Global healthcare systems are facing fundamental challenges as well as opportunities as a result of a combination of macro-economic, technological, demographic and scientific forces. These include but are not limited to reimbursement reform, rapid advances in science, the explosion of molecular data, and financial pressures to operate more efficiently. It's widely recognized that healthcare IT (HIT) will play a critical role in addressing these challenges - whether that is viewed through the lens of new value based reimbursement schemes or molecularly driven personalized medicine. However, much of the focus on HIT has centered on the core clinical transactional systems and basic interoperability. This new paradigm will require new information management and analytical systems to successfully leverage the diversity of electronic information available to improve quality of care, understand what value means in healthcare and accelerate the translation of research discoveries into practice by providing physicians, researchers, administrators and consumers with actionable data at the right time and place. The need for these new data management systems is particularly imperative in cancer today, where new molecular profiling technologies are first being adopted in clinical practice. The bottleneck is no longer the generation of molecular data but the analysis of that data in a broader clinical context. As a result, successful research and clinical enterprises of the future will be differentiated not by the mere existence of core clinical systems, but by their ability to manage, integrate, analyze and leverage clinical, financial, genomic and other biomedical information from across their enterprise and external to their enterprise. This presentation will focus on the new HIT platforms that will be required to provide a scalable, secure platform for personalized cancer medicine that accelerates biomarker discovery, validation and ultimately decision making at the point of care. This session will also discuss the challenges associated with integrating cross platform ‘omics’ data in a manner that scales to thousands of whole genome sequences whilst integrating with longitudinal clinical data from EMRs, case reports, registries and other “real world” data sources to provide an integrated view across genotype and phenotype. [Display omitted] [Display omitted]
Read full abstract