Introduction. The current imbroglio in health care data is highlighted by an escalating volume of unstructured, heterogeneous medical data with little embedded predictive analytics or machine learning ?This situation will soon be far more complex and daunting with the advent of both genomic data (as a result of the high throughput next generation sequencing) as well as physiologic data (from home monitoring and wearable physiologic devices).
　　The complex portfolio of health care data includes not only electronic medical records (patient encounters, vital signs, laboratory results, prescriptions, etc.) but also advanced imaging studies (such as MRI, CT scans, and echocardiograms and angiograms).? In addition, it is estimated that about 80% of health care data is unstructured.? Lastly, current estimate of health care data volume is above 150 exabytes in volume and escalating rapidly.
　　Despite the large volume, variety, and velocity of big data in biomedicine, there is little dividend in the form of information from this health care big data.? Yet, there are opportunities for utilizing health care big data to reduce costs: high-cost patients, readmissions, triage, adverse events, and treatment optimization. The continuum from data to information and from knowledge to intelligence starts with precise database management and accurate data analysis.
　　The exponential convergence of existing biomedical data with both genomic and biophysiologic data will render medical data to be even more voluminous, complex, and heterogeneous.? This explosion of medical data will need a more sophisticated database management strategy as well as cloud and virtual environments to enhance data discovery as well as ensure data security and privacy.
　　First, future medical data can be managed in a graph-based meta-database management system with real time analytic processing for both its storage capability and its query flexibility to accommodate the large and complex medical data in the ensuing decades.
　　Second, future medical data in the customized cloud infrastructure will be far more sophisticated than a simple public-private dichotomy and can be customized from a cloud infrastructure system based on customer vs supplier control, ownership, and responsibility as well as private vs shared infrastructure and operations; cloud security can be further enhanced by mechanisms such as homomorphic encryption and differential privacy.
　　Third, a software-defined data center (SDDC) architecture can be entirely virtualized so that the infrastructure that includes compute, network, and storage abstractions will result in IT as a service (ITaaS).? The future medical data system will be entirely in a virtual synergy with humans and contribute to medical intelligence.
　　Finally, the future of biomedicine can include a proposal for an artificial intelligence-inspired cloud continuum of data-information-knowledge-intelligence (a “medical intelligence” as a service, or “MIaaS”).? By embedding intelligence into all aspects of medical data from graph database and meta-database management system to customized cloud infrastructure and to software-defined data center and virtualization, the aforementioned strategies can accelerate this transformation in biomedicine from fragmented and unstructured data sets to cohesive and agile information imbued with medical intelligence.
Conclusion.? By embedding intelligence into all aspects of medical data from graph database and meta-database management system to cloud infrastructure and even software-driven virtualization, the aforementioned strategies can accelerate this transformation in biomedicine from fragmented and unstructured data sets to cohesive and agile information imbued with medical intelligence. We can then finally have not only a central nervous system but also a brain for biomedical systems .

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