Peter L. Elkin, MD; David A. Froehling, MD; Dietlind L. Wahner-Roedler, MD; Steven H. Brown, MD, MS; Kent R. Bailey, PhD
Disclaimer: All authors have had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. The contributors of this report have disclosed that they have no financial interest, relationship, affiliation, or other association with any organization that might represent a conflict of interest. In addition, this report does not contain any discussion of unlabeled use of commercial products or products for investigational use.
Acknowledgment: The authors thank Inna Gurewitz, MPH, for her assistance in preparing this manuscript.
Grant Support: By the CDC (grants PH00022 and HK00014) and a research contract from the Veterans Administration (contract V249P-0525; Biosurveillance SDR Project 330).
Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M11-0732.
Reproducible Research Statement:Study protocol: Available from Dr. Elkin (e-mail, firstname.lastname@example.org). Statistics code and data set: Not available.
Corresponding Author: Peter L. Elkin, MD, 212 East 95th Street, Suite 3B, New York, NY 10128.
Current Author Addresses: Dr. Elkin: Mount Sinai School of Medicine, Center for Biomedical Informatics, 212 East 95th Street, Suite 3B, New York, NY 10128.
Drs. Froehling, Wahner-Roedler, and Bailey: Mayo Clinic, 200 First Street, Rochester, MN 55905.
Dr. Brown: 2100 West End Avenue, Suite 840, Nashville, TN 37203.
Author Contributions: Conception and design: P.L. Elkin, D.L. Wahner-Roedler, K.R. Bailey.
Analysis and interpretation of the data: P.L. Elkin, D.A. Froehling, D.L. Wahner-Roedler, S.H. Brown, K.R. Bailey.
Drafting of the article: P.L. Elkin, K.R. Bailey.
Critical revision for important intellectual content: P.L. Elkin, D.A. Froehling, K.R. Bailey, S.H. Brown.
Final approval of the article: P.L. Elkin, D.A. Froehling, D.L. Wahner-Roedler, S.H. Brown, K.R. Bailey.
Provision of study materials or patients: D.L. Wahner-Roedler.
Statistical expertise: K.R. Bailey.
Obtaining of funding: P.L. Elkin.
Administrative, technical, or logistic support: P.L. Elkin, D.L. Wahner-Roedler.
Collection and assembly of data: P.L. Elkin, D.L. Wahner-Roedler.
Elkin PL, Froehling DA, Wahner-Roedler DL, Brown SH, Bailey KR. Comparison of Natural Language Processing Biosurveillance Methods for Identifying Influenza From Encounter Notes. Ann Intern Med. 2012;156:11-18. doi: 10.7326/0003-4819-156-1-201201030-00003
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Published: Ann Intern Med. 2012;156(1_Part_1):11-18.
An effective national biosurveillance system expedites outbreak recognition and facilitates response coordination at the federal, state, and local levels. The BioSense system, used at the Centers for Disease Control and Prevention, incorporates chief complaints but not data from the whole encounter note into its surveillance algorithms.
To evaluate whether biosurveillance by using data from the whole encounter note is superior to that using data from the chief complaint field alone.
6-year retrospective case–control cohort study.
Mayo Clinic, Rochester, Minnesota.
17 243 persons tested for influenza A or B virus between 1 January 2000 and 31 December 2006.
The accuracy of a model based on signs and symptoms to predict influenza virus infection in patients with upper respiratory tract symptoms, and the ability of a natural language processing technique to identify definitional clinical features from free-text encounter notes.
Surveillance based on the whole encounter note was superior to the chief complaint field alone. For the case definition used by surveillance of the whole encounter note, the normalized partial area under the receiver-operating characteristic curve (specificity, 0.1 to 0.4) for surveillance using the whole encounter note was 92.9% versus 70.3% for surveillance with the chief complaint field (difference, 22.6%; P < 0.001). Comparison of the 2 models at the fixed specificity of 0.4 resulted in sensitivities of 89.0% and 74.4%, respectively (P < 0.001). The relative risk for missing a true case of influenza was 2.3 by using the chief complaint field model.
Participants were seen at 1 tertiary referral center. The cost of comprehensive biosurveillance monitoring was not studied.
A biosurveillance model for influenza using the whole encounter note is more accurate than a model that uses only the chief complaint field. Because case-defining signs and symptoms of influenza are commonly available in health records, the investigators believe that the national strategy for biosurveillance should be changed to incorporate data from the whole health record.
Centers for Disease Control and Prevention.
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Infectious Disease, Pulmonary/Critical Care, Influenza.
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Print ISSN: 0003-4819 | Online ISSN: 1539-3704
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