Session 12 - How Machines Can Complement Humans to Make Faster & Better Informed Decisions: DM Case Studies

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Sunday, September 13, 2020: 8:00 AM - 1:00 PM
/ CEU Credits:0.2


Session Chair
Appalla Venkataprabhakar
Damon Fahimi
Product and Outreach Manager
Uppsala Monitoring Centre
Jennifer Bradford
Director Data Science
Shobhit Shrotriya
India Lead, Life Sciences R&D Services, Accenture Applied Life Sciences Solutions


In the recent times, there has been significant disruption on technology front like IoT, mHealth, wearables, sensor-enabled devices and much more that has resulted to exponential growth of data & this has brought opportunities to leverage use of smart technologies for making informed decisions with speed.

"Technology Explosion" has led to "Data Explosion" 90% of data in world today has been generated in last 2 years.

The development of smart technologies like AI, ML, NLP, RPA, Chabot's etc. aids in analyzing vast amount of data quickly with higher degree of accuracy.

This evolution is primarily fueled by the accumulation of huge amounts of data associated with strong computing power. This in turn enables the training & validation of complex ML algorithms.

It is technically possible today to automate repetitive and simple tasks as the cost & ease of implementation of AI technologies is becoming attractive.

McKinsey did estimate that big data & ML in pharma and medicine could generate a value of up to $100B annually, based on better decision-making, optimized innovation & improved efficiency of research. This in turn would also help us to bring medicines faster to the patients.

This session is all about how augmentation of both humans & machines would result to making decisions @ speed & this would be demonstrated only through case studies where organizations would have deployed & implemented some of these advanced technologies like AI, ML, NLP, RPA in their day today work which would have enabled them to make faster decision's & improved efficiency.



1. How human & machine hybrid approach could help in detecting unusual behavior during clinical data collection.
2. How human & machine journey could lead to Intelligent Clinical Data Science.
3. Knowing about Robotic Drug Coder
4. Evolution from Clinical Data Management to Clinical Data Science