Innovative Data Science And Real-World Analytics Approaches In Practice

We are now at the center of a perfect storm where a combination of forces is driving a transformational shift in how drugs are ultimately developed and accessed by patients.

With the rise in digital technologies, there has been an explosion in volume and type of data sources we can obtain from social media data and mobile apps, to wearable sensors and electronic health records and insurance claims data. This data could yield a more robust and complete picture of diseases, the patient journey, and the effectiveness of interventions in the real world to make better drug development, reimbursement, and clinical decisions. However, apart from accessing and curating this data, we also need to harness advanced analytical techniques including sophisticated statistical methods, machine learning, and artificial intelligence to realize the full potential of the opportunity and unlock the insights from this data.

New data sources bring new data dynamics to exploit such as real-time streaming of data, greater longitudinal patient records over time, and of course larger, more representative patient population data. Yet, alongside the opportunities, the new data sources bring inherent challenges to be overcome including lack of standardization, missing data, and variation in quality.