If you spoke to someone 5 years back about the landscape of eClinical software technologies, they would likely talk about the various systems that primarily capture the different aspects of clinical trial data (subject level and operational level). These were predominantly classified as:

  1. Clinical trial management system
  2. Electronic data capture system
  3. Interactive voice response system
  4. Electronic patient reported outcome system
  5. Imaging management software
  6. Laboratory information management software.

However today the scope of software in pharmaceutical research has widened tenfold and there are many more areas where eClinical systems are being used. I would like to focus this article to talk a little more about what the landscape for use of advanced data analytics looks like in the pharma industry in the coming years.

Patient e-recruitment – These systems are primarily aimed at providing the right patient pool for the future studies based on past feasibility analysis and also on the current epidemiology with the right geographical mix. This helps a lot in choosing the right investigative sites. The recent merger of Quintiles and IMS may lead to a new paradigm in recruitment they call the “Precision Enrollment” effort. QuintileIMS believe it will both speed up and streamline its patient search and site startup process for oncology trials.

Real world evidence – Pharma companies have a lot of insights to be derived from real world ‘big data’. Real world evidence can be generated from EMR/HER, claims data, pharmacy databases, wearable devices, research kits of Apple, Microsoft’s Health Vault, and sentiment analysis from patient forums like patientslikeme.com or smartpatients.com. There is an abundance of information available to analyze the patient pathways which describe the choices they make on treatments and diagnosis.  It also helps to understand the pathways of HCP engagement and referral on prescription drugs which in turn leads to yet another insight on comparison between clinical guidelines and real world drug utilization patterns.

Patient generated health data – With the advent of wearables and the ‘internet of things’ (IoT), digital health and lot of smart sensors, we are going to see billions of data points emerging from people in everyday life on multiple vitals or measures which in turn could be used by pharma in various ways. As an example the recent partnership with Teva and IBM Watson platform is focused on leveraging cognitive computing to help doctors, patients and payers to better manage chronic conditions like asthma, plus track treatments.

Whatever we see in the future it is clear that deriving actionable insights from the mass of data available to us will be at the center of the next generation of eClinical applications.