文章摘要
李澍,王浩,任海萍.临床决策支持系统技术现状及质量评价思路初探[J].中国药事,2019,33(9):1015-1021
临床决策支持系统技术现状及质量评价思路初探
Preliminary Study on Current Technique Status and Quality Evaluation of Clinical Decision Support System
投稿时间:2019-06-24  
DOI:10.16153/j.1002-7777.2019.09.009
中文关键词: 临床决策支持系统  人工智能  算法  伦理  可移植性
英文关键词: clinical decision support system  artificial intelligence  algorithms  ethics  portability
基金项目:国家重点研发计划(编号2017YFC0111203)
作者单位E-mail
李澍 中国食品药品检定研究院, 北京 102629  
王浩 中国食品药品检定研究院, 北京 102629  
任海萍 中国食品药品检定研究院, 北京 102629 renhaiping@nifdc.org.cn 
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中文摘要:
      目的:我国监管部门对临床决策支持系统(CDSS)的法律责任和监管模式正在探索之中,如何更好地规范临床决策支持系统,使其发挥临床辅助作用,是监管科学的重要研究方向。本文主要探索临床决策支持系统的质量评价方法和检测手段。方法:通过调研临床决策支持系统的发展现状以及分析现有的具有一定影响力的临床决策支持系统的技术特性,在此基础上,讨论临床决策支持系统在医疗领域的发展趋势和所面临的挑战。并对临床决策支持系统在质量评价过程中需重点关注的内容进行比较全面的总结,包括分类方法、评价方法和伦理问题。结果与结论:临床决策支持系统正处于蓬勃发展期,相应的技术和业态也在不断形成中,目前来说,绝大多数临床决策支持系统并不能直接给出相应的决策,多数还是处于启发和帮助医护人员进行思考的阶段。对于监管和评价来说,面临的挑战主要来自于算法适应性、决策透明性、界面可用性、系统鲁棒性和可移植性等因素的评价。
英文摘要:
      Objective:Regulatory authorities are exploring the legal responsibility and supervision model of the Clinical Decision Support System (CDSS) in China. How to better standardize the CDSS and play its clinical assistant role is an important research direction of regulatory science. This paper mainly discusses the quality evaluation methods and detection methods of CDSS. Methods:Development trend and challenges of CDSS in the medical field were discussed based on investigation of the development status of CDSS and the technical characteristics of existing influential clinical decision support system were analyzed. Moreover, the key points of CDSS in the process of quality evaluation, including classification methods, evaluation methods and ethical issues were summarized. Results and Conclusion:The CDSS is developing vigorously, and the corresponding technologies and formats are developing constantly as well. At present, most of the CDSS which are still in the stage of inspiring and helping medical staff to think can not directly make decisions. As far as monitoring and evaluation are concerned, the challenges mainly come from the evaluation of algorithm adaptability, decision transparency, interface availability, system robustness and portability.
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