Conducting clinical trials is a tedious process, with the cost depending significantly on a variety of factors such as the number of tests, kind of tests, and the country where the tests are being run among others things. A particular domain where costs can skyrocket is influenza clinical trials. Due to the severity and commonality of the disease, right from the recruitment of varied patient populations to the various levels of trials, the costs can quickly rack up.
However, in recent times, machine learning has shown great potential for estimating the cost of clinical trials based on the preliminary data available. One such example is the Clinical Trial Risk Tool from Fast Data Science. But how does it do it?
Fast Data Science’s Clinical Trial Risk Tool uses Natural Language Processing (NLP) to analyse the text of the trial protocol. This means that it ‘reads’ the document much like a human would, but at a considerably faster pace. Using a trained machine learning model, it detects the factors in the protocol that can significantly influence the cost of the trial. This includes things like the number and type of interventions, the number of trial sites, the number of patients involved, amongst others. Depending on these variables, the model provides an estimated cost for the trial.
The wealth of data used in training the model ensures the cost estimations are accurate and adhere to the real-world costs as much as possible. Organizations can harness this tool to gain a better understanding of the financial implications of their trial even before it commences. By doing so, they can fine-tune their trials to accommodate budget constrains without compromising on the trial’s efficacy.
In the context of an influenza clinical trial, costs may vary due to the necessity of testing various age groups, large patient cohort and intervention involving novel vaccine candidates. By analysing the trial protocols with the Clinical Trial Risk Tool, these organizations can preempt these costs and effectively streamline their clinical trials.
In an industry where costs are consistently rising, such tools can be a game changer, enabling more efficient and cost effective clinical trials. The strides in machine learning undoubtedly present an exciting future for clinical trial costing. By using tools like the Clinical Trial Risk Tool, we may save hundreds of hours and millions of dollars and pave the way for more efficient and accessible health innovations.
