The medical industry is a data-intensive industry, and data accumulation has existed in ancient times. However, at the level of data application, the medical industry lags far behind industries with better informationization such as the Internet, finance and telecommunications. Fengrui Capital Biomedical Technology team analyzed the medical data industry chain from the perspective of data generation, data processing and data consumption. Analysis shows that hospitals, clinics and other professional medical institutions and insurance institutions are still the most important source of medical data generation. Data from mobile apps and wearable devices are beginning to improve data integrity, continuity and accuracy; data processing is a system engineering , including cleaning, finishing, analysis and other standard links, put forward higher requirements for data structuring; as of now, the medical institutions, pharmaceutical companies and insurance companies that pay for medical data, let the patients and doctors at the C end pay for the data. It is still unrealistic. The US medical system is relatively market-oriented, and its investment in the medical system is huge, making it a long-term reference for the development of China's medical industry in pillar industries such as technology, services and processes. In recent years, the medical data industry has developed rapidly in the United States. The Fengrui Capital Biomedical Technology team selected four representative US medical big data companies (Flatiron, IBM Watson Oncology, IMS Health Oncology, Palantir) for case studies. The emergence of big data industry and analysis of medical data investment strategy The development of medical big data brings multiple health benefits. First, IBM defines big data with 3V IBM first proposed the 3V definition of big data. 3V is Volume, Variety, Velocity. Volume is better understood because the “big†of big data itself represents a huge amount of data. There are many reasons for the increasing amount of data. One of them is that machines and networks are generating large amounts of data every day. According to statistics, the amount of data we generate every two days is roughly equal to the sum of the data from the beginning of human civilization to 2013. The second feature is Variety, a variety. Diversification mainly refers to different data sources and types. Data in the traditional sense comes mainly from tables and databases like excel. Humans can now analyze data in various forms and types, such as e-mail, pictures, video, audio, surveillance instruments, and more. The third feature is Velocity, the speed at which data is generated. For example, the generation of data on the Internet is calculated in seconds or even milliseconds. For example, genetic sequencers and network-monitored recordings generate large amounts of data anytime, anywhere. The above three Vs are recognized as big data definitions. At the 2013 Big Data Summit in Boston, Inderpal Bhandar, chief data scientist at Express Scripts, came up with the concept of Veracity. Veracity mainly refers to whether the data is biased, how loud the data is, and whether there are abnormal values. When the industry accumulates data from various sources, whether the data is accurate or not becomes a very significant problem, otherwise it is "Garbage in, Garbage out". Endotracheal Tube,tube endotracheal,endotracheal tube holder,oral endotracheal tube,endotracheal tube price Anesthesia Medical Co., Ltd. , https://www.honestymed.com.jpg)
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