ABSTRACT
Future medical discoveries and breakthroughs remain uncertain yet inevitable. It is clear, however, that healthcare analytics must evolve to meet both existing and forthcoming challenges of clinical research. Generally, areas requiring evolution can be grouped broadly into: issues with data and computing, expanding end-user requirements, and specialty care needs. The complexity and magnitude of computing demands currently inhibit effective delivery of healthcare analytics, including massively parallel computing environments and ever-evolving specialized algorithms. Adoption of analytics by healthcare organizations (HCOs) and meeting the ongoing need to efficiently process large quantities of heterogeneous data for use by end users remain difficult. Providing “personalized care” through use and integration of genomic and other unique biomedical data plus the associated challenges of “unlocking” information from clinical documents represent two of the most important and complex data issues.
