Turning healthcare challenges into big data opportunities: A use‐case review across the pharmaceutical development lifecycle

Abstract

Editor's Summary In order to draw meaning from the exponentially increasing quantity of healthcare data, it must be dealt with from a big data perspective, using technologies capable of processing massive amounts of data efficiently and securely. The pharmaceutical industry faces the big data challenge through all phases of the drug development lifecycle. Genomics, clinical monitoring and pharmacovigilance illustrate the value of a big data approach. Whether focusing on genetic and environmental disease risks, pattern detection through real time biosensors for patients or post‐market monitoring of drug effectiveness, each area involves the collection and analysis of numerous variables and requires extreme computing power to reveal the details of interplay between the variables. To make big data work for pharmaceutical information, attention must be paid to data collection on a vast scale, from multiple sites and over long time periods. Big data support must be incorporated into interoperable electronic medical records and presented intuitively through visual analytics.