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Leveraging Big Data To Crack Open Oncology Insights

Infosys has a framework to evaluate the usage and applicability of Big Data across different segments of the pharma value chain in Oncology.



David Smith, Chief Information Officer of a Top 10 pharmaceutical company specializing in Oncology, must provide the leadership and vision for developing and implementing Information Technology initiatives that align with the strategic vision of the organization. One of David’s key focus areas is leveraging enormous amount of unused data.

The Human Genome Project, which lasted more than a decade, served as the source and foundation of data evolution in the field of Oncology. The ability to sequence genomes in a few hours and for a few thousand dollars has generated overwhelming volumes of data that is challenging his organization’s abilities to aggregate, accumulate, and analyze this information to derive actionable insights.

One can easily imagine the complexities when a disease involves 225 indications across the entire human body and includes a variety of treatment modalities including surgery, radiation, chemotherapy, biologics & targeted therapies, laser ablation, bone marrow transplants, and radioisotopes.

Here are a few pointers to comprehend the data explosion in Oncology and the challenges it poses:

• Increase in disease complexity: A vast knowledge of mutations has expanded the general study of cancer into multiple rare indications and specialized classifications.

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Rapid increase in the volume of data:

a) NIH’s Cancer Genome Atlas generated 2.5 petabytes of data in 2014.
b) The volume of data generated in cancer research has increased. The number of published cancer research papers had risen exponentially from 700,000 in 1984 to 3 million in 2014.

Insights locked in the databases:

Today, 1.7 million people in the U.S. have been detected with cancer. However, only 3 percent participate in clinical trials, which means effectively 97 percent of the patient population (~ 1.65 Mio.) is out of reach. Our ability to manage data will directly translate into the ability to reach and treat patients better.

Big Data is the big buzz word on the block and David wants to explore the options of investing across varied projects utilizing Big Data analytics. One of the byproducts of David’s task is the development of a working definition of Big Data. He senses that the term means something different to everyone, and nobody has ever proposed a rock solid definition or parameters as to what Big Data is in the medical and pharmaceutical fields. 

We are constantly on the lookout for ways to target only the mandatory genes so that the treatment benefit could be maximized and resistance to the drug could be minimized. If Big Data can help us achieve this goal, then we might be looking at the next big blockbuster drug.

David is advised by key stakeholders in his company that although there are plentiful opportunities, not all might qualify for Big Data analysis. Furthermore, he is told that large volumes and varied data do not automatically help business leaders prioritize which business areas or situations to focus on from a data analytics point of view. 

Martha, Senior Director in the R&D division, points out to the importance of conducting data analytics in drug discovery phase. “We are constantly on the lookout for ways to target only the mandatory genes so that the treatment benefit could be maximized and resistance to the drug could be minimized. If Big Data can help us achieve this goal, then we might be looking at the next big blockbuster drug,” she says. 

Paul, Senior Director in Sales & Marketing, pitches in: “Conducting data analytics in the R&D space definitely has its benefits, but if we run some analytics on our customer-facing programs, we can expect results in a much shorter time frame. For instance, we might benefit from an upside impact on sales if we can run data analysis on our drug adherence program and make some program modifications.” 

David’s challenge, very typical of the one faced by many senior leaders of top pharmaceutical companies, is to prioritize business-critical areas for application of Big Data. If he had access to a framework that captures the nuances of humongous data availability within Oncology, he would be more certain about which project areas are worth his investment.

Establishing a Framework for Big Data in Oncology

To harness Big Data technology for delivering actionable insights and taking into account the multifarious sets of data that come into play in pharma, Infosys has come up with a framework to evaluate the usage and applicability of Big Data across different segments of the pharma value chain in the Oncology therapeutic area. We have defined the framework along the basic principles of Big Data, taking into consideration the timeframe for solution and implementation and the influence on revenue.

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For the evaluation of the cases for volume parameter, we defined Systems/Solutions as:

Efficient: Current Systems/Solutions can process data with speed, without information loss, with minimal incremental costs.

Adequate: Current Systems/Solutions can process data, however there are limitations with respect to speed of processing or considerable information loss or high incremental costs.

Landscape Analysis of Problem Areas in Oncology

To utilize the potential of Big Data to the fullest extent in order to facilitate incisive decision-making, we propose to first identify the key problem areas and challenges in the Oncology segment across the value chain. After performing an exhaustive analysis of challenges in Oncology, we came up with Top 10 problem areas alongside the opportunities they present and their data availability.

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Heat Map for Big Data Use Cases

Applying the defined framework across the listed problem areas, we have created a heat map to demonstrate the relative importance of Big Data across various use cases. That way, Mr. Smith knows exactly where in the production line he needs to apply and parse Big Data and where it is of relatively lesser importance. Such knowledge will have tremendous cost savings on developing and bringing a new cancer drug to market.

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The metrics in the framework can further be scored based on current state assessment and future state vision of the company. Traditional ROI and NPV based analysis should be used in conjunction with the Key Performance Indicators to identify potential avenues to leverage big data technology in Oncology. 

Conclusion

A framework for potential evaluation of usability and applicability of Big Data technology in Oncology has been presented in this paper. Although Big Data technology is a propeller, it’s not a catchall solution and pharma should be aware of how to utilize just the right and most effective data at the right time. Big Data is not a “one-size-fits-all” solution. Rather, it’s a tool that, framed the right way, can drill down and give a pharmaceutical company executive like Mr. Smith the results he needs in less time and in far more efficient ways.


About the authors

Shreela Murugesan is a Business Consultant with the Life Sciences and Services practice at Infosys Management Consulting Services. She has 3+ years of experience in new product development, strategy consulting, new market entry and digital market transformation across the Life Sciences industry. Shreela can be reached at Shreela_Murugesan@infosys.com

Ashish Kumar is a Business Consultant with Infosys Limited with 5+ years of work-experience across Analytics and Sales & Marketing within the Life Sciences Industry. He is keenly interested in the application of Big Data, Analytics and Consulting particularly within Specialty Care segment.

Ashish can be reached at Ashish_Kumar99@infosys.com

 


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Real World Evidence & Market Access Summit USA

Dec 3, 2015 - Dec 4, 2015, Philadelphia

Leverage Real Life Data & Analytics for Value-based Market Access

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