Cancer Registry Data Linkage of Electronic Health Record Data From ASCO's CancerLinQ: Evaluation of Advantages, Limitations, and Lessons Learned.

Autor:	Charlton ME; Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA.; Iowa Cancer Registry, College of Public Health, University of Iowa, Iowa City, IA.; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA., Kahl AR; Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, IA.; Iowa Cancer Registry, College of Public Health, University of Iowa, Iowa City, IA., McDowell BD; Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA., Miller RS; CancerLinQ®, American Society of Clinical Oncology, Alexandria, VA., Komatsoulis G; CancerLinQ, American Society of Clinical Oncology, Alexandria, VA., Koskimaki JE; CancerLinQ, American Society of Clinical Oncology, Alexandria, VA., Rivera DR; Surveillance, Epidemiology and End Results Program, Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI), National Institutes of Health (NIH), Rockville, MD., Cronin KA; Surveillance, Epidemiology and End Results Program, Division of Cancer Control and Population Sciences (DCCPS), National Cancer Institute (NCI), National Institutes of Health (NIH), Rockville, MD.
Jazyk:	angličtina
Zdroj:	JCO clinical cancer informatics [JCO Clin Cancer Inform] 2022 Mar; Vol. 6, pp. e2100149.
DOI:	10.1200/CCI.21.00149
Abstrakt:	Purpose: To evaluate the completeness of information for research and quality assessment through a linkage between cancer registry data and electronic health record (EHR) data refined by ASCO's health technology platform CancerLinQ. Methods: A probabilistic data linkage between Iowa Cancer Registry (ICR) and an Iowa oncology clinic through CancerLinQ data was conducted for cases diagnosed between 2009 and 2018. Demographic, cancer, and treatment variables were compared between data sources for the same patients, all of whom were diagnosed with one primary cancer. Treatment data and compliance with quality measures were compared among those with breast or prostate cancer; SEER-Medicare data served as a comparison. Variables captured only in CancerLinQ data (smoking, pain, and height/weight) were evaluated for completeness. Results: There were 6,175 patients whose data were linked between ICR and CancerLinQ data sets. Of those, 4,291 (70%) were diagnosed with one primary cancer and were included in analyses. Demographic variables were comparable between data sets. Proportions of people receiving hormone therapy (30% v 26%, P < .0001) or immunotherapy (22% v 12%, P < .0001) were significantly higher in CancerLinQ data compared with ICR data. ICR data contained more complete TNM stage, human epidermal growth factor receptor 2 testing, and Gleason score information. Compliance with quality measures was generally highest in SEER-Medicare data followed by the combined ICR-CancerLinQ data. CancerLinQ data contained smoking, pain, and height/weight information within one month of diagnosis for 88%, 52%, and 76% of patients, respectively. Conclusion: Linking CancerLinQ EHR data with cancer registry data led to more complete data for each source respectively, as registry data provides definitive diagnosis and more complete stage information and laboratory results, whereas EHR data provide more detailed treatment data and additional variables not captured by registries. Competing Interests: George KomatsoulisEmployment: Zephyr AILeadership: Zephyr AIStock and Other Ownership Interests: Zephyr AINo other potential conflicts of interest were reported.
Databáze:	MEDLINE
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