Machine Learning: The Next Frontier in Pharma Intelligence
In pursuit of better answers
Chances are, if you are a pharma 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.