The Clinical Trial Risk Tool has been featured in a guest column by Thomas Wood, director of Fast Data Science, in Clinical Leader, titled A Tool To Tackle The Risk Of Uninformative Trials, in cooperation with Abby Proch, Executive Editor at Clinical Leader.
In this article, Thomas Wood discusses the issue of uninformative clinical trials in pharmaceutical research. Uninformative trials fail to provide meaningful results, either because they don’t address key questions or are poorly designed. These trials waste resources, expose participants to unnecessary risks, and hinder progress in medical knowledge.
Wood uses the definition of “uninformative trials” from Zarin et al (2019)[1] as trials that do not deliver informative results, even though the investigated treatment may be effective or ineffective. An informative trial, according to experts, must meet five conditions: addressing an important question, providing meaningful evidence, being feasible, conducted scientifically, and reporting methods and results promptly.
The Clinical Trial Risk Tool was created to prevent such trials by providing features like a clinical trial budget estimator based on real-world cost data. The development of the first version of the tool was published in Gates Open Research[2]. In future, the tool could retrieve past trials with similar endpoints or inclusion/exclusion criteria to improve protocol design. It could also generate personalised feedback for different stakeholders, such as medical professionals, financial planners, and patient advocates.
Fast Data Science’s Clinical Trial Risk Tool offers a solution by allowing users to upload trial protocols, where AI analyzes the document for risk and cost factors. The tool estimates trial costs and identifies risks, helping to avoid uninformative outcomes early in the design process.
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Why are product returns a problem? Large businesses like Amazon can swallow the cost of product returns, and generally account for it, but if you’re running a small business a single return can be very expensive. However, product returns can be a major problem for small and large businesses and it is helpful to identify when this is likely to occur.
Fast Data Science Ltd’s flagship AI platform, the Clinical Trial Risk Tool, has been accepted as a supplier on the UK Government’s G-Cloud 15 framework.
We are pleased to announce that Thomas Wood, director of Fast Data Science, will be appearing as a panelist at the Bond Solon Expert Witness Conference on 6 November 2026 at Church House, Westminster in London. This follows Thomas’s recent appearance at the Ireland’s Expert Witness Conference on 20 May 2026.
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