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Blockchain-Based Electronic Medical Records Management of Hospital Emergency Ward

机译:基于区块的电子医疗记录医院应急病房管理

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The handling of electronic medical records in the emergency ward is very crucial because it is the basis of patients’ medical treatment information before transferring to other ward in hospitals. Although, the current Electronic Medical Records (EMR) system has provided several advantages in handling patients’ relevant information such as quick searching and eliminating duplicated records. However, the system is still facing the challenges of security concern particularly data manipulation issue that is considered a serious threat to data integrity. In fact, the application of blockchain technology overcomes data security problem by validating this issue based on smart contract supported with hash method of peer to peer network. This method has allowed the safe sharing of information across nodes and secures the transaction over networks through the accuracy of data transmission. Therefore, this paper aims to perform data transmission analysis of a smart contract-based hash validation method through peer to peer network (P2P). The analysis result shows that the data transmission is accurate and non-error in every node on P2P. When the number of peers and data size is increased, it has a greater effect on the processing latency. Each peer can act as both a server and a client. With 8 peers, maximum data size 2048 bytes, latency was 3,249 milliseconds which shows the latency does not increase more than the smaller data size. After that, we brought the questionnaire relating to our system to ask 34 people, the results show that 50% of them aware of the importance of medical data and 100% of data integrity.
机译:在紧急病区中的电子医疗记录的处理是至关重要的,因为它是在转移到医院其他病房之前的患者医疗信息的基础。虽然,目前的电子医疗记录(EMR)系统在处理患者的相关信息方面提供了几个优势,例如快速搜索和消除重复记录。然而,该系统仍面临安全问题的挑战特别是数据操纵问题,这些问题被认为是对数据完整性的严重威胁。事实上,基于对等网络的哈希方法支持的智能合同验证了智能合同来验证数据安全问题,克服了数据安全问题。该方法允许在节点上安全分享信息,并通过数据传输的准确性来通过网络来保护交易。因此,本文旨在通过对等网络(P2P)对基于智能合同的哈希验证方法进行数据传输分析。分析结果表明,数据传输在P2P上的每个节点中是准确的和不误差。当对等体和数据大小的数量增加时,它对处理延迟产生了更大的影响。每个对等体都可以充当服务器和客户端。对于8个对等体,最大数据大小2048字节,延迟为3,249毫秒,显示延迟不会增加较小的数据大小。之后,我们带来了与我们的系统有关的调查问卷,要求34人,结果表明,50%的人意识到医疗数据的重要性和100%的数据完整性。

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