PharmaSignals by SetuServ

Voice of Healthcare Providers and Patients

Data gathered from clinical trial feedback and patient studies provides deep insights on drug effectiveness for pharmaceutical brands. In addition, unsolicited organic feedback from patient conversations across social media channels can significantly augment current methods to provide immediate feedback to pharmaceutical companies, uncovers early warning indicators on drug market reception and HCP influence. PharmaSignals by SetuServ offers such rich insights including early market reception, HCP tracking and feedback, flash trends and sentiments on a variety of organic topics.

 

Mining Insights and Signals from Pharma Data

 

Pharma brands have access to rich data and are sophisticated with their analysis. However, most of the data sources have a lag of a few weeks to months. Mining publicly available social feedback and publications data yields near real-time insights. Examples from data sources include:

    • Volume of Discussions (e.g. ~1 MM tweets on Leukemia alone)
    • Audience Variety: Discussions from several stakeholders 
    • Data Velocity: Tracks organic topics from online stakeholders on a near real time bases for immediate trends and feedback

Common Challenges in Social Listening for the Pharmaceutical Industry

 

    • Compliance: Pharma brands fear legal risks/AE
    • Lack of clear regulations and guidelines by the FDA 
    • Noisy data – Quite a few discussions contain data that is not relevant to the drug or topic in question.. Finding valuable insights in such data is similar to the problem of “finding needles in a haystack”. 
    • Extensive manual research – As multiple Text Analytics models are required to analyze different drugs.
    • Organizational ownership and operational integration

Get deep and accurate signals & insights

 

We understand the limitations of fully automated text analytics models pertaining to inaccuracy in results. Our solution mitigates them by combining both manual research and automated text analytics models designed for different drug types to increase the accuracy of signals and insights. Thus, helping in delivering more actionable insights.