Guest post by Priya Sapra, chief product officer, SHYFT Analytics.
As the healthcare industry continues to become simultaneously more patient-centered as well as more performance-oriented, healthcare organizations and biotech companies alike are taking a closer look at how they can improve clinical quality measures. Although the industry has been widely criticized for a lack of meaningful, uniform industry standards, there’s no denying the link between understanding clinical effectiveness and improving overall patient outcomes. To truly assess quality, organizations need to make sense of the myriad of real-world evidence (RWE) data they already have at their fingertips.
RWE data enables a comprehensive understanding of data physician utilization patterns, patient treatment options, drug comparative effectiveness and more. However, the current, typical approach to RWE – a vast array of siloed databases, services-dependent, with access restricted to just two or three “power users” – has shown to be utterly ineffective. In fact, market estimates suggest big pharma spends $20 million dollars on average annually on RWE, but they are still no closer to fully understanding the real-world impact of pharmacologic and non-pharmacologic treatment on patients.
The problem is not a lack of data, but rather an inability to access RWE data quickly by the very people who are best suited to make sense of the information. Current strategies and tools simply cannot access, analyze, and deliver insights quickly enough for the information to be of use to the organization. However, new approaches to data analytics are ready to eliminate these historical roadblocks and transform RWE data into meaningful insights that can help measure clinical quality effectiveness.
Leveraging cloud-based analytics is one such approach. These solutions are increasingly becoming a critical tool to uncover how quality care initiatives are progressing. Unlike tools of the past, cloud-based offerings can provide rapid access to the data and derived insights in the language that resonates most when measuring quality. For instance, delivery via the cloud enables the real-time scalability necessary for RWE data. As the variety, volume and velocity of RWE data continues to increase, on-premises solutions simply cannot scale quickly enough to contend with terabytes of data and the analytic demands of its users.