Integration of Cancer Registry Data into the Text Information Extraction System: Leveraging the Structured Data Import Tool

Abstract

Introduction/Background:Cancer registries in the US collect timely and systematic data on new cancer cases, extent of disease, staging, biomarker status, treatment, survival, and mortality of cancer cases. Existing methodologies for accessing local cancer registry data for research are time-consuming and often rely on the manual merging of data by staff registrars. In addition, existing registries do not provide direct access to these data nor do they routinely provide linkage to discrete electronic health record (EHR) data, reports, or imaging data. Automation of such linkage can provide an impressive data resource and make valuable data available for translational cancer research.Methods:The UPMC Network Cancer Registry collects highly structured, longitudinal data on all reportable cancer patients, from the point of the diagnosis throughout treatment and follow-up/outcomes. Using commercial registry software, we collect data in compliance with standards governed by the North American Association of Central Cancer Registries. This standardization ensures that the data are highly structured with standard coding and collection methods, which support data exchange among central cancer registries and the Centers for Disease Control and Prevention.Results:At the UPMC Hillman Cancer Center and University of Pittsburgh, we explored the feasibility of linking this well-curated, structured cancer registry data with unstructured text (i.e., pathology and radiology reports), using the Text Information Extraction System (TIES). We used the TIES platform to integrate breast cancer cases from the UPMC Network Cancer Registry system and then combine these data with other EHR data as a pilot use case that can be replicated for other cancers.Conclusions:As a result of this integration, we now have a single searchable repository of information for breast cancer patients from the UPMC registry, combined with their pathology and radiology reports. The system that we developed is easily scalable to other health systems and cancer centers.

Prevalence of Missing Data in the National Cancer Database and Association With Overall Survival

“…For example, greater adherence to structured data entry within the medical record may enable automatic abstraction of structured data elements. 40,41 For unstructured data, natural language processing tools are being explored to capture information that would otherwise require substantial manual review for data abstraction. 42,43 Data missingness itself may not be the reason for worse survival.…”

Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management

“…In addition to entering data from routine clinical reports into cancer registries, cancer registrars often are called upon to assist with the extraction and merging of data from cancer registries for other purposes. Researchers at the University of Pittsburgh developed a tool to extract and merge cancer registry data with unstructured data from other reports 15 .…”

Is there any benefit to particles over photon radiotherapy?

“…Even there, these data are not always structured and so not available for research. Linking structured data of the EHR with existing cancer registries can bridge a gap between daily practice and research [23,24]. Furthermore, retrospective observational studies suffer from time-related biases [4,25,26].…”