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Text is a ubiquitous component of medical data, containing valuableinformation about patient characteristics and care that are often missing fromstructured chart data. Despite this richness, it is rarely used in clinicalresearch, owing partly to its complexity. Using a large database of patientrecords and treatment histories accompanied by extensive notes by attendantphysicians and nurses, we show how text data can be used to support causalinference with electronic health data in all stages, from conception and designto analysis and interpretation, with minimal additional effort. We focus onstudies using matching for causal inference. We augment a classic matchinganalysis by incorporating text in three ways: by using text to supplement amultiple imputation procedure, we improve the fidelity of imputed values tohandle missing data; by incorporating text in the matching stage, we strengthenthe plausibility of the matching procedure; and by conditioning on text, we canestimate easily interpretable text-based heterogeneous treatment effects thatmay be stronger than those found across categories of structured covariates.Using these techniques, we hope to expand the scope of secondary analysis ofclinical data to domains where quantitative data is of poor quality ornonexistent, but where text is available, such as in developing countries.

Leveraging text data for causal inference using electronic health records