The Registry Case Finding Engine (CaFE): An automated approach for cancer patient identification from unstructured, free-text pathology reports

Abstract

6080 Background: A vital component to maintaining an accurate cancer registry is the identification of patients with cancer. The University of Michigan Cancer Registry identifies more than 90% of all registry patients by manually reading free-text pathology reports and their associated SNOMED codes. This method is labor and time intensive and is subject to errors of omission. Methods: We created an application to scan free-text pathology reports to identify cases of interest to the registry. It uses a custom-made list of approximately 3,300 words, phrases, and SNOMED codes to positively identify relevant cases and to eliminate non-relevant cases, including those which may mention cancer-related terms. Experienced registrars reviewed 2,451 pathology reports and marked cases of interest to the registry; this served as the gold standard. These reports were also analyzed by the Registry CaFE. The time required for case identification was recorded for both processes. Results: Experienced registrars marked 795 (32.4%) cases as being of interest compared to the CaFE which marked 1,009 (41.1%). The sensitivity of the CaFE was 100% whereas the specificity was 87.1%. An analysis of the 214 errors made by the CaFE revelead that 30 cases (14%) were due to incorrect SNOMED codes assigned by our auto-coding system (Cerner Corporation, Kansas City, MO) and 89 (41.6%) were either skin squamous or basal cell carcinomas (most non-melanomatous skin cancers are not tracked in the registry). Registrars required an average of 21 seconds per pathology report whereas the Registry CaFE processed each report in less than a second. Conclusions: The Registry CaFE identified all relevant cases and correctly eliminated most cases that were not important; it is both effective and time-saving. Future efforts directed at improving the CaFE for squamous and basal cell carcinomas would yield the largest improvement in accuracy. No significant financial relationships to disclose.