  In studies that rely on data from electronic health records (EHRs),
unstructured text data such as clinical progress notes offer a rich source of
information about patient characteristics and care that may be missing from
structured data. Despite the prevalence of text in clinical research, these
data are often ignored for the purposes of quantitative analysis due their
complexity. This paper presents a unified framework for leveraging text data to
support causal inference with electronic health data at multiple stages of
analysis. In particular, we consider how natural language processing and
statistical text analysis can be combined with standard inferential techniques
to address common challenges due to missing data, confounding bias, and
treatment effect heterogeneity. Through an application to a recent EHR study
investigating the effects of a non-randomized medical intervention on patient
outcomes, we show how incorporating text data in a traditional matching
analysis can help strengthen the validity of an estimated treatment effect and
identify patient subgroups that may benefit most from treatment. We believe
these methods have the potential to expand the scope of secondary analysis of
clinical data to domains where structured EHR data is limited, such as in
developing countries. To this end, we provide code and open-source replication
materials to encourage adoption and broader exploration of these techniques in
clinical research.
