How predictive analytics platforms can optimise clinical research
Clinical research trials are a huge problem for pharmaceutical companies. According to research, the median cost of research trials was $19 million with an interquartile range (IQR) of $12 million to $33 million. This makes research projects a very expensive proposition for most organisations (which could lead to several issues like funding).
Expenses, however, are only one problem facing clinical research organisations (CROs). Finding suitable volunteers for the trial is also a significant challenge. Research trials often fall short of the required number of volunteers. When this happens, it throws research trials off—compromising accuracy and test results.
To work around these problems, clinical research organisations (CROs) need to find new solutions to help optimise clinical research, anticipate problems, and mitigate some of the problems they are facing. One of these solutions is predictive analytics platforms.
Resolving clinical research problems with predictive analytics
Predictive analytics helps CROs resolve several problems related to clinical research.
Analytics helps with patient recruitment
Predictive analytics can help organisations improve the recruitment process. CROs are turning to analytics and treasure troves of big data to make better decisions when it comes to assessing the feasibility process. For example, rather than spending hours creating the perfect survey, patient recruitment is now a more data-driven process.
A data-driven approach provides a more detailed scenario of the possible patient base for a particular drug or treatment. By using this data, it becomes much easier to predict clusters of people that are eligible for clinical trials in a certain geographical location. This helps reduce the time taken to find a suitable list of candidates for research, making the process far more efficient than before.
Predictive analytics platforms can help CROs work around unexpected volunteer shortages. For example, if researchers usually turn to particular trial sites to find their candidates, they can use predictive analytics to check if they can get a sufficient number of patients needed to complete research trials. They can also determine if they are falling short of the required number of patient volunteers and take action to remedy the problem.
Analytics helps with managing volunteers
However, it is not just a question of anticipating patient volunteers for a clinical trial. It is also a question of understanding their behaviour.
Human behaviour is complex and there are many variables that are hard to anticipate during the research process. Will volunteers behave as expected? Will they drop out before the trial process is complete? Clinical research analytics can help CROs work around this problem by anticipating how patients might behave during the clinical trial process by mining medical data.
CROs can optimise the clinical research trial process
It’s not unusual for organisations to spend millions, if not billions, on research and development. The R&D process absorbs a significant amount of funds and takes years to complete before one drug is ready.
Predictive modelling platforms can help CROs reduce the cost of clinical research trials. The analytics platform can account for several variables that can affect drug development.
Predictive models can account for many variables related to the viability and efficiency of a drug. Furthermore, the model allows users to simulate different phases of the disease process on the human body (depending on the research trial), making many parts of RnD, like drug formulation and side effects, more cost-effective.
This additional insight can help researchers optimise parts of the trial process to reduce the cost of drug research and development, as well as bring the product to market faster. Researchers also have an easier time understanding complex medical data thanks to analytics.
Making clinical research more efficient
The cost of clinical research trials is growing and it is taking longer to get the product to market.
CROs can use predictive analytics to make the process more efficient than before by helping organisations make better use of their big data. Big data is a useful asset that contains a lot of valuable information—information that can help CROs optimise the trial process.
This can help organisations reduce the cost of research trials and shorten the time required to bring the product to fruition, which will help improve ROI on medical research.
To learn more about predictive analytics platforms, visit Selerity.