Relationship advances key aspect of Sanofi’s digital strategy, providing behavioral insights across company, including identification of ‘digital biomarkers’.
Medicines aren’t the only tools that biopharma companies like Sanofi are using to help patients. Information technology has become a key tool in every part of Sanofi’s business. Among the most important digital tools are data and analytics, which enable Sanofi to apply insights gained from real world evidence to the discovery, development, and delivery of new medicines.
To advance that aspect of its digital strategy, Sanofi has strengthened its relationship with Evidation Health. The two companies will work together over the next three years to use Evidation’s Real Life study platform to help increase Sanofi’s understanding of the daily burden of disease and develop innovative solutions that help improve therapeutic outcomes. Sanofi will tap Evidation’s expertise as a pioneer in quantifying the impact of various real-world factors, including patient behaviors, on the eventual outcome of a course of treatment.
About Sanofi’s relationship with Evidation
This relationship is an important step in implementing Sanofi’s digital strategy. One of our big priorities is to use data and analytics, and especially what we call real world evidence, to help us discover and develop better drugs, and to improve patient outcomes. Working closely with Evidation and its distinctive analytics platform will help us better understand the factors that affect patient outcomes. We can then use those insights to support patients and healthcare professionals in managing disease and delivering the most value from our therapies. As an example, we have already worked with Evidation Health to identify behavioral markers that could improve treatment adherence and guide positive lifestyle change in patients with type 2 diabetes.
Heather Bell, SVP and Global Head of Digital and Analytics at Sanofi.
Digital Biomarkers – Behaviors Influencing Outcomes
The data Evidation Health is able to analyze is increasingly important; in part because of the insights it can provide to guide improved patient outcomes.
Healthcare professionals and research scientists have long understood that behaviors and environment can play a significant role in how people respond to medication. In some cases, those factors can even help predict how likely it is for an individual to get sick in the first place. Until recently, however, the challenge has been discovering which previously unmeasurable behaviors or circumstances are linked to a specific condition or treatment, and how significant a role they play in outcomes.
As more patients become “connected,” however, the data needed for that analysis is becoming more widely available. Patient-generated information from wearables and mobile devices is growing rapidly; last year, 46% percent of U.S. consumers were considered active digital health adopters, while 24% owned a wearable and 22% were actively tracking at least one key health factor via a mobile application, according to a national survey conducted by Rock Health.
Consumers are sharing their digital footprint through wearables, sensors, and apps, and we need to listen, so we can better help them navigate their day-to-day health Journey. By partnering with Sanofi’s innovative team, we have an opportunity to better manage population health in the digital era by illuminating underlying health behaviors in a privacy-safe and patient-permissioned way.
Christine Lemke, Cofounder and President of Evidation Health.
This explosion of real-world data includes both traditionally available data as well as behavioral measures like activity levels, diet, even a patient’s physical location and the local weather. Machine learning and analytics practices can pinpoint the most important of these factors, creating a set of “digital biomarkers” that are analogous to physical biomarkers, such as blood sugar levels, that can be used to guide treatments for the best outcome. Additionally, digital biomarkers can predict the onset of a condition – for example, understanding the connection between breathing anomalies and a serious asthma attack, or correlating the decrease in a person’s frequency of social media interactions with the likelihood of an episode of severe depression.