Introduction

Research is being published at an ever-increasing rate and it is becoming more and more difficult for systematic reviewers to find research in a timely way and keep existing reviews updated as new studies are published (Bastian, Glasziou and Chalmers, 2010). This is a particular problem for organizations which maintain libraries of systematic reviews, such as Cochrane and the Campbell Collabor ation, as the more systematic reviews they publish, the greater the burden of maintenance. It is also a challenge for guideline-producing org - anizations which, for pragmatic reasons, typically invest significant resources and effort in one-off periodic updates without knowing whether the evidence base has changed or has actually changed so rapidly that more frequent updating would have been warranted. Previous work has shown that systematic reviews can date very quickly, with some out of date as soon as they are published (Shojania et al., 2007). It is becoming clear that our current methods of research curation are wasteful of societal investment in research and risk resulting in suboptimal outcomes (MacLeod et al., 2014).

This chapter is concerned with this problem of ‘data deluge’ and the need to establish better surveillance of research in order to keep abreast of new developments. It is thus related to work on living systematic reviews (LSRs), and these are ‘continually updated, incorporating relevant new evidence as it becomes available’ (Elliott et al., 2014; Elliott et al., 2017). The chapter outlines developments in automation technologies that are already making the systematic review process more efficient and then focuses on the way that global research curation systems are organised. The chapter suggests that new approaches are needed in order to support the production of evidence syntheses in efficient and timely ways. Case studies 9.1 and 9.2 explain how these new developments are being put into practice to realise these benefits.

Discussion

New ways of working that integrate and capitalise on automation are necessary to tackle the growing burden of identifying and synthesising research. New technologies – which range from the mundane (such as identifying duplicates in bibliographic records) to full artificial intelligence (AI) systems – are under constant development and are already assisting various aspects of the evidence curation (see Tip below) and discovery process.