboratory and disposition data that provides a public database dedicated to the surveillance of acute respiratory infections (ARIs) in EDs.

methodsThe Respiratory Virus Laboratory Emergency Department Network Surveillance (RESP-LENS) network includes 26 academic investigators, from 24 sites, with 91 hospitals, and the Centers for Disease Control and Prevention (CDC) to survey viral infections. All data originate from electronic medical records (EMRs) accessed by structured query language (SQL) coding. Each Tuesday, data are imported into the standard data form for ARI visits that occurred the prior week (termed the index file); outcomes at 30 days and ED volume are also recorded. Up to 325 data fields can be populated for each case. Data are transferred from sites into an encrypted Google Cloud Platform, then programmatically checked for compliance, parsed, and aggregated into a central database housed on a second cloud platform prior to transfer to CDC.

resultsAs of August, 2023, the network has reported data on over 870,000 ARI cases selected from approximately 5.2 million ED encounters. Post-contracting challenges to network execution have included local shifts in testing policies and platforms, delays in ICD-10 coding to detect ARI cases, and site-level personnel turnover. The network is addressing these challenges and is poised to begin streaming weekly data for dissemination.

conclusionsThe RESP-LENS network provides a weekly updated database that is a public health resource to survey the epidemiology, viral causes, and outcomes of ED patients with acute respiratory infections.

Indexed as

Electronic Health RecordsRespiratory Tract InfectionsEmergency Service, HospitalHumansLaboratoriesPublic HealthElectronic medical recordsEmergency medicineEpidemiologySARS-CoV-2

Identifiers

PMID37848896
PMCPMC10580574

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.