Journal article
Combining free text and structured electronic medical record entries to detect acute respiratory infections
PloS one, v 5(10), pp e13377-e13377
14 Oct 2010
PMID: 20976281
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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).
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.
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%.
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.
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Details
- Title
- Combining free text and structured electronic medical record entries to detect acute respiratory infections
- Creators
- Sylvain DeLisle - University of Maryland, BaltimoreBrett South - University of UtahJill A Anthony - Johns Hopkins UniversityEricka Kalp - Johns Hopkins UniversityAdi Gundlapallli - University of UtahFrank C Curriero - Johns Hopkins UniversityGreg E Glass - Johns Hopkins UniversityMatthew Samore - University of UtahTrish M Perl - Johns Hopkins University
- Publication Details
- PloS one, v 5(10), pp e13377-e13377
- Publisher
- Public LIbrary of Science (PLOS)
- Grant note
- RFA HK000013 / PHITPO CDC HHS U38 HK000013 / PHITPO CDC HHS R01 CI000098 / NCPDCID CDC HHS P01 HK000069 / PHITPO CDC HHS 1 P01HK000069-01 / PHITPO CDC HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Environmental and Occupational Health
- Web of Science ID
- WOS:000282941000014
- Scopus ID
- 2-s2.0-78149436687
- Other Identifier
- 991021899313904721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Medical Informatics