  The sheer number of research outputs published every year makes systematic
reviewing increasingly time- and resource-intensive. This paper explores the
use of machine learning techniques to help navigate the systematic review
process. ML has previously been used to reliably 'screen' articles for review -
that is, identify relevant articles based on reviewers' inclusion criteria. The
application of ML techniques to subsequent stages of a review, however, such as
data extraction and evidence mapping, is in its infancy. We therefore set out
to develop a series of tools that would assist in the profiling and analysis of
1,952 publications on the theme of 'outcomes-based contracting'. Tools were
developed for the following tasks: assign publications into 'policy area'
categories; identify and extract key information for evidence mapping, such as
organisations, laws, and geographical information; connect the evidence base to
an existing dataset on the same topic; and identify subgroups of articles that
may share thematic content. An interactive tool using these techniques and a
public dataset with their outputs have been released. Our results demonstrate
the utility of ML techniques to enhance evidence accessibility and analysis
within the systematic review processes. These efforts show promise in
potentially yielding substantial efficiencies for future systematic reviewing
and for broadening their analytical scope. Our work suggests that there may be
implications for the ease with which policymakers and practitioners can access
evidence. While ML techniques seem poised to play a significant role in
bridging the gap between research and policy by offering innovative ways of
gathering, accessing, and analysing data from systematic reviews, we also
highlight their current limitations and the need to exercise caution in their
application, particularly given the potential for errors and biases.
