Linking household members and defining relational networks using routine health data.

Purpose of review: The growth of rich electronic health record (EHR) data and large health databases has introduced new opportunities to link individuals together into households and relational networks. These 'linked relational networks' hold promise for providing family-level care and studying intergenerational epidemiology and clinical outcomes. However, as linked relational networks become more commonly available in EHRs and research databases, it is critical to understand their challenges and limitations.

Recent findings: Matching algorithms are being used to create linked relational networks in EHR and health databases. Clinically, these algorithms have been most useful to provide dyadic maternal-newborn care. In research, studies using these algorithms investigate topics ranging from the pharmacoepidemiology of parental drug exposure on childhood health outcomes, to heritability of chronic conditions, to associations between parental and child healthcare access and service delivery. However, ethical and technical challenges continue to limit use of these algorithms. There is also a critical research gap in the external validity of these matching algorithms.

Summary: Linked relational networks are in widespread use in pediatric clinical care and research. More research is needed to understand the scope, limitations, and biases inherent in existing matching strategies.