Four State Disability and Health Data Analysis Collaboration

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ABSTRACT Since 2010, clinical medicine has benefited from a rapid surge of clinical research on chronic diseases using data from electronic health records (EHRs). EHRs are appealing because they can offer large sample sizes, timely information, and a wealth of clinical, diagnostic, and laboratory information. However, while millions of patient records are included in large EHR databases, there is poor understanding about the completeness, validity and reliability of information that can be extracted from EHR records on patient populations, and data from EHR networks are not population-representative, constraining their utility for population health surveillance. In this proposal, we propose to leverage our longstanding expertise in developing EHR surveillance indicators, work we have done in partnership with the NYC Health department, and expand the partnership to include the New York State Department of Health and investigators from the NYU Comprehensive Cancer Center. Our goal is to design and test the feasibility of a model surveillance report that includes performance measures and quality of cancer prevention and control in ambulatory care. Indicators will be developed using rules-based testing approaches, and then validated using well-established chart review methods to assess sensitivity and specificity.. We offer: (1) access to a very large EHR network that actively uses the same OMOP CDM employed by the entire PCORNet distributed research network (11 sites) across the country, providing opportunities for future scalability; (2) access to EHR data covering a large proportion of residents living in a large metropolitan area, including high proportions of underinsured, low income, racially/ethnically diverse patients; and (3) an investigation team with extensive prior experience analyzing OMOP CDM electronic health databases and performing population health surveillance. Conducting this study in the diverse, urban environment of NYC offers potential to characterize disparities in at-risk populations