Every clinical trial is made up of patients. And every patient is different.
Tools4Patient uses machine learning to understand these inherent differences between patients and minimize key sources of variability in clinical trial data.
The biopharmaceutical industry spends millions each year trying to reduce the risk of trial failure.
Protocols are optimized to ensure that the right data are collected for the trial.
Sites are selected and trained to optimize the experience that the patient has in the trial.
Patient psychology, the one critical piece of the puzzle – the piece that is needed to understand how patient characteristics and experience will impact data – has been missing in understanding clinical trials. Until now.
Tools4Patient technologies deliver the ability to understand and quantify patient psychology. When combined with machine learning, this powerful approach can reduce data variability that is driven by differences between patients.
Imagine if you could predict the placebo responsiveness of every single patient in a clinical trial, thus increasing the probability of trial success.
How can you characterize an individual’s response to stress or changes in clinical trial methodology during a pandemic?
Insurance for Drug Development
Tools4Patient delivers innovative, disease-specific, modular approaches driven by proprietary AI-based algorithms.
Our patented platform technologies are proven to identify and manage key sources of variability within clinical trial data; significantly improving the success rate of trials and reducing the risk within drug development. Traditional approaches have led to high rates of failure in Phase II and III resulting from inability to demonstrate therapeutic efficacy. To counter this, clinical trials evolved by becoming increasingly complex, larger, longer, more expensive, and more burdensome on both the patient and Investigative sites.
It is time to re-imagine the drug development paradigm. Discover how we can reduce complexity and significantly improve the probability of success at key stages of drug development.