医疗保健行业各层级对大数据的认识趋于一致:新西兰案例

K. Weerasinghe, David Pauleen, Nazim Taskin, Shane Scahill
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引用次数: 0

摘要

大数据和相关技术有可能促进医疗保健规划和服务的改进,从而改变医疗保健行业。大数据研究凸显了大数据实施与业务需求相结合以取得成功的重要性。本文是首批研究大数据对医疗保健行业业务-IT 协调的影响的论文之一,本文探讨的问题是:在新西兰(NZ)医疗保健行业,利益相关者对大数据的认知如何影响大数据技术与医疗保健行业需求在宏观、中观和微观层面的协调?本文采用半结构式访谈进行定性调查,以了解新西兰医疗保健行业对大数据的看法。该研究采用了一种新理论--社会技术表征理论(TSR)--来研究人们对大数据技术的看法及其在日常工作中的适用性。这些表征在每个层面上进行分析,然后跨层面进行分析,以评估吻合程度。我们从社会维度的角度来探讨整个行业对大数据的相互理解。研究结果表明,通过对数据质量的重要性、日益严峻的隐私和安全挑战以及利用现代和新数据衡量健康成果的重要性的共同理解,整个部门对大数据的理解是一致的。不一致的方面包括对大数据的不同定义,以及对数据所有权、数据共享、使用患者生成的数据和互操作性的看法。本研究的实践和理论贡献均在讨论之列。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Alignment of Big Data Perceptions Across Levels in Healthcare: The case of New Zealand
Big data and related technologies have the potential to transform healthcare sectors by facilitating improvements to healthcare planning and delivery. Big data research highlights the importance of aligning big data implementations with business needs to achieve success. In one of the first studies to examine the influence of big data on business-IT alignment in the healthcare sector, this paper addresses the question: how do stakeholders’ perceptions of big data influence alignment between big data technologies and healthcare sector needs across macro, meso, and micro levels in the New Zealand (NZ) healthcare sector? A qualitative inquiry was conducted using semi-structured interviews to understand perceptions of big data across the NZ healthcare sector. An application of a novel theory, Theory of Sociotechnical Representations (TSR), is used to examine people’s perceptions of big data technologies and their applicability in their day-to-day work. These representations are analysed at each level and then across levels to evaluate the degree of alignment. A social dimension lens to alignment was used to explore mutual understanding of big data across the sector. The findings show alignment across the sector through the shared understanding of the importance of data quality, the increasing challenges of privacy and security, and the importance of utilising modern and new data in measuring health outcomes. Areas of misalignment include the differing definitions of big data, as well as perceptions around data ownership, data sharing, use of patient-generated data and interoperability. Both practical and theoretical contributions of the study are discussed.
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