Is Your Oncology Analytics Organization Best in Class Yet?

Posted by Arup Das on Oct 25, 2018 8:00:00 AM

Arun Jain co-wrote this blog post with Arup Das.

Imagine a world where oncology analytics transform the way that a pharma manufacturer engages with its customer: Sales reps have real-time insight into where patients are being diagnosed. Accurate predictions help them anticipate when to follow up with a customer with a relevant message about their soon-to-relapse patient. Customers give them unencumbered access because they trust that the manufacturer will engage with them through their preferred channels at the right cadence. Does this sound too good to be true? Some oncology companies are already exploring these possibilities today, but according to ZS’s recent benchmarking study of oncology analytics organizations, many are not there yet in their analytics maturity and are missing out on critical opportunities to engage their customers when it matters most.


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A New Way of Doing Primary Market Research in Oncology

Posted by Aritra Das on Aug 7, 2018 8:00:00 AM

Carolyn Morrow co-wrote this blog post with Aritra Das.

Pharmaceutical companies face numerous obstacles when conducting primary market research in the oncology space. For one, the complex and nuanced clinical data that’s being presented to oncologists can cause decision paralysis, making it difficult for traditional market research to reliably predict future market outcomes. Second, various cognitive biases create a gap between how HCPs respond to a survey and how they actually behave in real life. Third, real-world group decision-making (tumor boards, for example) can’t be adequately mimicked in a one-on-one market research capacity. Lastly, to add to it all, overcrowded markets are creating an overload of research requests directed at the same set of HCPs, which causes survey fatigue and leads to lower-quality responses and lower response rates.


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AI Is Driving Change in Oncology Practice and Commercialization. Are You Part of the Wave?

Posted by Shankar Viswanathan on Feb 15, 2018 9:15:07 AM

Arup Das co-wrote this blog post with Shankar Viswanathan.

Looking back at 2017, artificial intelligence and machine learning made impressive progress when it comes to improving cancer diagnosis and treatment. Using deep learning, computers are scouring images to detect signs of breast cancer in mammograms earlier than humans are currently capable of. Using AI “random forests,” a learning algorithm, investigators are more accurately predicting which drug combination will work better in BRAF mutant melanoma. The AI-facilitated discovery of Berg Health’s BPM 31510, a pancreatic cancer drug, has entered human development clinical trials. AI is not only powering oncology drug discovery, faster detection and personalized treatment but also helping to improve oncology commercialization effectiveness and agility through analytics. 


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