Clario, a technology company that delivers the leading endpoint solutions for decentralized, hybrid and site-based clinical trials, today published a manuscript in Nature Medicine that outlines how AI can improve patients’ experience in DCTs. This comes as remote trials continue to rise in popularity, placing increased responsibility on participants. The study explores how AI automation can support improvements in digital health user interfaces.
“AI is being used to enhance user experience in customer-facing applications across many industries,” said Kevin Thomas, Ph.D., Director of Artificial Intelligence at Clario, one of the researchers. “Adopting this approach in clinical trials means we can help more patients to enroll, empower them to complete trials without undue burden, and ensure they are able to submit high-quality health assessments throughout their participation.”
In the study, Thomas and coauthor Łukasz Kidziński, Ph.D., also Director of Artificial Intelligence at Clario, suggest the following domains of AI can be leveraged to enhance the customer experience in clinical trials:
- Reinforcement learning, which is used to optimize notifications on social media platforms, can customize notifications for each participant, helping participants fit eCOA tasks into their schedule while minimizing unhelpful alerts.
- Computer vision enables automatic assessment of images and videos and is used by many mobile banking apps in the U.S. to coach customers on how to photograph their checks for electronic deposits. If deployed in clinical trials, it could empower users to quickly and successfully submit their medical photos and avoid having to retake them.
- Temporal data AI models can be used in mobility trials that rely on wearable sensors, which can be time-consuming for participants to put on, require live guidance, and remain error-prone. These models could be trained to infer how one body part moves based on data collected at a different body part, in some cases enabling participants to replace a full-body array of sensors with a single sensor.
“Asking participants to engage in time-consuming data entry tasks and unintuitive image capturing can reduce their adherence to a trial’s protocols or increase the number of errors they make,” said Kidziński. “AI can address these challenges and reduce the time required for post-hoc central quality control.”
