Journal of Pathology Informatics Journal of Pathology Informatics
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ORIGINAL ARTICLE
Year : 2018  |  Volume : 9  |  Issue : 1  |  Page : 18

Can text-search methods of pathology reports accurately identify patients with rectal cancer in large administrative databases?


1 Division of General Surgery, University of Ottawa, Ottawa, ON, Canada
2 Department Epidemiology and Community Medicine, Ottawa Hospital Research Institute, Ottawa, ON, Canada

Correspondence Address:
Reilly P Musselman
The Ottawa Hospital, Civic Campus, 737 Parkdale Avenue, Room CPC 300, Ottawa, ON K1Y 1J8
Canada
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jpi.jpi_71_17

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Background: The aim of this study is to derive and to validate a cohort of rectal cancer surgical patients within administrative datasets using text-search analysis of pathology reports. Materials and Methods: A text-search algorithm was developed and validated on pathology reports from 694 known rectal cancers, 1000 known colon cancers, and 1000 noncolorectal specimens. The algorithm was applied to all pathology reports available within the Ottawa Hospital Data Warehouse from 1996 to 2010. Identified pathology reports were validated as rectal cancer specimens through manual chart review. Sensitivity, specificity, and positive predictive value (PPV) of the text-search methodology were calculated. Results: In the derivation cohort of pathology reports (n = 2694), the text-search algorithm had a sensitivity and specificity of 100% and 98.6%, respectively. When this algorithm was applied to all pathology reports from 1996 to 2010 (n = 284,032), 5588 pathology reports were identified as consistent with rectal cancer. Medical record review determined that 4550 patients did not have rectal cancer, leaving a final cohort of 1038 rectal cancer patients. Sensitivity and specificity of the text-search algorithm were 100% and 98.4%, respectively. PPV of the algorithm was 18.6%. Conclusions: Text-search methodology is a feasible way to identify all rectal cancer surgery patients through administrative datasets with high sensitivity and specificity. However, in the presence of a low pretest probability, text-search methods must be combined with a validation method, such as manual chart review, to be a viable approach.


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