A digital technology player has designed a new platform which minimises stress effects on patients monitors patients and provides machine learning-driven predictions around conditions or disease states.
Initial test applications focussed on clinical trials. The company claims that stress is an underlying cause of behavioural and disease states but it is poorly characterised, leading to badly controlled clinical trials with average drop-out rates at 30%. The company then designed a system to measure and monitor a participant’s stress levels during trials.
The measure could save money and reduce the amount of clinical trials that fail to conclude.
Verum uses biometric data, primarily voice and electromyography (EMG), and machine learning to better understand outcomes in order to increase the likelihood of clinical success. During trials the machine can provide real-time triggers and alerts to enable any medical staff and trial co-ordinators to mitigate the effect of stress, investigate it as a confounding factor and inform trial design.
Jaquie Finn, head of digital health at Cambridge Consultants, said: “The rising cost of clinical trials, combined with the commercial risks of failure, mean it’s vital we’re able to harness the power of AI and continuous patient monitoring to mitigate the impact of stress on clinical trial outcomes. Verum informs better adaptive trial design through bigger, real-time contextualized data sets and will mark a step-change in the efficiency of clinical trials.”
The company claims that Verum could be used to help diagnose neurological conditions and used in post market surveillance of drugs, the development of closed loop therapeutics, rehabilitation and remote patient monitoring.