How to build high quality, standardized medical big data through openEHR standards?

"The development of health medical big data application, the standard is the premise, security is the guarantee, service is the purpose."

In recent years, with the continuous advancement of the “Healthy China 2030” plan, China's medical information industry has developed rapidly, accumulating a large variety of valuable medical and health data resources. The importance of health care big data as a national basic strategic resource has gradually emerged. In the future, China's health care big data will usher in explosive growth, so data standardization will become crucial.

To discuss how to build high-quality, standardized medical big data through the open international standard openEHR, build medical big data application services, and release big data application dividends with information standardization. Recently, China Medical Device Industry Association Medical Software Branch, Smart and Mobile Medical The club successfully held the "openEHR standard and medical big data" special theme forum at the 6th China Smart Health Medical Development Summit Forum in Shenzhen.

如何通过openEHR标准建设高质量、标准化医疗大数据?

The forum brought together more than 300 experts in medical information standardization construction from all over the country, as well as entrepreneurs and investors in the medical information industry. They discussed the theme of “openEHR standard and medical big data” from different levels and perspectives. Sharing the current hot topics in the field of medical information standardization.

So what is openEHR? Professor Lu Xudong from Zhejiang University, as the host and opening guest of this forum, gave a detailed introduction to the specific content of openEHR and its application development in China, including the openEHR standard technical committee, modeling work in various sub-fields, and case studies. Introduction and so on.

如何通过openEHR标准建设高质量、标准化医疗大数据?

Professor Lu Xudong, Zhejiang University

The openEHR specification is an open electronic health record specification developed by the openEHR organization. The core of the openEHR specification is to separate medical knowledge from specific clinical information and establish a two-layer model—a reference model and a prototype model. Through the two-layer modeling method, openEHR enables domain experts to participate in the design and implementation of information systems by defining domain models, thereby achieving the goal of “putting domain experts to the driver's position” and effectively reducing the information system. The degree of dependence on domain knowledge enables the system based on openEHR to adapt to changes in domain knowledge, and is reusable, scalable, and easy to maintain.

openEHR has been widely used in countries such as Europe, Australia and Japan, and was accepted by the International Standards Organization in 2008 and developed into the ISO13606-2 standard. To date, the National Electronic Health Archives Data Center has been adopted in many European countries, and the National Electronic Health Archives Data Center project launched in 2015 is also planned to adopt this standard.

Although openEHR has been widely researched and applied worldwide as an international standard, and has accumulated a certain number of prototypes and templates, these prototypes and templates cannot be directly applied to clinical application in China, and urgently need to be based on clinical needs in China. Extend and localize.

Professor Lu Xudong said that the platform for the development of China's local open multi-agency collaborative participation standards has been put into use. At present, more and more hospital information technology related manufacturers and some institutions are beginning to understand and adopt this standard.

如何通过openEHR标准建设高质量、标准化医疗大数据?

Researcher of the Military Medical Research Institute Zhao Dongsheng (left), general manager of ZTE's medical product line Zhang Siwei (right)

Zhao Dongsheng, a researcher at the Military Medical Research Institute, and Zhang Siwei, general manager of the medical product line of Zhongxing Netcom Technology Co., Ltd., and many other experts and experts have made wonderful keynote speeches around openEHR and medical big data applications.

General Manager Zhang Sijun analyzed the use requirements of the openEHR-based population health information platform from three levels: medical and health institutions, health management departments, and resident public service platforms, and introduced the role of openEHR in the system. The core database based on openEHR includes population information database, electronic medical record information, health records, and BI data warehouse. It has built rich application services such as basic public health, two-way referral, and health supervision on the core database. At present, the health information platform has been put into formal use in many medical and health institutions at home and abroad.

Wang Ping, general manager of Infinity Software (Shanghai) Co., Ltd. pointed out that current big data will change the traditional medical model and bring about tremendous changes in the medical field. At the same time, high-quality medical health data is difficult to obtain, and it has become the biggest bottleneck in the development of medical and health care. He said that there are two ways to make clinical medical research have efficient data support: one is to assist medical application scenarios through artificial intelligence, such as medical imaging, disease risk prediction, and new drug development. At present, artificial intelligence-assisted diagnostic products have used artificial intelligence to make breakthroughs in image lesion identification and labeling; the second is to build clinical data and support systems based on openEHR. By constructing a specialist clinical model and establishing a specialist data center, through data engine processing, it can quickly and efficiently construct upper-layer application development such as diagnosis, research, and management. At present, Infinity Software is setting up a research database for oral clinical research based on openEHR and a clinical data database for liver transplantation.

如何通过openEHR标准建设高质量、标准化医疗大数据?

Liu Baogang (left), co-founder and CEO of Yixian Technology, and Wang Ping, general manager of Infinity Software (right)

Liu Baogang, co-founder and CEO of Yixian Technology (Beijing) Co., Ltd. analyzed the pain points in the current enterprise software business chain. For example, for medical software companies, the R&D cycle is long and the R&D costs are high; for hospital users Software integration, lack of flexibility, and ease of use. Based on this, Yixian Technology has developed a high-productivity application development and integration platform based on openEHR to effectively solve the above problems by utilizing the flexible advantages of openEHR two-layer modeling. The platform can greatly reduce the overall IT cost of the health care business-related enterprises, improve business resilience, and meet international and domestic standards, in line with medico-legal requirements.

如何通过openEHR标准建设高质量、标准化医疗大数据?

Fu Ren Medical Technology Director Fu Bin

Fu Bin, Technical Director of Hangzhou Juren Medical Technology Co., Ltd., from the National Health and Wellness Committee on the evaluation of the functional application level of electronic medical record system, pointed out that the new evaluation dimension of 2018 annual rating: data quality score and requirements; Lack of technical systems for the effective use of medical data.

His proposed solution is based on openEHR's medical data integration and integration platform, which gives the right to access data to those who really need data, and uses the standardized information model of openEHR double-layer modeling to solve the complexity and dynamics of medical data. Solve the diversity of data applications and people through openEHR-based full data multi-channel access. High-quality medical data is formed through medical data input control, index automatic matching, automatic terminology conversion, natural language information extraction, medical image annotation, and medical image feature extraction.

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