Facilitating Clinical Outcomes Assessment through the Automated Identification of Quality Measures for Prostate Cancer Surgery
- aMassachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Administration Hospital, Boston, MA
- bThe Graduate Program in Health Informatics, College of Computer and Information Science and the Bouvé College of Health Sciences, Northeastern University, Boston, MA
- cDepartments of Urology and Health Services, University of California, Los Angeles, CA
- dCenter for Surgery and Public Health, Brigham and Women's Hospital, Boston, MA
- eMedical Imaging Informatics Group, University of California, Los Angeles, CA
- Correspondence: Leonard W. D'Avolio, PhD, MAVERIC (151MAV), Boston VA HCS, 150 South Huntington Avenue, Jamaica Plain, MA 02130 (e-mail: <ldavolio{at}ccs.neu.edu>)
- Received 16 October 2007
- Accepted 11 February 2008
Abstract
Objectives The College of American Pathologists (CAP) Category 1 quality measures, tumor stage, Gleason score, and surgical margin status, are used by physicians and cancer registrars to categorize patients into groups for clinical trials and treatment planning. This study was conducted to evaluate the effectiveness of an application designed to automatically extract these quality measures from the postoperative pathology reports of patients having undergone prostatectomies for treatment of prostate cancer.
Design An application was developed with the Clinical Outcomes Assessment Toolkit that uses an information pipeline of regular expressions and support vector machines to extract CAP Category 1 quality measures. System performance was evaluated against a gold standard of 676 pathology reports from the University of California at Los Angeles Medical Center and Brigham and Women's Hospital. To evaluate the feasibility of clinical implementation, all pathology reports were gathered using administrative codes with no manual preprocessing of the data performed.
Measurements The sensitivity, specificity, and overall accuracy of system performance were measured for all three quality measures. Performance at both hospitals was compared, and a detailed failure analysis was conducted to identify errors caused by poor data quality versus system shortcomings.
Results Accuracies for Gleason score were 99.7%, tumor stage 99.1%, and margin status 97.2%, for an overall accuracy of 98.67%. System performance on data from both hospitals was comparable. Poor clinical data quality led to a decrease in overall accuracy of only 0.3% but accounted for 25.9% of the total errors.
Conclusion Despite differences in document format and pathologists' reporting styles, strong system performance indicates the potential of using a combination of regular expressions and support vector machines to automatically extract CAP Category 1 quality measures from postoperative prostate cancer pathology reports.
Footnotes
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This work was supported in part by the National Library of Medicine Medical Informatics Training Grant LM07356 and National Institutes of Health grant R01 EB00362.
