Skip Navigation

JNCI Monographs 2005 2005(35):116-118; doi:10.1093/jncimonographs/lgi049
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Request Permissions
Google Scholar
Right arrow Articles by Haque, R.
Right arrow Articles by Geiger, A. M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Haque, R.
Right arrow Articles by Geiger, A. M.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2005. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org.

Brief Report

An Automated Data Algorithm To Distinguish Screening and Diagnostic Colorectal Cancer Endoscopy Exams

Reina Haque, Vicki Chiu, Kapil R. Mehta, Ann M. Geiger

Affiliations of authors: Kaiser Permanente Southern California, Research and Evaluation, CA (RH, VC, AMG); Southern California Permanente Medical Group, South Bay, CA (KRM)

Correspondence to: Reina Haque, PhD, MPH, Kaiser Permanente Southern California, Research and Evaluation, 100 S. Los Robles Ave., 2nd Floor, Pasadena, CA 91101 (e-mail: reina.haque{at}kp.org).

Despite questions about accuracy, automated data are used increasingly for research and quality measurement. The goal of this study was to develop an automated data algorithm designed to distinguish screening and diagnostic endoscopy (sigmoidoscopy and colonoscopy) exams. We assessed the algorithm's ability to correctly classify the exams using paper medical records as the "gold standard." The algorithm used diagnostic codes to identify the indication of the endoscopies. The algorithm's ability to classify the indication varied by endoscopy exam. The sensitivities for identifying diagnostic sigmoidoscopy and colonoscopy were 48.1% and 23.8%, respectively. The algorithm missed most of the diagnostic endoscopies. Conversely, the sensitivities for identifying screening sigmoidoscopy and colonoscopy were high (87.9% and 84.4%, respectively) but were associated with low specificities. Our findings suggest that studies relying solely on automated data overestimate screening rates if indication is not considered. The automated algorithm presented here needs further improvements to better differentiate screening from diagnostic exams.


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.