How real-world data can revolutionise drug development9 Aug 2019
Utilising the current data environment and its data pools for the benefit of advancing research.
The changing face of drug development and the end of the blockbuster era demands a monumental shift in drug development strategies. Making use of a wider range of resources to inform research and not just focusing on clinical trial data can provide greater insights into treatments and patients. Real-world data taken from patients outside of the clinical research setting is one such resource that is gradually becoming more called upon. Aiden Flynn, CEO of clinical trial data and design experts, Exploristics, explores the current data environment and its data pools and how these can be fully utilised for the benefit of advancing research.
Types of data
Real-world data is a resource that can complement traditional clinical trial research to provide a more comprehensive picture and generalise findings to larger populations of patients. Real-world data comes from a variety of sources: from electronic health records (EHR) to medical insurance claims. There is also a new breed of data that is emerging via wearable technologies and genetic profiling kits such as 23 and me, both of which have the potential to reveal deeper insights into the base line health status of normal healthy subjects not presenting as patients or taking part in clinical trials.
The growing number of data sources via various organisations offer a vital opportunity to glean useful information for healthcare decision-makers. Many developed countries now have high-value, real-world data pools through healthcare systems, charities and disease registries. There is a considerable need to exploit these wide-ranging public healthcare data sources effectively in order to ensure that this information can be fed into the system and provide useful input for researchers and other stakeholders along the drug development pipeline. To realise the full potential of real-world data gleaned from public and private sector bodies requires a Learning Healthcare System. Based on EHRs and other routinely collected healthcare data, a Learning Healthcare System allows data to be continuously fed into the system to improve understanding of treatment pathways and outcomes. As these systems evolve the potential exists to plug the output into platforms that help develop novel medical interventions and streamline patient care pathways.
Compiling and using real-world data in this way would bolster research from clinical trials and facilitate the integration of all medical research. For it to become a reality, healthcare bodies must be equipped with the right technologies to facilitate good data capture and ensure that any captured data is subjected to the right analytical methods. There also needs to be improved standards around consent, ethics and data access. Fully digitising and standardising data in the healthcare field will require a joint effort on an international scale and ensure that Learning Healthcare Systems can be fully implemented and utilised.
The pools of rich real-world data that have been collected over the years across different countries offer a new and evolving prospect for bringing real-world insight into drug development. The liberation of this real-world data via state-of-the-art, digitised learning systems could transform the development of new medicines and the delivery of healthcare. The data revolution is only just beginning but it is set to be a significant transformation.
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