Cambridge Consultants, a UK-based tech firm, has developed a new digital health platform, "Verum", that uses machine learning (ML) to improve patient monitoring. Its first iteration focuses on improving the clinical trial process and managing the effect of patient stress on trial outcomes.
The platform consists of sensors integrated into a wearable data collection app and a widget for healthcare dashboards that use existing data alongside patient-specific predictions and alerts. It applies an ML algorithm that produces a numerical estimator of a patient's stress at any stage in the trial.
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."
"Stress is an underlying cause of behavioral and disease states and yet it is poorly characterized, leading to badly controlled clinical trials with average drop-out rates at 30%," Cambridge Consultants noted. "Verum harnesses the power of biometric data, primarily voice and electromyography (EMG), and ML to better understand outcomes and increase the likelihood of clinical success."
Verum's ML system provides deeper insight on participants state of mind during trials using real-time triggers and alerts, meaning that health workers can mitigate and monitor the effect of stress resulting in more thorough and efficient clinical trials.