The pharma industry is accustomed to operating in a highly regulated and risk-conscious environment. While companies continue to invest in scientific innovation, they have been slower to adopt technology. 

Long cycle times and uncertainty around new platforms have made organizations hesitant to disrupt entrenched workflows. That hesitation has led to many leaders falling into a “watch and wait mentality,” missed opportunities to lower costs, shorten timelines and deliver new treatments to the patients in need faster. 

Pharma has a habit of implementing “point solutions”, or solving smaller problems incrementally, rather than integrating technologies capable of reimagining workflows from beginning to end. While point solutions can improve specific tasks and address  pain points without impeding existing processes, they limit overall improvements in efficiency and speed. Then, when another pain point emerges, the process becomes a rinse and repeat exercise.

Real-world data and AI are examples of how this habit is beginning to make a U-turn. In a recent Tufts CSDD study, 10 out of 14 companies said they expect the ROI from real-world data to increase over the next one to two years, showing how organizations are moving beyond isolated use cases and applying these tools across the entire drug development lifecycle. Real-world data can help monitor safety signals and identify eligible patient populations, while also improving visibility into these populations, minimizing recruitment challenges, and helping to design trials that mirror how patients are being treated in real world settings. 

Real-world data continues to be valuable even after a drug has reached the market by providing a better understanding of treatment adoption, unmet needs and how therapies are being administered.

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