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Electronic Interpretation of Chest Radiograph Reports to Detect Central Venous Catheters

Published online by Cambridge University Press:  02 January 2015

William E. Trick ,
Wendy W. Chapman ,
Mary F. Wisniewski ,
Brian J. Peterson ,
Steven L. Solomon  and
Robert A. Weinstein
William E. Trick*
Affiliation:
Health Outcomes Branch, Division of Healthcare Quality Promotion, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Public Health Service, U.S. Department of Health and Human Services, Atlanta, Georgia
Wendy W. Chapman
Affiliation:
University of Pittsburgh Center for Biomedical Informatics, Pittsburgh, Pennsylvania
Mary F. Wisniewski
Affiliation:
Chicago Antimicrobial Resistance Project, Cook County Hospital, Chicago, Illinois
Brian J. Peterson
Affiliation:
Chicago Antimicrobial Resistance Project, Cook County Hospital, Chicago, Illinois
Steven L. Solomon
Affiliation:
Health Outcomes Branch, Division of Healthcare Quality Promotion, National Center for Infectious Diseases, Centers for Disease Control and Prevention, Public Health Service, U.S. Department of Health and Human Services, Atlanta, Georgia
Robert A. Weinstein
Affiliation:
Chicago Antimicrobial Resistance Project, Cook County Hospital, Chicago, Illinois Rush Medical College, Chicago, Illinois
*
Division of Infectious Disease/Durand Bldg., 637 S. Wood St., Chicago, IL 60612
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Abstract

Objective:

To evaluate whether a natural language processing system, SymText, was comparable to human interpretation of chest radiograph reports for identifying the mention of a central venous catheter (CVC), and whether use of SymText could detect patients who had a CVC.

Design:

To identify patients who had a CVC, we performed two surveys of hospitalized patients. Then, we obtained available reports from 104 patients who had a CVC during one of two cross-sectional surveys (ie, case-patients) and 104 randomly selected patients who did not have a CVC (ie, control-patients).

Setting:

A 600-bed public teaching hospital.

Results:

Chest radiograph reports were available from 124 of the 208 participants. Compared with human interpretation, SymText had a sensitivity of 95.8% and a specificity of 98.7%. The use of SymText to identify case- and control-patients resulted in a sensitivity of 43% and a specificity of 98%. Successful application of SymText varied significantly by venous insertion site (eg, a sensitivity of 78% for subclavian and a sensitivity of 3.7% for femoral). Twenty-six percent of the case-patients had a femoral CVC.

Conclusions:

Compared with human interpretation, SymText performed well in interpreting whether a report mentioned a CVC. In patient populations with less frequent CVC placement in femoral veins, the sensitivity for CVC detection likely would be higher. Applying a natural language processing system to chest radiograph reports may be a useful adjunct to other data sources to automate detection of patients who had a CVC.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 24 , Issue 12 , December 2003 , pp. 950 - 954
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2003 

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