Where Do We Stand in the Use of Wearables and Big Data in Clinical Trials?
Data collection through wearables and its interpretation with AI reveals new horizons for clinical trials.
Wearables and big data have the potential to significantly improve the collection and analysis of patient information for clinical trials.
Wearables can track consumer and patient information for longer periods of time compared to traditional methods.
It can be applied in deep phenotyping, recruitment of clinical trial participants and detection and interpretation of adverse events.
Artificial intelligence (AI) tools can be used to analyze a large amount of patient data and possibly track patients’ compliance with adherence criteria for clinical trials.
AI tools such as natural language processing and machine learning may also be used to improve recruitment and retention of clinical trial patients.
Several universities and big pharma and tech companies have already launched initiatives for the use of wearables and AI in clinical studies.
To promote the adoption of wearables and AI tools in clinical trials, it important to educate clinical scientists on device technologies and to bring device engineers into drug development and clinical trials.
KEY POINTS
- Wearables and AI tools have the potential to streamline and simplify the collection and analysis of patient data in clinical trials.
- Data shows that around 460 wearable studies are already underway and another study estimates that 70% of clinical trials will incorporate sensors by 2025.
- Pharmaceutical and medical device companies must take advantage of this opportunity to improve their clinical trials and treatment efficacy.
“While the use of wearables and AI tools creates the potential to simplify and streamline clinical research, it also highlights the need to similarly simplify existing processes, roles, and systems in life sciences companies,” says Yvette Jansen, experienced manager for Grant Thornton’s Operations Transformation Healthcare & Life Sciences.
Article source: https://bit.ly/3drllgO
Image source: http://bit.ly/2SIMrX1
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