June 1, 2015 (Vol. 35, No. 11)

The increasing availability of unique datasets and access to big data from various sources are providing us with knowledge about the patient population we never had before. These data can come from both “traditional” sources, such as genomic information or primary research, and also newer sources, such as social media or wearable devices.

Applying advanced analytics to these datasets can drive insights across the life sciences and healthcare landscape, resulting in improvements to patient care and the value delivered for the cost of care. In biopharma, there are specific opportunities to use this population data to generate significant value across the value chain, including areas such as early research, clinical development, marketing, and pricing.

In the development space, advances in the ability to sequence and analyze large genomic datasets are delivering breakthroughs in identification of new therapeutic targets, and enhancing understanding of which patients would best benefit from or tolerate specific therapies. Also, it is anticipated that these advances will eventually help with patient selection for targeted clinical trials and ultimately personalized medicine. Further, wearable technology promises that trial participant outcome information will be collected in a real-life setting, breaking down barriers that have historically inhibited participant recruitment, and enabling improved safety and efficacy data.

When new biopharma treatments move in to the commercial space, there are additional opportunities to leverage patient insights delivered by big data. For example, a challenge for biopharma has been setting the right price for products. This requires using analytics to create a holistic view of levers impacting pricing decisions—which include market size, access, patient population, and competitive landscape.

These same data can also significantly benefit marketing efforts by guiding manufacturers to the right customers and helping them use marketing dollars more effectively. Navigating the data complexity inherent in these approaches will involve new analytics capabilities and tools. If biopharma can adopt them successfully, significantly reduced time-to-market and substantially improved market share are in reach. 

Todd Skrinar is a partner at Ernst & Young and the West Coast leader of the firm’s life sciences advisory practice.

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