When people think about real-world evidence, they generally think about using these data to address questions around drug effectiveness, or population level safety effects. But there are many applications that “real-world data” can address. If you think of real-world data as any type of information gathered about drugs in non-trial settings, a whole world of possibilities opens up:
- Social media data can be used to understand how well packaging and formulations are working.
- Customer call feeds can be analyzed for trends in drug switching, off-label use, or contra-indicated medications among concomitant drugs.
- Full text literature can be mined for information about epidemiology, disease prevalence, and more.
Text Mining transforms Real-World Data to Real-World Evidence
Many of these real-world sources have unstructured text fields, and this is where text analytics, and natural language processing (NLP), can fit in. At Linguamatics, we have customers who are using text analytics to get actionable insight from real-world data – and finding valuable intelligence that can inform commercial business strategies.
In this blog, we will be looking at two different Linguamatics customer use cases, where text mining has been used to transform real-world data to real-world evidence.