Sam Li-Sheng Chen

As informatics are likely to have a significant impact on the efficiency and quality of hospital medicine, ambulatory medicine, telemedicine, and public health. A telemedicine system can support multiple tasks in diabetes care including surveillance, on-line health education, risk assessment, and self-care. At the crossroads of medical informatics and public health, Cybermedicine is proposed to support self-care, consumer empowerment, and prevention. Cybermedicine deals with the global exchange of open and non-clinical information, mostly from patient-to-patient, sometimes from patient-to-physician and from physician-to-physician using information technology. The revolution of digital health is now moving forward to big data movement across systems and services.

Big data are characterized by 7 Vs including volume, velocity, variety, variability, veracity, visualization, and value. Targeting data on clinical medical data and community health data, big data is to process data storage and management, analysis and processing with various kinds of state-of-the-art technique, and the translation of their results into health decision-making.

To develop consumer-based informatics, the “BlockChain” model can be used. The BlockChain technology is designed to streamline the sharing of health records in a secure way and give data owners more control over their information. Based on this BlockChain model, the enterprise model for sharing health data to support EBM prevention and to develop the applications of digital health becomes feasible in the near future. These aid the feasibility of reaching the goals of P4 medicine proposed by Leroy Hood and comprised of predictive, personalized, preventive, and participatory aspects. In order to develop a successful precision prevention medicine, advanced informatics and technologies can be used to generate and process a large biological dataset (omics data). For a better understanding of the molecular mechanisms, processes and pathways on specific disease, it could be very helpful from enormous potential in the integration and use of omics data to identify the states of disease progress. It is a challenge of dealing data standardization, data sharing, storing Omics data appropriately and exploring Omics data in bioinformatics.

The main Omics fields are genomics, transcriptomics and proteomics. By means of functional integration analyses, a valuable platform should be constructed by integrating these fields. The influence of other elements such as epigenomics, pharmacogenomics, metabolomics and environmental factors is important to have a better and more understanding of risk pathways. The assessment of Omics data is promoting a critical shift in development of successful precision medicine.

Big data can generate significant financial value to save 12% ~ 17% of healthcare costs. $300 billion in cost reductions was estimated by McKinsey Global Institute Analysis.