  The large volume of text in electronic healthcare records often remains
underused due to a lack of methodologies to extract interpretable content. Here
we present an unsupervised framework for the analysis of free text that
combines text-embedding with paragraph vectors and graph-theoretical multiscale
community detection. We analyse text from a corpus of patient incident reports
from the National Health Service in England to find content-based clusters of
reports in an unsupervised manner and at different levels of resolution. Our
unsupervised method extracts groups with high intrinsic textual consistency and
compares well against categories hand-coded by healthcare personnel. We also
show how to use our content-driven clusters to improve the supervised
prediction of the degree of harm of the incident based on the text of the
report. Finally, we discuss future directions to monitor reports over time, and
to detect emerging trends outside pre-existing categories.
