Combining free text and structured electronic medical record entries to detect acute respiratory infections.

<h4>Background</h4>The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respir...

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Autores principales:	Sylvain DeLisle, Brett South, Jill A Anthony, Ericka Kalp, Adi Gundlapallli, Frank C Curriero, Greg E Glass, Matthew Samore, Trish M Perl
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2010
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ce05894ff30647cd9da775b64dc621f3
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spelling	oai:doaj.org-article:ce05894ff30647cd9da775b64dc621f32021-11-18T07:03:17ZCombining free text and structured electronic medical record entries to detect acute respiratory infections.1932-620310.1371/journal.pone.0013377https://doaj.org/article/ce05894ff30647cd9da775b64dc621f32010-10-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/20976281/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).<h4>Methodology</h4>A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.<h4>Principal findings</h4>An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.<h4>Conclusion</h4>Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties.Sylvain DeLisleBrett SouthJill A AnthonyEricka KalpAdi GundlapallliFrank C CurrieroGreg E GlassMatthew SamoreTrish M PerlPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 5, Iss 10, p e13377 (2010)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Sylvain DeLisle Brett South Jill A Anthony Ericka Kalp Adi Gundlapallli Frank C Curriero Greg E Glass Matthew Samore Trish M Perl Combining free text and structured electronic medical record entries to detect acute respiratory infections.
description	<h4>Background</h4>The electronic medical record (EMR) contains a rich source of information that could be harnessed for epidemic surveillance. We asked if structured EMR data could be coupled with computerized processing of free-text clinical entries to enhance detection of acute respiratory infections (ARI).<h4>Methodology</h4>A manual review of EMR records related to 15,377 outpatient visits uncovered 280 reference cases of ARI. We used logistic regression with backward elimination to determine which among candidate structured EMR parameters (diagnostic codes, vital signs and orders for tests, imaging and medications) contributed to the detection of those reference cases. We also developed a computerized free-text search to identify clinical notes documenting at least two non-negated ARI symptoms. We then used heuristics to build case-detection algorithms that best combined the retained structured EMR parameters with the results of the text analysis.<h4>Principal findings</h4>An adjusted grouping of diagnostic codes identified reference ARI patients with a sensitivity of 79%, a specificity of 96% and a positive predictive value (PPV) of 32%. Of the 21 additional structured clinical parameters considered, two contributed significantly to ARI detection: new prescriptions for cough remedies and elevations in body temperature to at least 38°C. Together with the diagnostic codes, these parameters increased detection sensitivity to 87%, but specificity and PPV declined to 95% and 25%, respectively. Adding text analysis increased sensitivity to 99%, but PPV dropped further to 14%. Algorithms that required satisfying both a query of structured EMR parameters as well as text analysis disclosed PPVs of 52-68% and retained sensitivities of 69-73%.<h4>Conclusion</h4>Structured EMR parameters and free-text analyses can be combined into algorithms that can detect ARI cases with new levels of sensitivity or precision. These results highlight potential paths by which repurposed EMR information could facilitate the discovery of epidemics before they cause mass casualties.
format	article
author	Sylvain DeLisle Brett South Jill A Anthony Ericka Kalp Adi Gundlapallli Frank C Curriero Greg E Glass Matthew Samore Trish M Perl
author_facet	Sylvain DeLisle Brett South Jill A Anthony Ericka Kalp Adi Gundlapallli Frank C Curriero Greg E Glass Matthew Samore Trish M Perl
author_sort	Sylvain DeLisle
title	Combining free text and structured electronic medical record entries to detect acute respiratory infections.
title_short	Combining free text and structured electronic medical record entries to detect acute respiratory infections.
title_full	Combining free text and structured electronic medical record entries to detect acute respiratory infections.
title_fullStr	Combining free text and structured electronic medical record entries to detect acute respiratory infections.
title_full_unstemmed	Combining free text and structured electronic medical record entries to detect acute respiratory infections.
title_sort	combining free text and structured electronic medical record entries to detect acute respiratory infections.
publisher	Public Library of Science (PLoS)
publishDate	2010
url	https://doaj.org/article/ce05894ff30647cd9da775b64dc621f3
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Combining free text and structured electronic medical record entries to detect acute respiratory infections.

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