Understanding what factors affect the cost of medical trials is a challenging task but for COVID-related studies, it becomes especially important. With a race against time to provide an effective vaccine or treatment, scientific and financial resources are pushed to their limits.
Knowing that factors such as number of subjects, the type of intervention, the site location, and others can affect trial cost, Fast Data Science’s Clinical Trial Risk Tool uses machine learning (ML) techniques to analyze these aspects and predict the overall cost of a clinical trial. ML algorithms can learn from the historical cost data of previous trials, absorbing complex cost dependencies benefiting from the rich protocol text that contains information about all aspects of a trial without human bias.
But how does this prediction take place, particularly in the case of COVID-19 trials?
In COVID-19 trials, the costs could be influenced by things like the type of subjects (age group, pre-existing conditions, etc.), the prevalence of cases in trial locations, or the type of intervention tested (vaccines, antivirals, etc.). The cost can range from a couple of million to tens of millions.
Fast Data Science uses Natural Language Processing (NLP) techniques to extract information from the protocol text - which is a detailed plan of a clinical trial. The algorithm learns to recognize the significance of certain words, phrases, or sections in the text to estimate how they would affect the trial cost.
The Clinical Trial Risk Tool can predict the cost right at the time of trial design, which helps in anticipating future financial needs. This has the potential to make a pivotal difference by guiding fund allocation and optimizing resource use, ultimately accelerating the development and delivery of COVID-19 solutions.
In conclusion, with the power of machine learning and natural language processing, Fast Data Science’s Clinical Trial Risk Tool can not only offer a scholarly guess on the cost of COVID-19 trials but also help in making critical decisions regarding trial planning and design. It’s a giant step towards effective resource allocation in healthcare, promising to bring a whole new edge in combating the COVID-19 pandemic.
