Using Electronic Health Record Data to Study Latino Immigrant Populations in Health Services Research.

Abstract

The study of healthcare disparities in Latino immigrants is underdeveloped and limited by risk to participants. To validate an electronic health record (EHR)-based algorithm that could serve as a safe proxy for self-reported immigration status for health services researchers. Primary collection/analysis of interview data and secondary analysis of electronic health record data. We developed an EHR algorithm to classify a population of patients as likely undocumented or recent Latino immigrants and validated this algorithm by conducting semi-structured interviews of staff whose main role entails asking about immigration status. We presented them with a list of patients (masked to the interviewer) with whom they had worked, and asked them to indicate patient's immigration status, if they recalled it. We analyzed the correspondence between staff knowledge and our EHR algorithm. Staff described routine conversations with patients about immigration status. The EHR algorithm had fair agreement (66.2%, 95% CI 57.3-74.2) with staff knowledge. When the staff were more confident of their assessment, agreement increased (77.6%, 95% CI 63.4-88.2). The EHR has potential for studying immigration status in health services research, although more study is needed to determine the accuracy and utility of EHRs for this purpose.