Thursday, January 15, 2015

Applying Analytics to Clinical Trails

The below link is a good article on using Big Data Analytics to improve the efficiency of clinical trials.

Snippets from the article -

"Recruiting patients has been a challenge for pharmaceutical companies. 90 per cent of trials are delayed with patient enrollment as a primary cause.  
Effective target segmentation for enrollment is a key to success. Traditional methods of enrollment rely upon campaign and segmentation based on disease lines across wider populations. Using data science, we can look at the past data to identify proper signals and help planners with more precise and predictive segmentation. 

Data scientists will look at the key attributes that matter for a given patient to successfully get enrolled. For each disease type, there may be several attributes that matter. For example, a clinical trial that is focused on a new diabetes medication targets populations’ A1C levels, age group, demographics, outreach methods, and site performance. Data science looks at the above attribute values for the target users past enrollment data and then builds ‘patient enrollment propensity’ and ‘dropout propensity’ models. These models can generate multi variant probabilities for predicting future success. 

In addition to the above modeling, we can identify the target segment’s social media footprint for valuable clues. We can see which outreach methods are working, and which social media channels the ‘generation Googlers’ are using.  Natural language processing (NLP) techniques to understand the target population’s sentiment on clinical trial sites, physicians, and facilities can be generated and coded into a machine understandable form. Influencer segments can be generated from this user base to finely tune campaign methods for improving effectiveness."

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