Introduction Colorectal cancer is the second leading cause of cancer deaths in the USA, and screening tests are underutilized. The aim of this study was to determine the proportion of individuals at average risk who utilized a recommended initial screening test in a universal healthcare coverage system. Materials and Methods This is a retrospective cohort study of active duty and retired military members as well as civilian beneficiaries of the Military Health System. Individuals born from 1960 to 1962 and eligible for full benefits on their 50th birthday were evaluated. Military rank or rank of benefits sponsor was used to determine socioeconomic status. Adherence to the U.S. Preventive Services Task Force guidelines for initial colorectal cancer screening was determined using “Current Procedural Terminology” and “Healthcare Common Procedure Coding System” codes for colonoscopy, sigmoidoscopy, fecal occult blood test, and fecal immunohistochemistry test. Average risk individuals who obtained early screening ages 47 to 49 were also identified. Results This study identified 275,665 individuals at average risk. Of these, 105,957 (38.4%) adhered to screening guidelines. An additional 19,806 (7.2%) individuals were screened early. Colonoscopy (82.7%) was the most common screening procedure. Highest odds of screening were associated with being active duty military (odds ratio [OR] 3.63, 95% confidence interval [CI] 3.43 to 3.85), having highest socioeconomic status (OR 2.37, 95% CI 2.31 to 2.44), and having managed care insurance (OR 4.36, 95% CI 4.28 to 4.44). Conclusions Universal healthcare coverage does not ensure initial colorectal cancer screening utilization consistent with guidelines no does it eliminate disparities.

PURPOSE Synoptic reporting provides a mechanism for uniform and structured pathology diagnostics. This paper demonstrates the functionality of Perl alternation and grouping expressions to classify electronic pathology reports generated from military treatment facilities. Eight Perl-based algorithms are validated to classify malignant melanoma, Hodgkin lymphoma, non-Hodgkin lymphoma, leukemia, and malignant neoplasms of the breast, ovary, testis, and thyroid. METHODS Case finding cohorts were developed using diagnostic codes for neoplasm groups and matched by unique identifiers to obtain pathology records. Preprocessing techniques and Perl-based algorithms were applied to classify records as malignant, in situ, suspect, or nonapplicable, followed by a hand-review process to determine the accuracy of the algorithm classifications. Interrater reliability, sensitivity, specificity, positive predictive values, and negative predictive values were computed following abstractor adjudication. RESULTS The specificity of the Perl-based algorithms was consistently high, over 98%. Very few benign results were classified as malignant or in situ by the Perl-based algorithms; the leukemia algorithm classification was the only group to demonstrate a positive predictive value below 95%, at 91.9%. Three algorithm classification groups demonstrated a sensitivity of < 80%, including malignant neoplasm of the ovary (33.3%), leukemia (52.8%), and non-Hodgkin lymphoma (62.9%). The pathology records for these results included substantial linguistic variation. CONCLUSION This paper contextualizes the utility and value of an algorithm logic built around synoptic reporting to identify neoplasms from electronic pathology results. The major strength includes the application of Perl-based coding in SAS, an accessible software application, to develop highly specific algorithms across institutional variation in diagnostic documentation.

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PURPOSE This study demonstrates the functionality of semiautomated algorithms to classify cancer-specific grading from electronic pathology reports generated from military treatment facilities. Two Perl-based algorithms are validated to classify WHO grade for tumors of the CNS and Gleason grades for prostate cancer. METHODS Case-finding cohorts were developed using diagnostic codes and matched by unique identifiers to obtain pathology records generated in the Military Health System for active duty service members from 2013 to 2018. Perl-based algorithms were applied to classify document-based pathology reports to identify malignant CNS tumors and prostate cancer, followed by a hand-review process to determine accuracy of the algorithm classifications. Inter-rater reliability, sensitivity, specificity, positive predictive values (PPVs), and negative predictive values were computed following abstractor adjudication. RESULTS The high PPV for the Perl-based algorithms to classify CNS tumors (PPV > 98%) and prostate cancer (PPV > 99%) supports this approach to classify malignancies for cancer surveillance operations, mediated by a hand-reviewed semiautomated process to increase sensitivity by capturing ungraded cancers. Early detection was pronounced where 33.6% and 50.7% of malignant records retained a CNS WHO grade of II or a Gleason score of 6, respectively. Sensitivity metrics met criteria (> 75%) for brain (79.9%, 95% CI, 73.0 to 85.7) and prostate (96.7%, 95% CI, 94.9 to 98.0) cancers. CONCLUSION Semiautomated, document-based text classification using Perl coding successfully leveraged identification of WHO and Gleason grades to classify pathology records for CNS tumors and prostate cancer. The process is recommended for data quality initiatives to support cancer reporting functions, epidemiology, and research.