Living systematic reviews

2. Combining human and machine effort

James Thomas, Anna Noel-Storr, Iain Marshall, Byron Wallace, Steven McDonald, Chris Mavergames, Paul Glasziou, Ian Shemilt, Anneliese Synnot, Tari Turner, Julian Elliott, The Living Systematic Review Network, Guy Tsafnat, Sarah A. Elliott, Joerg Meerpohl, Peter Tugwell, Itziar Etxeandia, Bronwen Merner, Alexis Turgeon, Robin Featherstone & 39 others Stefania Mondello, Tari Turner, Ruth Foxlee, Richard Morley, Gert van Valkenhoef, Paul Garner, Marcus Munafo, Per Vandvik, Martha Gerrity, Zachary Munn, Byron Wallace, Paul Glasziou, Melissa Murano, Sheila A. Wallace, Sally Green, Kristine Newman, Chris Watts, Jeremy Grimshaw, Robby Nieuwlaat, Laura Weeks, Kurinchi Gurusamy, Adriani Nikolakopoulou, Aaron Weigl, Neal Haddaway, Anna Noel-Storr, George Wells, Lisa Hartling, Annette O'Connor, Wojtek Wiercioch, Jill Hayden, Matthew Page, Luke Wolfenden, Mark Helfand, Manisha Pahwa, Juan José Yepes Nuñez, Julian Higgins, Jordi Pardo Pardo, Jennifer Yost, Sophie Hill

Research output: Contribution to journalReview article

72 Citations (Scopus)
9 Downloads (Pure)

Abstract

New approaches to evidence synthesis, which use human effort and machine automation in mutually reinforcing ways, can enhance the feasibility and sustainability of living systematic reviews. Human effort is a scarce and valuable resource, required when automation is impossible or undesirable, and includes contributions from online communities (“crowds”) as well as more conventional contributions from review authors and information specialists. Automation can assist with some systematic review tasks, including searching, eligibility assessment, identification and retrieval of full-text reports, extraction of data, and risk of bias assessment. Workflows can be developed in which human effort and machine automation can each enable the other to operate in more effective and efficient ways, offering substantial enhancement to the productivity of systematic reviews. This paper describes and discusses the potential—and limitations—of new ways of undertaking specific tasks in living systematic reviews, identifying areas where these human/machine “technologies” are already in use, and where further research and development is needed. While the context is living systematic reviews, many of these enabling technologies apply equally to standard approaches to systematic reviewing.

Original languageEnglish
Pages (from-to)31-37
Number of pages7
JournalJournal of Clinical Epidemiology
Volume91
DOIs
Publication statusPublished - 1 Nov 2017
Externally publishedYes

Bibliographical note

Copyright the Author(s) 2017. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Automation
  • Citizen science
  • Crowdsourcing
  • Machine learning
  • Systematic review
  • Text mining

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