The recent explosion in data generation is upending the traditional biopharma business model at a time when R&D costs are also rising due to skeptical payers and a need to demonstrate real world value. What if Biopharmas could integrate such data into their R&D programs to identify significantly more quickly efficacious drug targets, while enhancing the efficiency of clinical trials? What if companies could harness data from a variety of sources – electronic health records, payers’ claims, genetic tests, and wearables -- to design more differentiated therapies? Put more simply, what if companies could combine these different streams of data to see a personalized “big picture”?
Artificial or better described augmented intelligence, using computers to rapidly and the intelligently analyze data and generate insights, could be a critical enabler of this data-rich future – and a new cohort of start-ups, many with backing from technology and enterprise analytics players, hopes to capitalize on the growing need. Beyond the opportunity, there are real challenges: biopharmas are only beginning to understand how best to use and combine data, much of which remains siloed across disparate organizations; there are also significant cultural, technological and regulatory barriers to new data-enabled business practices. In this session, we move beyond the buzz words to discuss with innovators and incumbents how AI will transform the biopharma value chain – and where it will happen first.
Session ID: 24729