文章摘要
武小赟,王金华,曾昕,管海燕,刘映倩.基于大数据技术的药品生产流通全环节实时动态智慧监管启示[J].中国药事,2019,33(12):1377-1382
基于大数据技术的药品生产流通全环节实时动态智慧监管启示
Inspiration on Real-time Dynamic Intelligence Regulation of All Aspects of Drug Production and Circulation Based on Big Data Technology
投稿时间:2019-06-04  
DOI:10.16153/j.1002-7777.2019.12.007
中文关键词: 大数据技术  药品安全  全环节  预警平台  药品监管  智慧监管
英文关键词: big data technology  drug safety  the whole link  early warning platform  drug regulation  intelligence rehulation
基金项目:
作者单位
武小赟 重庆市药品技术审评认证中心, 重庆 401120 
王金华 三台县乐安镇中心卫生院, 三台 621000 
曾昕 重庆市药品技术审评认证中心, 重庆 401120 
管海燕 重庆市药品技术审评认证中心, 重庆 401120 
刘映倩 重庆市药品技术审评认证中心, 重庆 401120 
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中文摘要:
      目的:提高监管的实时性和有效性,杜绝重大药品安全事故的发生,达到提升整个药品行业的质量水平和技术水平的目标。方法:通过分析药品现行监管模式存在的问题与不足,探讨应用大数据、云计算及人工智能等技术手段,建立起在药品生产流通各环节实现数据的实时采集、追溯、研判和监管响应的"互联网+药品生产大数据综合分析预警平台"。结果:针对现行监管模式下可能存在的数据真实性问题及数据获取的滞后性问题,通过对生产流通数据的实时采集和溯源,保证监管数据的真实性和实时性;依托人工智能技术建立起对异常监管数据的高效研判和及时响应机制,实现对药品生产流通的实时动态智慧监管。结论:科学技术高速发展必将推动药品监管技术手段、策略乃至监管模式的巨大变革。积极探索大数据及人工智能等新技术在药品监管领域的应用,对于解决监管领域存在的诸多问题是非常有意义的尝试,同时也为未来监管技术模式提供了重要的参考。
英文摘要:
      Objective: To improve the timeliness and effectiveness of regulation, put an end to the occurrence of major drug safety accidents, and finally achieve the goal of improving the quality level and technical level of the whole drug industry. Methods: By analyzing the problems and shortcomings of the current drug regulatory mode, this paper discusses the application of big data, cloud computing, artificial intelligence and other technical means to establish the "comprehensive analysis and early warning platform for big data of Internet plus drug production" to realize real-time data collection, tracing, research, judgment and regulatory response in all links of drug production and circulation. Results: In view of the possible problems of data authenticity and hysteresis of data acquisition under the current regulation mode, the authenticity and timeliness of the regulatory data can be guaranteed by the real-time collection and tracing of data of production and circulation. A real-time and dynamic intelligent regulation of drug production and circulation could be established based on an efficient judgment and timely response mechanism for abnormal regulatory data by artificial intelligence technology. Conclusion: The rapid development of science and technology will certainly promote the great reform of technical means, strategies and even modes of drug regulation. Actively exploring the application of new technology such as big data and artificial intelligence in the field of drug regulation is a very meaningful attempt to solve many problems, and also provides an important references for the future regulatory technology mode.
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