Literature DB >> 16177689

Implementation of laboratory order data in BioSense Early Event Detection and Situation Awareness System.

Haobo Ma1, H Rolka, K Mandl, D Buckeridge, A Fleischauer, J Pavlin.   

Abstract

INTRODUCTION: Laboratory test orders constitute an early outbreak data source. CDC receives laboratory order data in HL7 format from the Laboratory Corporation of America (LabCorp) and plans to use the data in the BioSense Early Event Detection and Situation Awareness System.
METHODS: These LabCorp data contain information on tests ordered and include the type of test ordered and the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM)-coded reasons for the order. A consensus panel was formed to group test orders on the basis of expert opinion into eight standard syndrome categories to provide an additional data source for early outbreak detection. A laboratory order taxonomy was developed and used in the mapping consolidation phase. The five main classes of this taxonomy are miscellaneous functional tests, fluid screening tests, system-specific tests, tests for specific infections (by primary manifestation), and tests for specific noninfectious diseases.
RESULTS: Summary of numbers of laboratory order codes in each syndrome category are fever (53), respiratory (53), gastrointestinal (27), neurological (35), rash (37), lymphadenitis (20), localized cutaneous lesion (11), and specific infection (63).
CONCLUSION: With the daily use of laboratory order data in BioSense, the actual distribution of laboratory order codes in syndrome groups can be evaluated, allowing modification of the mapping.

Entities:  

Mesh:

Year:  2005        PMID: 16177689

Source DB:  PubMed          Journal:  MMWR Suppl        ISSN: 2380-8942


  4 in total

1.  The Indiana Public Health Emergency Surveillance System: ongoing progress, early findings, and future directions.

Authors:  Shaun Grannis; Michael Wade; Joseph Gibson; J Marc Overhage
Journal:  AMIA Annu Symp Proc       Date:  2006

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

Authors:  Sylvain DeLisle; Brett South; Jill A Anthony; Ericka Kalp; Adi Gundlapallli; Frank C Curriero; Greg E Glass; Matthew Samore; Trish M Perl
Journal:  PLoS One       Date:  2010-10-14       Impact factor: 3.240

3.  Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine.

Authors:  Fernanda C Dórea; C Anne Muckle; David Kelton; J T McClure; Beverly J McEwen; W Bruce McNab; Javier Sanchez; Crawford W Revie
Journal:  PLoS One       Date:  2013-03-07       Impact factor: 3.240

4.  Automatic online news monitoring and classification for syndromic surveillance.

Authors:  Yulei Zhang; Yan Dang; Hsinchun Chen; Mark Thurmond; Cathy Larson
Journal:  Decis Support Syst       Date:  2009-05-04       Impact factor: 5.795

  4 in total

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