Harnessing Big Data: The Raw Material Of Digital Transformation
To realize the full benefits of emerging technologies, CROs and sponsors must appreciate that these innovations will have a higher impact when employed collectively than when used individually. This involves realizing that as digital technologies advance, databases will only get exponentially bigger and more complex. Further, the capacity to increase processing and analytical infrastructure to match this acceleration is crucial for realizing its full potential. The development and upkeep of this capability presents a considerable challenge for sponsors. So, the first step to making the most of the digital change to your Veristat clinical trials is to learn how to best use Big Data in your decentralized clinical trials.
To evaluate, normalize, and structure Big Data when analyzing clinical trials, you’ll need a lot of effort due to the diversity and volume of the data. The usefulness and relevance of data is also influenced by factors such as its origins and quality. Using Big Data to identify and address these research needs has been shown to significantly increase trial efficiency and to foster the development of skills and confidence in the application of digital technologies necessary to address increasingly complex issues as they occur.
Below is a brief look at a few data sources and how they can improve the efficiency of clinical research.
Traditional clinical data
Electronic health records (EHRs) and other types of traditional clinical data could prove useful for determining study inclusion and exclusion criteria and identifying prospective research sites. Also, when combined with genetic information, they can help pinpoint patients who are at high risk for acquiring chronic diseases. In addition, they facilitate the generation of RWE for value-based payment models. Traditional clinical data also consists of laboratory, pharmacy, and insurance records.
Emerging real-world data sources
Sources of real-world data (RWD) include mobile clinical monitoring, patient-reported outcome apps, the Internet of medical things, imaging, genomic, and molecular research. Although there are obstacles, such as cybersecurity, usability, and durability, as well as the price of expertise in trial design and digital endpoint validation, the benefits of deploying devices in virtual clinical trials can be substantial. Device efficacy can be established in a shorter time thanks to the amount and granularity of their data. Additionally, mobile devices can facilitate trials, decrease the number of clinic visits, and boost patient retention rates. Similarly, diagnostics, follow-up care, and new treatments can all benefit from genomic, proteomic, and imaging research. As a result of the potential of these technologies to improve the efficiency of novel molecule development, approval rates may increase, doubling the effectiveness of clinical research.
Emerging additional environmental, economic, and social data
Big Data encompasses a wide range of information, from environmental factors to socioeconomic indicators such as health insurance coverage, level of education, and income. For instance, air pollution can have an effect on people with asthma or COPD, mimicking the effect of a drug under trial. Datasets can be cleaned up and the size of trials reduced using supplementary information. In the same way, a person’s general level of education and knowledge of health can affect how well they follow their treatment and help doctors develop treatments that are more likely to work in the real world.
Big Data’s increasing volume and specificity have the potential to boost the effectiveness of clinical trials. To tap into this potential, significant infrastructure, knowledge, and expertise are needed to decide when and how to deploy it. To improve clinical trial efficiency, however, you’ll need a deep understanding of how clinical trials work.
