Abstract：
BACKGROUND: The term \"big data\" refers to the vast volume, variety, and velocity of data generated from various sources-e.g., sensors, social media, and online platforms. Big data adoption within healthcare poses an intriguing possibility for improving patients\' health, increasing operational efficiency, and enabling data-driven decision-making. Despite considerable interest in the adoption of big data in healthcare, empirical research assessing the factors impacting the adoption process is lacking. Therefore, this review aimed to investigate the literature using a systematic approach to explore the factors that affect big data adoption in healthcare.
METHODS: A systematic literature review was conducted. The methodical and thorough process of discovering, assessing, and synthesizing relevant studies provided a full review of the available data. Several databases were used for the information search. Most of the articles retrieved from the search came from popular medical research databases, such as Scopus, Taylor & Francis, ScienceDirect, Emerald Insights, PubMed, Springer, IEEE, MDPI, Google Scholar, ProQuest Central, ProQuest Public Health Database, and MEDLINE.
CONCLUSIONS: The results of the systematic literature review indicated that several theoretical frameworks (including the technology acceptance model; the technology, organization, and environment framework; the interactive communication technology adoption model; diffusion of innovation theory; dynamic capabilities theory; and the absorptive capability framework) can be used to analyze and understand technology acceptance in healthcare. It is vital to consider the safety of electronic health records during the use of big data. Furthermore, several elements were found to determine technological acceptance, including environmental, technological, organizational, political, and regulatory factors.
METHODS: A systematic literature review was conducted. The methodical and thorough process of discovering, assessing, and synthesizing relevant studies provided a full review of the available data. Several databases were used for the information search. Most of the articles retrieved from the search came from popular medical research databases, such as Scopus, Taylor & Francis, ScienceDirect, Emerald Insights, PubMed, Springer, IEEE, MDPI, Google Scholar, ProQuest Central, ProQuest Public Health Database, and MEDLINE.
CONCLUSIONS: The results of the systematic literature review indicated that several theoretical frameworks (including the technology acceptance model; the technology, organization, and environment framework; the interactive communication technology adoption model; diffusion of innovation theory; dynamic capabilities theory; and the absorptive capability framework) can be used to analyze and understand technology acceptance in healthcare. It is vital to consider the safety of electronic health records during the use of big data. Furthermore, several elements were found to determine technological acceptance, including environmental, technological, organizational, political, and regulatory factors.
摘要：
背景：术语“大数据”是指庞大的体积，品种，以及从各种来源产生的数据的速度-例如，传感器,社交媒体,和在线平台。医疗保健中的大数据采用为改善患者健康提供了一个有趣的可能性，提高运营效率，并实现数据驱动的决策。尽管人们对在医疗保健中采用大数据非常感兴趣，缺乏评估影响采用过程的因素的实证研究。因此,这篇综述旨在使用系统的方法对文献进行调查，以探索影响医疗保健中大数据采用的因素。
方法：进行了系统的文献综述。有条不紊和彻底的发现过程，评估,综合相关研究，对现有数据进行了全面审查。几个数据库用于信息搜索。从搜索中检索到的大多数文章都来自流行的医学研究数据库，比如Scopus,泰勒和弗朗西斯，ScienceDirect,翡翠见解，PubMed,Springer,IEEE,MDPI,谷歌学者，ProQuestCentral,ProQuest公共卫生数据库,和MEDLINE。
结论：系统文献综述的结果表明，几个理论框架(包括技术接受模型；技术，组织,和环境框架；交互式通信技术采用模型；创新理论的扩散；动态能力理论；和吸收能力框架）可用于分析和理解医疗保健中的技术接受。在使用大数据的过程中，考虑电子健康记录的安全性至关重要。此外，发现了几个因素来确定技术接受度，包括环境,技术,组织，政治,和监管因素。
方法：进行了系统的文献综述。有条不紊和彻底的发现过程，评估,综合相关研究，对现有数据进行了全面审查。几个数据库用于信息搜索。从搜索中检索到的大多数文章都来自流行的医学研究数据库，比如Scopus,泰勒和弗朗西斯，ScienceDirect,翡翠见解，PubMed,Springer,IEEE,MDPI,谷歌学者，ProQuestCentral,ProQuest公共卫生数据库,和MEDLINE。
结论：系统文献综述的结果表明，几个理论框架(包括技术接受模型；技术，组织,和环境框架；交互式通信技术采用模型；创新理论的扩散；动态能力理论；和吸收能力框架）可用于分析和理解医疗保健中的技术接受。在使用大数据的过程中，考虑电子健康记录的安全性至关重要。此外，发现了几个因素来确定技术接受度，包括环境,技术,组织，政治,和监管因素。
