Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.

<h4>Backgrounds</h4>Electronic medical records (EMR) form a rich repository of information that could benefit public health. We asked how structured and free-text narrative EMR data should be combined to improve epidemic surveillance for acute respiratory infections (ARI).<h4>Metho...

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Autores principales:	Hongzhang Zheng, Holly Gaff, Gary Smith, Sylvain DeLisle
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/07cf7bc9d58643c4964f920511c89338
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spelling	oai:doaj.org-article:07cf7bc9d58643c4964f920511c893382021-11-25T06:09:12ZEpidemic surveillance using an electronic medical record: an empiric approach to performance improvement.1932-620310.1371/journal.pone.0100845https://doaj.org/article/07cf7bc9d58643c4964f920511c893382014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25006878/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Backgrounds</h4>Electronic medical records (EMR) form a rich repository of information that could benefit public health. We asked how structured and free-text narrative EMR data should be combined to improve epidemic surveillance for acute respiratory infections (ARI).<h4>Methods</h4>Eight previously characterized ARI case detection algorithms (CDA) were applied to historical EMR entries to create authentic time series of daily ARI case counts (background). An epidemic model simulated influenza cases (injection). From the time of the injection, cluster-detection statistics were applied daily on paired background+injection (combined) and background-only time series. This cycle was then repeated with the injection shifted to each week of the evaluation year. We computed: a) the time from injection to the first statistical alarm uniquely found in the combined dataset (Detection Delay); b) how often alarms originated in the background-only dataset (false-alarm rate, or FAR); and c) the number of cases found within these false alarms (Caseload). For each CDA, we plotted the Detection Delay as a function of FAR or Caseload, over a broad range of alarm thresholds.<h4>Results</h4>CDAs that combined text analyses seeking ARI symptoms in clinical notes with provider-assigned diagnostic codes in order to maximize the precision rather than the sensitivity of case-detection lowered Detection Delay at any given FAR or Caseload.<h4>Conclusion</h4>An empiric approach can guide the integration of EMR data into case-detection methods that improve both the timeliness and efficiency of epidemic detection.Hongzhang ZhengHolly GaffGary SmithSylvain DeLislePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 7, p e100845 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hongzhang Zheng Holly Gaff Gary Smith Sylvain DeLisle Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.
description	<h4>Backgrounds</h4>Electronic medical records (EMR) form a rich repository of information that could benefit public health. We asked how structured and free-text narrative EMR data should be combined to improve epidemic surveillance for acute respiratory infections (ARI).<h4>Methods</h4>Eight previously characterized ARI case detection algorithms (CDA) were applied to historical EMR entries to create authentic time series of daily ARI case counts (background). An epidemic model simulated influenza cases (injection). From the time of the injection, cluster-detection statistics were applied daily on paired background+injection (combined) and background-only time series. This cycle was then repeated with the injection shifted to each week of the evaluation year. We computed: a) the time from injection to the first statistical alarm uniquely found in the combined dataset (Detection Delay); b) how often alarms originated in the background-only dataset (false-alarm rate, or FAR); and c) the number of cases found within these false alarms (Caseload). For each CDA, we plotted the Detection Delay as a function of FAR or Caseload, over a broad range of alarm thresholds.<h4>Results</h4>CDAs that combined text analyses seeking ARI symptoms in clinical notes with provider-assigned diagnostic codes in order to maximize the precision rather than the sensitivity of case-detection lowered Detection Delay at any given FAR or Caseload.<h4>Conclusion</h4>An empiric approach can guide the integration of EMR data into case-detection methods that improve both the timeliness and efficiency of epidemic detection.
format	article
author	Hongzhang Zheng Holly Gaff Gary Smith Sylvain DeLisle
author_facet	Hongzhang Zheng Holly Gaff Gary Smith Sylvain DeLisle
author_sort	Hongzhang Zheng
title	Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.
title_short	Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.
title_full	Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.
title_fullStr	Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.
title_full_unstemmed	Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.
title_sort	epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/07cf7bc9d58643c4964f920511c89338
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Epidemic surveillance using an electronic medical record: an empiric approach to performance improvement.

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