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Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness
BACKGROUND: For 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 net...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10580574/ https://www.ncbi.nlm.nih.gov/pubmed/37848896 http://dx.doi.org/10.1186/s12911-023-02310-4 |
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author | Kline, Jeffrey A. Reed, Brian Frost, Alex Alanis, Naomi Barshay, Meylakh Melzer, Andrew Galbraith, James W. Budd, Alicia Winn, Amber Pun, Eugene Camargo, Carlos A. |
author_facet | Kline, Jeffrey A. Reed, Brian Frost, Alex Alanis, Naomi Barshay, Meylakh Melzer, Andrew Galbraith, James W. Budd, Alicia Winn, Amber Pun, Eugene Camargo, Carlos A. |
author_sort | Kline, Jeffrey A. |
collection | PubMed |
description | BACKGROUND: For 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. METHODS: The 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. RESULTS: As 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. CONCLUSIONS: The 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02310-4. |
format | Online Article Text |
id | pubmed-10580574 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105805742023-10-18 Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness Kline, Jeffrey A. Reed, Brian Frost, Alex Alanis, Naomi Barshay, Meylakh Melzer, Andrew Galbraith, James W. Budd, Alicia Winn, Amber Pun, Eugene Camargo, Carlos A. BMC Med Inform Decis Mak Database BACKGROUND: For 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. METHODS: The 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. RESULTS: As 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. CONCLUSIONS: The 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02310-4. BioMed Central 2023-10-17 /pmc/articles/PMC10580574/ /pubmed/37848896 http://dx.doi.org/10.1186/s12911-023-02310-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Database Kline, Jeffrey A. Reed, Brian Frost, Alex Alanis, Naomi Barshay, Meylakh Melzer, Andrew Galbraith, James W. Budd, Alicia Winn, Amber Pun, Eugene Camargo, Carlos A. Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness |
title | Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness |
title_full | Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness |
title_fullStr | Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness |
title_full_unstemmed | Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness |
title_short | Database derived from an electronic medical record-based surveillance network of US emergency department patients with acute respiratory illness |
title_sort | database derived from an electronic medical record-based surveillance network of us emergency department patients with acute respiratory illness |
topic | Database |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10580574/ https://www.ncbi.nlm.nih.gov/pubmed/37848896 http://dx.doi.org/10.1186/s12911-023-02310-4 |
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