Personalized medicine has emerged as the way forward in the healthcare industry. Healthcare Providers (HCPs) and patients seek information specific and relevant to individual care before making crucial treatment decisions. While sourcing this information, pharmaceutical companies must tread the fine line between medical / business interests and data rights and regulations.
Open data sources, such as social media channels, are powerful allies offering a wealth of information that can be harnessed for a competitive advantage and customer satisfaction. This whitepaper explores the impact of social media listening and how it can be maximized using artificial intelligence and automation to process the massive influx of raw data from platforms like Twitter, Facebook and Instagram.
The crux of the challenge lies in extricating trends and patterns from the vast and complex input data. Applying topic modeling to open source data for a respiratory disorder, this paper illustrates the path to uncovering hidden themes, emerging patterns and meaningful projections. The use case outlines how topic modeling leverages neural network models and unsupervised learning to analyze both structured and unstructured data for relevant topics and key phrases.
For pharma companies today, social media data presents the opportunity to drive invaluable interactions with the burgeoning millennial and Gen Z segments. Navigating this uncharted terrain requires a clear strategy that begins with setting a goal, identifying the audience, active monitoring and specialized expertise.
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