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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You are probably familiar with traditional databases. For example, a teacher at a school will need to enter students’ grades into a system where they get stored, and at the end of the year the grades would need to be retrieved to create the report card for each student. Or an employee database might store employees’ home addresses, pay grades, start dates, and other crucial information. Traditionally, organisations use a structure called a relational database, where different types of data are stored in different tables, with links between them, and they can be queried using a special language called SQL.
A problem we’ve come across repeatedly is how AI can be used to estimate how much a project will cost, based on information known before the project begins, or soon after it starts. By “project” I mean a large project in any industry, including construction, pharmaceuticals, healthcare, IT, or transport, but this could equally apply to something like a kitchen renovation.
Senior lawyers should stop using generative AI to prepare their legal arguments! Or should they? A High Court judge in the UK has told senior lawyers off for their use of ChatGPT, because it invents citations to cases and laws that don’t exist!
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