What will Clinical Research Look Like in 5, 10, 20 Years? Are We Heading to Heaven, Hell or Purgatory? Part 2 of 4

Part-2

The following is the second installment of the excerpted panel discussion, “Future Visions: Are We Heading to Heaven, Hell or Purgatory?,” that Saama Technologies’ Chief Strategy Officer Sagar Anisingaraju participated in on October 28 at MAGI’s Clinical Research Conference – 2019 West, in Las Vegas. The discussion was moderated by Jonathan Zung, Ph.D., member of Saama’s Clinical Board of Advisors and EVP, WCG.

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Jonathan Zung: What role will technology, (i.e., sensors, apps, etc. ) play in clinical research?

Sagar:

Personal technology will play a huge role in clinical research; from Fitbits to watches to phones to specific functional devices, all are at the beginning of the innovation curve and their true impact and contributions are yet to be seen. These and a new wave of devices are going to make a huge difference and generate an enormous amount of data points to analyze and improve clinical research.

Understanding and leveraging these data assets is going to be critical to bring the clinical development transformation we are seeking.

I will give a perspective on what role technology, and more importantly AI, will play. In my view, there are three key areas that we need to focus on: We call them Connect, Contextualize and Converse.

The first area, Connect, is all about how the data assets that are needed, either for new study planning and design or during study conduct, are connected, cleaned, harmonized and mapped. This process of data management is some of the most expensive, resource-intensive, time-consuming and error-prone work that the industry goes thru for every study. Thousands of highly-qualified data managers across the pharma and CRO ecosystem are spending countless hours to ensure the highest data quality for accurate results. Thanks to deep learning and other advances in AI and technology, we are expecting to see a huge change in this data management area. Technology and AI modeling are now available to let a machine understand the data patterns and intelligent overrides that the domain experts are enforcing, as well as integrate continuous learning models into the business process.

For example, we are now working with pharma to predict data queries that would require critical data changes. Such a smart data query mechanism alone is expected to eliminate millions in wasteful expenses and, more importantly, allow for consistent results across studies. A few years ago, innovations such as auto ML and explainable AI techniques were not available for mainstream. Thanks to the tech industry and its pioneering work that has been shared in open domains, we are able to leverage and verticalize that work into the clinical domain. We are seeing some wonderful early results here.

The second area, Contextualize, is about how the right insights are contextualized and presented within a day-in-a-life of the clinical research personas. You see these examples of contextualized, byte-sized insights being presented in our consumer world daily. For example, Google can tell you what time to depart for your next meeting, based on traffic, weather, calendar, distance and maybe few other attributes. Amazon can tell us which products to buy when it is really relevant. We asked our AI researchers why the same deep learning methods cannot be applied for study managers, CRAs, data monitors, portfolio execs, protocol designers and the hundreds of other clinical researchers who are doing the heavy lifting daily. We expect more and more practical innovations to occur in this area.

The third area of technology and AI innovation is around Conversation with the clinical data. Intelligent bots for study managers and humanoid comfort bots for patients will become the norm. These virtual assistants will become smarter over a period and will be continuously learning the intent of the users. Valuable clinical experts’ time need not be wasted on a majority of questions to which these virtual assistants can respond. We expect millions of dollars of savings across the spectrum with these innovations.

Connect, Contextualize and Converse are three areas where I see technology and AI playing a key role in future clinical studies.

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Moderator: Jonathan Zung, Ph.D.

Member of Saama’s Clinical Board of Advisors and EVP, WCG

Speaker: Sagar Anisingaraju

Chief Strategy Officer of Saama Technologies.

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About Sagar Anisingaraju

mmAs Saama’s Chief Strategy Officer, Sagar Anisingaraju creates strategic initiatives that lead Saama into emerging business areas and products with competitive differentiation. Sagar was recognized by PM360 in 2019 as one of the "100 Most Influential People in the Healthcare Industry" for his expertise as a strategist. Northwestern University’s Kellogg School of Management published a case study on the transformation journey that Sagar led for its MBA class of 2018. Sagar was also recognized as the Chief Strategy Officer of the Year in 2013 by Innovation Enterprise.


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