DRG uses cookies to improve your experience on this website. Some of the cookies we use are essential for parts of the website to operate. Please be aware that if you continue without changing your cookie settings, you consent to this. For more information on our use of cookies, please review our cookie policy.

Machine Learning: The Next Frontier in Pharma Intelligence

 

In pursuit of better answers

Chances are, if you are a biopharma executive, you will already have devoted plenty of time and energy to exploring the possibilities of machine learning. Regardless of whether you had been a proponent or a skeptic, the bold claims and non-stop buzz surrounding machine learning will have grabbed your attention and challenged your thinking, demanding that you evaluate its potential and take a position on its suitability for your business.

 

Machine learning represents a giant leap forward in advanced data analytics. The ability to integrate real-world data sources and build a dynamic picture of markets, patients, providers with such depth, granularity and precision has unlocked a vast goldmine of untapped patterns and insights that could inform (and potentially transform) strategy and drive growth for the business. By creating a dynamic, multi-faceted view of the landscape, machine learning enables the data to speak for itself and reveal the true story, without human bias.

 

How machine learning can transform commercial planning

In simple terms, machine learning enables teams to look at all the data at once, deploying an algorithm to reveal which are the most important patterns of data. It extends way beyond a linear analysis, offering a dynamic understanding of the market, lending itself to a number of planning activities, such as market sizing, patient segmentation, targeting, provider segmentation, payer segmentation, and messaging, as well as Health Economics and Outcomes Research (HEOR) activities.

 

Because patients are profiled in a holistic, multi-faceted way, it enables us to see what other diagnoses they have, what healthcare services they are using, what other treatments they are receiving, and any number of other characteristics from the data sources. We can also find undiagnosed patients by building profiles of known patients, and using these flags to identify other patients among the general population, to find undiagnosed patients.

 

Download Full Analytics Viewpoint

One example is the NASH (nonalcoholic steatohepatitis) market which includes a largely unaware and asymptomatic patient population. In a recent project, we deployed machine learning techniques with the goal of expanding the NASH population beyond diagnosed patients and creating meaningful sub-segments. This required an analysis of the progression to NASH to determine key characteristics among potential patients, matching EHR and claims records to build a profile of known patients, and machine learning modeling to flag undiagnosed patients based on key characteristics (i.e., elevated liver function tests, comorbidities).

 

But it doesn’t always have to be a battle between machine learning and traditional methods. There are scenarios when it’s perfectly fine to run the old-school analysis because the business question may not require advanced methodology. In fact, there are cases when machine learning might over-complicated the analysis.

 

Want to learn more about when and how to apply machine learning to your business questions?

 

Download the full Analytics Viewpoint to learn:

  • Planning use cases and how machine learning can help
  • 3 elements of machine learning project success
  • 4 stages of machine learning adoption
  • Data science partner must-haves