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医疗大数据的价值、风险与法律规制——基于新型冠状病毒肺炎疫情治理的思考
引用本文:吴何奇. 医疗大数据的价值、风险与法律规制——基于新型冠状病毒肺炎疫情治理的思考[J]. 科技导报(北京), 1980, 38(23): 31-38. DOI: 10.3981/j.issn.1000-7857.2020.23.004
作者姓名:吴何奇
作者单位:上海财经大学法学院, 上海 200433
摘    要: 在新型冠状病毒肺炎疫情(简称“新冠肺炎”)的应对中,医疗大数据技术体现了多方面的价值:通过大数据实现了对医疗资源的有效调度、基于大数据的AI医疗、通过大数据预测疫情的暴发、疫情趋势的监测等。但在大数据技术成为医疗领域重要技术手段的同时,也产生了数据风险的新问题与新挑战,完善数据规制从而协调数据保护与数据使用之间的冲突成为制度设计的难点。梳理了当前个人医疗数据规制的现状,结合医疗实践分析数据规制存在的不足,通过对国外数据规制的借鉴,提出了完善中国数据规制的建构逻辑,即以社会整体利益为核心,在平衡利益关系的前提下进行制度设计,将数据滥用行为纳入数据规制,引入场景理念修正同意规则并推动数据去识别化标准的形成。

关 键 词:医疗大数据  疫情治理  数据风险  法律规制

Value,data risks,regulation of medical big data thinking: Based on epidemic management
WU Heqi. Value,data risks,regulation of medical big data thinking: Based on epidemic management[J]. Science & Technology Review, 1980, 38(23): 31-38. DOI: 10.3981/j.issn.1000-7857.2020.23.004
Authors:WU Heqi
Affiliation:School of Law, Shanghai University of Finance and Economics, Shanghai 200433, China
Abstract:As a national strategy, big data technology aims to benefit all people through the smart medical model. In response to the novel coronavirus pneumonia epidemic, medical big data technology embodies many values:effective scheduling of medical resources, AI medical treatment, prediction of outbreaks, monitoring of outbreaks, trends, etc. However, while big data technology becomes an important technical means in medical field, new problems and challenges in data risks also arise. It has become difficult for regulation design to coordinate conflicts between data protection and data use. This article sorts out the current status of personal medical data regulation, analyzes the shortcomings of data regulation in combination with medical practice, and proposes a construction logic of perfecting China's data regulation by drawing on foreign data regulation, that is, with the overall interests of society being the core, system design is carried out on the premise of balancing interest relationships and data abuse is included in data regulation. Scenario concepts are also introduced to modify consent rules and to promote the formation of data de-identification standards.
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