Shulou(Shulou.com)06/03 Report--

Author: Li Yuchen, 2020-06-02 18:57 introduction: what is the focus of the construction of the health care big data platform?

After the epidemic and the two sessions, new medical infrastructure has become a hot topic.

On May 7, Shanghai released the "35 articles of New Infrastructure", with a total investment of about 270 billion yuan for the first batch of major projects. Shanghai has become the first city in the world to make specific plans for new infrastructure.

Medical treatment is the key area of attention in this wave of construction. 5G, big data Center and artificial intelligence are the focus of the development of science and technology medical treatment in recent years.

Sun Pishu, chairman and CEO of Chaochao Group, suggested at the "two sessions": relying on health care big data and integrating epidemic situation, medical treatment, transportation, communications, tourism and other data, to form a set of automatic and intelligent epidemic situation big data coordinated monitoring, early warning and emergency response mechanism, to achieve online health status monitoring, online revisit, and follow-up monitoring, while fighting the epidemic, while maximizing the protection of medical personnel.

However, the construction of public health system is a systematic project. And a big base and core grip of this project is the medical big data platform.

Where is the policy "spring breeze" blowing?

Medical big data is not a new concept.

In recent years, the government has launched a series of policies and actions one after another.

In October 2016, China's health care big data officially entered the stage of implementation. Fujian Province, Jiangsu Province, Fuzhou, Xiamen, Nanjing and Changzhou were identified as the first batch of pilot provinces and cities for the national pilot project of health care big data center and industrial park construction.

In the first half of 2017, within three months, the "national team" of the medical big data industry-- China Health Medical big data Industrial Development Group, China Health Medical big data Science and Technology Development Group, and China Health Medical big data Co., Ltd., have announced preparations for establishment, and the pattern of the three major groups has been quickly established. In the same year, the second batch of national pilot projects were carried out in Shandong, Anhui and Guizhou provinces.

In September 2018, the National Health Commission issued the National Health Care big data Standards, Safety and Service Management measures (for trial implementation), which standardizes the medical and health big data industry from the perspective of standardized management, development and utilization.

Under the policy spring breeze that blows almost every year, what is the focus of the construction of the health care big data platform?

An article in the China Social Science Daily by the School of Public Health of Southeast University lists several key points:

The first is the important monitoring of disease prevention and control. For example, to build a platform for the prevention and control of chronic diseases to achieve the purpose of reducing the incidence of chronic diseases and delaying the development of diseases; secondly, through the analysis of drug use of patients with chronic diseases in the region, carry out the optimal allocation of drug resources in the region.

Second, the platform for the prevention and control of infectious diseases. We will build a big data platform for infectious disease prevention and control, which is based on centers for disease control and prevention and hospitals, and integrates active monitoring, early warning and emergency management of infectious diseases, so as to improve the emergency response capacity of major public health events.

This is also the common voice of many "two" representatives after COVID-19 's epidemic.

Of course, the third purpose of the medical big data platform is to integrate the medical information in the region, take the medical consortium as the carrier, build an Internet hospital service platform, and provide Internet-based medical services such as online consultation, so as to make up for the level differences among medical institutions in the Medical Federation, and improve the capacity of regional medical and health services and the efficiency of resource utilization.

An industrial chain with a clear division of labor

From the perspective of process, medical big data includes many links, such as data output, data collection, data storage, data processing, data analysis and data application, and there is a corresponding supply and demand relationship in each link.

Around this project, there is also an industrial chain with a clear division of labor.

The bottom layer is the chip, server and other manufacturers, which is the "foundation" of the whole industry chain.

Since 2012, Intel has been laying out in the direction of Medical big data.

At that time, Intel provided the Shanghai Health Bureau (now the Shanghai Health and Family Planning Commission) with the Apache Hadoop software Intel distribution and Intel Xeon solutions developed for big data's storage, management, processing and query needs.

The third Hospital of Beijing Medical College also implements distributed data storage based on Hadoop distributed big data technology, and constructs multiple distributed data computing nodes for complex clinical data analysis.

In addition, as an infrastructure provider, Intel entered into a partnership with Jiahe Meikang, an application software developer in the medical industry, in 2013.

At that time, Fan Kefang, deputy general manager of Jiahe Meikang Information Technology Co., Ltd., once said: "We have adopted a database based on big data technology in the Jiahe CDR program. Because Intel's Hadoop database has completed a comprehensive optimization from chip hardware to database management, it has reduced our workload and overall cost in the process of application software production."

Most of the middle-level manufacturers are providers of storage computing services and medical information solutions, and do data collection for the bottom of the entire industry.

For example, Wanliyun, the main cloud medical image big data cloud platform, relies on the platform advantages of Ali Health, Wandong Medical, Yuyue Group and Mainian Health to build the medical image big data cloud platform, provide remote medical imaging services and image cloud technology services, build an offline third-party medical imaging center, and provide technology development, promotion and consulting services related to the service and operation.

At the same time, this layer is also dominated by information manufacturers.

These companies, which were originally based on medical IT solutions, have established a very good degree of trust with hospitals over the years, have a deep understanding of the medical industry, and have the ability to process medical data.

On the basis of the original, medical information companies began to take different development routes according to the different needs of the hospital.

It is worth mentioning that the construction of China's intelligent medical cloud platform is mainly supported by three major databases: population information database, electronic medical record database and electronic health file database.

Taking the electronic medical record database as an example, the electronic medical record system is developed, which integrates HIS, LIS, PACS, RIS, pathology, ultrasound, endoscopy, rational drug use and other systems to realize the sharing of hospital business data and eliminate the isolated island of information in the hospital.

Neusoft is also an established manufacturer in the medical information industry.

In terms of in-hospital platform, Neusoft launched the hospital core business platform RealOne Suite in 2017, the main purpose of which is to realize the interconnection between inside and outside the hospital and the data sharing among multiple systems, so as to better support the efficient operation and fine management of many business processes in the hospital. In terms of regional platform, Neusoft built an interconnection platform with health archives as the core in the region, and successively helped Liaoning, Qinghai and other nine provinces to establish provincial health information platforms.

At the top are the start-up enterprises with core technologies such as deep learning, natural semantic analysis and vertical scene exploration. In recent years, medical AI companies have emerged to provide services around "data processing services".

Lei Feng learned that on February 5th, the "Intelligent Evaluation system of New Coronavirus pneumonia", which was developed by Medical AI Company and directed by the Municipal Public Health Center, was launched in the Shanghai Public Health Clinical Center. Artificial intelligence and other technologies were used to quantitatively analyze, evaluate the curative effect and predict the prognosis of the new coronavirus lesions in lung CT images.

Previously, Yitu Medical has launched an AI cancer prevention map, which not only provides AI auxiliary diagnosis system, but also helps the public health system to establish a screening database, and calculates the prevalence rate of various types of tumors in the urban area through the accumulation of medical data, which is more consistent with the concept of "treatment without disease" that has been advocated all the time.

Founded in 2013, Jiedu Yun carries out more basic work: helping hospitals to store, process and analyze medical data across systems and businesses, structural processing of unstructured data, and the use of artificial intelligence and machine learning to help hospitals and doctors improve clinical service quality, scientific research conversion rate and management efficiency.

During the epidemic, the Yidu Yun team immediately assisted the Ningbo Health Commission to establish the big data platform for monitoring the epidemic situation of COVID-19, which dynamically displayed the latest information such as the changes of confirmed cases, different types of cumulative cases, the prediction of the number of confirmed cases, the comparative analysis of the development trend of the epidemic, the RT index, the changing trend of RT, and the cure rate.

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