clinical trials

Emerging technologies, innovative software and medical devices are revolutionising the healthcare industry. Decentralising clinical trial data is helping to unlock the full potential these tools by enabling people in different places to work securely on data without compromising patient privacy . One of the areas that I find most exciting is the use of predictive modelling and artificial intelligence in federated machine learning – a way to learn from data without removing it from the location where it is stored. This takes us beyond the current established concept of federated databases. By combining several new technologies, we can not only have distributed data – but also distributed 'data fictionalisation' (i.e. the learning from data). This enables owner control of the data during learning. In practice, this disruptive approach ensures that the study is built around the patient, rather than a centralized trial site. It unleashes new ways to use data, transforming how scientists conduct R&D in the discovery and management of various diseases. This new patient-centred approach means data can be collected anywhere – at a hospital or research centre, or from the patient's home using telemedicine tools. This produces data that is far more representative of a patient's real-world activities during their participation in a clinical study. Building clinical trials around patients in their homes and in the community through remote visits and monitoring, enhances recruitment and increases convenience for participants. The implementation of this decentralised research approach is well accepted by patients, offering measurable benefits. It means fewer site visits, making trial participation less daunting. Today's remote advanced technologies have provided many opportunities for healthcare organizations to enhance the overall care experience, improve the health of populations and reduce per capita healthcare costs, globally. As a result, we have seen readmissions among congestive heart failure (CHF) patients in...