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J Clin Epidemiol. 2018 Dec 21. pii: S0895-4356(18)30707-8. doi: 10.1016/j.jclinepi.2018.12.014. [Epub ahead of print]

Software engineering principles address current problems in the systematic review ecosystem.

Author information

1
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia. Electronic address: rabia.bashir@students.mq.edu.au.
2
Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.

Abstract

Systematic reviewers are simultaneously unable to produce systematic reviews fast enough to keep up with the availability of new trial evidence while overproducing systematic reviews that are unlikely to change practice because they are redundant or biased. Although the transparency and completeness of trial reporting has improved with changes in policy and new technologies, systematic reviews have not yet benefited from the same level of effort. We found that new methods and tools used to automate aspects of systematic review processes have focused on improving the efficiency of individual systematic reviews rather than the efficiency of the entire ecosystem of systematic review production. We use software engineering principles to review challenges and opportunities for improving the interoperability, integrity, efficiency, and maintainability. We conclude by recommending ways to improve access to structured systematic review results. Major opportunities for improving systematic reviews will come from new tools and changes in policy focused on doing the right systematic reviews rather than just doing more of them faster.

KEYWORDS:

Evidence synthesis; Machine learning; Software engineering; Systematic reviews as topic; Trial registration; Updating systematic reviews

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