Social Media Analytics in Pharma-Use Case (Part-1)
Updated: May 3, 2020
In today's era Social Media is contributing a major part in all aspects of Life and Businesses. Its even has put its noteable footmarks in Healthcare and Pharma industry too. The newly generated data and various analytics engines are helping the Pharma industry to discover more personalized drugs which are much deeper in acting and enhancing the patient care too. Using Social media Analytics, several new dimensions are getting explored, which were previously untouchable due to lack of informations. Through proper channel, now Doctor's and Patient's data are easily accessable for many of the industry competators.
Here in this post we will try to understand, using social media data, how several different dimensions of a medicine we can explore. We will discuss the key points of the study. Discussing indetail is out of our scope here.
Key Dimensions and Parameters for a Medicine:
As we have studied, we have found that there are mostly 3 major dimensions which takes part a vital role in measuring a medicine's performance in the market.
Root of Admin
Familiarity Key opinion Leaders
Ability to meet unmet needs
Size of the market
Addressing multiple indications
Top 10 brands of a popular pharma company, named Pfizer:
Benchmark of Portfolio of different products in Clinical, Commercial and Social:
Text Mining for analysis of Social Aspect:
We tried to study the social impact of the medicine named, 'Lyrica'. For that purpose we extracted few reviews of patients on the medicine from Drugs.com and analyzied that using some of the advanced Machine Learning techniques.
Some important words which the patients are discussing:
Then we tried to see what are the experiences of those people, who have given rating between 5 to 8 outof 10, to that medicine.
This patient group is mostly discussing:
We studied word associations in their reviews to understand the contexts of their discussions.
Note: Comparatively BOLDER and THICKER lines between two words mean stronger association between them compare to other words.
Capturing the approriate users:
From a review we extracted 3 different metrics to understand the users and medicine effectiveness better.
First two are captured directly from the review. 3rd one we have calculated from the reviews using Polarity score from Sentiment Analysis.
We can capture those customers who are not satisfied with the medicine Lyrica, from name field we can capture them and accordingly target them for appropriate marketing strategies.
The ‘usefull’ attribute is for the indication of which issue is being faced by most of the patients e.g. here user ‘Danvic’ has given only ‘1’ rating to Lyrica and his/her review has been found useful by 66 users. So we should concentrate on what ‘Danvic’ is saying.
So what we have got to know is using Social Media and other Website's data, we can better understand a medicine's effectiveness and can improve on the areas where consumers are not happy.
In the Next part of discussion, we will see how we can fetch data from Social Media like Facebook and Doctor's Blog to understand the market scenario of different well known drugs. Till then Happy Learning :)