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BMJ Evid Based Med. 2019 Aug 29. pii: bmjebm-2018-111126. doi: 10.1136/bmjebm-2018-111126. [Epub ahead of print]

Developing a fully automated evidence synthesis tool for identifying, assessing and collating the evidence.

Author information

Trip Database Ltd, Newport, UK
Trip Database Ltd, Newport, UK.
Thoughtful Technology, Newcastle, UK.
Institute of Information Systems Engineering, TU Wien (Vienna University of Technology), Vienna, Austria.
Research Studio Data Science, RSA FG, Vienna, Austria.


Evidence synthesis is a key element of evidence-based medicine. However, it is currently hampered by being labour intensive meaning that many trials are not incorporated into robust evidence syntheses and that many are out of date. To overcome this, a variety of techniques are being explored, including using automation technology. Here, we describe a fully automated evidence synthesis system for intervention studies, one that identifies all the relevant evidence, assesses the evidence for reliability and collates it to estimate the relative effectiveness of an intervention. Techniques used include machine learning, natural language processing and rule-based systems. Results are visualised using modern visualisation techniques. We believe this to be the first, publicly available, automated evidence synthesis system: an evidence mapping tool that synthesises evidence on the fly.


health informatics; world wide web technology


Conflict of interest statement

Competing interests: Both JB and CP are shareholders in Trip Database. JB is also a member of the editorial board of BMJ EBM. No other declared competing interests from the other authors.

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