Use of electronic medical records (EMR) for oncology outcomes research: assessing the comparability of EMR information to patient registry and health claims data

Edmund C Lau1, Fionna S Mowat1, Michael A Kelsh1,*, Jason C Legg2, Nicole M Engel-Nitz3, Heather N Watson1, Helen L Collins2, Robert J Nordyke4,5, Joanna L Whyte21Exponent, Menlo Park, CA, USA; 2Amgen, Thousand Oaks, CA, USA; 3i3 Innovus, Eden Prairie, MN, USA; 4PriceSpective LLC, El Segundo, CA, USA; 5Department of Health Services, UCLA School of Public Health, Los Angeles, CA, USA*Now at Amgen, Thousand Oaks, CA, USAAbstract: Electronic medical records (EMRs) are used increasingly for research in clinical oncology, epidemiology, and comparative effectiveness research (CER).Objective: To assess the utility of using EMR data in population-based cancer research by comparing a database of EMRs from community oncology clinics against Surveillance Epidemiology and End Results (SEER) cancer registry data and two claims databases (Medicare and commercial claims).Study design and setting: Demographic, clinical, and treatment patterns in the EMR, SEER, Medicare, and commercial claims data were compared using six tumor sites: breast, lung/bronchus, head/neck, colorectal, prostate, and non-Hodgkin's lymphoma (NHL). We identified various challenges in data standardization and selection of appropriate statistical procedures. We describe the patient and clinic inclusion criteria, treatment definitions, and consideration of the administrative and clinical purposes of the EMR, registry, and claims data to address these challenges.Results: Sex and 10-year age distributions of patient populations for each tumor site were generally similar across the data sets. We observed several differences in racial composition and treatment patterns, and modest differences in distribution of tumor site.Conclusion: Our experience with an oncology EMR database identified several factors that must be considered when using EMRs for research purposes or generalizing results to the US cancer population. These factors were related primarily to evaluation of treatment patterns, including evaluation of stage, geographic location, race, and specialization of the medical facilities. While many specialty EMRs may not provide the breadth of data on medical care, as found in comprehensive claims databases and EMR systems, they can provide detailed clinical data not found in claims that are extremely important in conducting epidemiologic and outcomes research.Keywords: electronic health records, data generalizability, oncology research, health care claims data, epidemiology