For health and life sciences organizations, discussions about big data include gaining value from that data in the form of real-world evidence.
Consider for a moment the amount of healthcare data that exists today thanks to the adoption of electronic health records. Then think about the future with data from wearables and health monitoring tools from your smartphone, as highlighted by Dr. Eric Topol at the SAS Health Analytics Forum.
Patient records, insurance claims, clinical trial research all add up to real-world data. So how is this data valuable?
For starters, more data means more information for biopharmaceutical researchers. From seeking cures for life-impacting diseases, to preventive measures for mundane health conditions, insights from this data offer confidence in the research. It offers better population targeting for clinical trials. Plus greater understanding of efficacy across demographics is derived.
In short, life sciences companies can make smarter investment decisions. Drug development costs are reduced, as are poor clinical trial outcomes. Healthcare payers benefit from less expensive prescription drugs and providers have greater assurance in care provided to their patients.
But there are significant business and infrastructure challenges. Disparate data sets, huge organizational silos, lack of analytical resources and the necessary time to manage all of these factors exist. This is typically where the conversation around real-world data falters.
However, it doesn’t have to stop there. The right combination of data and analytics tools means benefits can be realized from clinical and claims data.
If you would like to dive a bit deeper, take a look at the FiercePharma on-demand webinar, Maximizing the Value of Real World Evidence and join us in continuing the conversation!