Clinical Research, Artificial Intelligence, and COVID-19
How life sciences companies are reimagining trials during the biggest health crisis in a century
Whatever the world was like on March 15, it’s not like that now — and it probably won’t be for months or years. Everything from buying groceries to renewing a driver’s license is completely different from the way we did things just a few months ago. Clinical trials, like most medical activities, have been significantly affected as governments and pharmaceutical companies have pivoted to a single focus: combatting the novel coronavirus. At the same time, the rush to develop cures and vaccines for COVID-19 is condensing the review and approval process from years to months. This is where artificial intelligence (AI) can play a vital role in changing how clinical trials are conducted and how therapies are evaluated and tested.
AI and machine learning are no longer an emerging trend in the healthcare and life sciences industry: in 2019 there was an 88 percent increase in the number of healthcare professionals who said their organizations were implementing an AI strategy. Nevertheless, the reality is that life sciences companies have traditionally been slow to adopt new technologies and currently many are in the process of working through digital transformation strategies to enable AI capabilities. The COVID-19 pandemic is acting as an external disruptive force to the industry that is accelerating innovation around the traditional clinical development process. With the urgent global need for virus prevention and treatment, the industry can no longer wait to innovate, modernize or adopt advanced technologies that positively impact the timelines and costs associated with bringing new treatments to market.
Life sciences organizations need to leverage AI and advanced data science techniques to run clinical trials more efficiently with less cost and time in order to bring important new therapies, including a COVID vaccine, to market faster. With the AI healthcare market projected to reach $6.6 billion by 2021, it is important that organizations know what to expect from an AI-powered future, where to invest to prepare for that future, and how they can leverage these technologies today. It’s easy to throw around terms like AI and machine learning, but it’s a lot harder to operationalize them for use in the real world. Let’s look at a few practical applications of advanced techniques to expedite research.
Data Mapping
Clinical trial protocol complexity is driving higher volume and more diverse data collection from numerous systems and sources, including longitudinal data from devices. Efficient data collection and analysis strategies are key for quicker approvals and speed to market. To get the most value and insights out of clinical data, data must be collected across diverse systems and mapped or standardized. This process can be time consuming for organizations, and when it’s done manually, can run the risk of human error. It relies on identifying and mapping tables, variables, derivations and code-lists to apply proper transformations and to ensure full transparency of that data for data lineage.
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