White Paper

Pharma's Digital Awakening: Research-Ready Health Information And AI To Reduce Cost And Deliver Better Treatments

Source: Ciox Health - Life Sciences

By Florian A. Quarré, Chief Digital Officer, Ciox Health

Block Chain, AI, Internet Of Things: Future Of PharmaTech?

Digital innovations and their emerging technologies, such as artificial intelligence (AI), advanced analytics, and cloud-based computing, are transforming industries and markets across the world as they offer novel ways to boost R&D, increase product quality and safety, and ultimately improve customer satisfaction. For example, the digitization of the automotive industry now allows manufacturers to decentralize production and distribute their supply chain, leading to partner systems’ integration via cloud connectivity.1 Giving all stakeholders the ability to analyze the same data increases productivity and lowers costs through fluid design, manufacturing, and delivery, while being increasingly closer to the consumer and their personalized requirements. This “digital awakening” offers consumer-centric products and service innovations that stand to transform not just the products that are built, but how and for whom they are built.2

The pharmaceutical industry is striving for a similar type of revolution, where drug companies determine the best course of treatment for a disease based on a unique patient’s physiology and their ability to metabolize targeted drugs. This customized approach to healthcare leading to personalized medicine engages with a precise patient population, rather than a large, undifferentiated market of patients. Using real-world data to represent the evolving demands and requirements of targeted markets—as demonstrated in the automotive industry—would create drug manufacturing pipelines that are living ecosystems, ushering flexible production into the pharmaceutical industry. Specifically, an agile, digitally driven supply chain uses AI to sift through the ocean of information to support the engagement of target patients, identify early efficacy indicators and potential safety issues, leading to adaptive design, manufacturing, and distribution. This ability to go from real world data to real world evidence could dramatically reduce investments overheads, shortening time to production and increasing positive patient outcomes by creating a holistic improvement in their health.