Future of evidence ecosystem series: 1. Introduction—Evidence synthesis ecosystem needs dramatic change

Isabelle Boutron, Perrine Créquit, Hywel Williams, Joerg Meerpohl, Jonathan C. Craig, Philippe Ravaud

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Objectives: This article presents why the planning, conduct, and reporting of systematic reviews and meta-analyses of therapeutic interventions are suboptimal. Study Design and Setting: We present an overview of the limitations of the current system of evidence synthesis for therapeutic interventions. Results: Systematic reviews and meta-analyses are a cornerstone of health care decisions. However, despite the increasing a number of published systematic reviews of therapeutic interventions, the current evidence synthesis ecosystem is not properly addressing stakeholders’ needs. The current production process leads to a series of disparate systematic reviews because of erratic and inefficient planning with a process that is not always comprehensive and is prone to bias. Evidence synthesis depends on the quality of primary research, so primary research that is not available is biased or selectively reported raises important concerns. Moreover, the lack of interactions between the community of primary research producers and systematic reviewers impedes the optimal use of data. The context has considerably evolved, with ongoing research innovations, a new medical approach with the end of the one-size-fits-all approach, more available data, and new patient expectations. All these changes must be introduced into the future evidence ecosystem. Conclusion: Dramatic changes are needed to enable this future ecosystem to become user driven and user oriented and more useful for decision-making.

Original languageEnglish
Pages (from-to)135-142
Number of pages8
JournalJournal of Clinical Epidemiology
Publication statusPublished - Jul 2020


  • Decision-making
  • Evidence synthesis ecosystem
  • Meta-analysis
  • Methods
  • Systematic review
  • Waste in research


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