PDF(Pubmed)
Abstract：
BACKGROUND: Efforts are underway to capitalize on the computational power of the data collected in electronic medical records (EMRs) to achieve a learning health system (LHS). Artificial intelligence (AI) in health care has promised to improve clinical outcomes, and many researchers are developing AI algorithms on retrospective data sets. Integrating these algorithms with real-time EMR data is rare. There is a poor understanding of the current enablers and barriers to empower this shift from data set-based use to real-time implementation of AI in health systems. Exploring these factors holds promise for uncovering actionable insights toward the successful integration of AI into clinical workflows.
OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.
METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics.
RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework.
CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.
OBJECTIVE: The first objective was to conduct a systematic literature review to identify the evidence of enablers and barriers regarding the real-world implementation of AI in hospital settings. The second objective was to map the identified enablers and barriers to a 3-horizon framework to enable the successful digital health transformation of hospitals to achieve an LHS.
METHODS: The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were adhered to. PubMed, Scopus, Web of Science, and IEEE Xplore were searched for studies published between January 2010 and January 2022. Articles with case studies and guidelines on the implementation of AI analytics in hospital settings using EMR data were included. We excluded studies conducted in primary and community care settings. Quality assessment of the identified papers was conducted using the Mixed Methods Appraisal Tool and ADAPTE frameworks. We coded evidence from the included studies that related to enablers of and barriers to AI implementation. The findings were mapped to the 3-horizon framework to provide a road map for hospitals to integrate AI analytics.
RESULTS: Of the 1247 studies screened, 26 (2.09%) met the inclusion criteria. In total, 65% (17/26) of the studies implemented AI analytics for enhancing the care of hospitalized patients, whereas the remaining 35% (9/26) provided implementation guidelines. Of the final 26 papers, the quality of 21 (81%) was assessed as poor. A total of 28 enablers was identified; 8 (29%) were new in this study. A total of 18 barriers was identified; 5 (28%) were newly found. Most of these newly identified factors were related to information and technology. Actionable recommendations for the implementation of AI toward achieving an LHS were provided by mapping the findings to a 3-horizon framework.
CONCLUSIONS: Significant issues exist in implementing AI in health care. Shifting from validating data sets to working with live data is challenging. This review incorporated the identified enablers and barriers into a 3-horizon framework, offering actionable recommendations for implementing AI analytics to achieve an LHS. The findings of this study can assist hospitals in steering their strategic planning toward successful adoption of AI.
摘要：
背景：正在努力利用电子病历（EMR）中收集的数据的计算能力来实现学习卫生系统（LHS）。医疗保健中的人工智能(AI)承诺改善临床结果，许多研究人员正在针对回顾性数据集开发AI算法。很少将这些算法与实时EMR数据集成。人们对当前的推动者和障碍了解不足，无法使这种从基于数据集的使用转变为在卫生系统中实时实施AI。探索这些因素有望为将AI成功整合到临床工作流程中提供可行的见解。
目标：第一个目标是进行系统的文献综述，以确定在医院环境中实施AI的推动者和障碍的证据。第二个目标是将确定的推动者和障碍映射到3-horides框架，以使医院的成功数字健康转型实现LHS。
方法：遵循PRISMA（系统评价和荟萃分析的首选报告项目）指南。PubMed,Scopus,WebofScience,和IEEEXplore被搜索了2010年1月至2022年1月之间发表的研究。包括有关使用EMR数据在医院环境中实施AI分析的案例研究和指南的文章。我们排除了在初级和社区护理环境中进行的研究。使用混合方法评估工具和ADAPTE框架对已识别论文进行质量评估。我们对纳入的研究中的证据进行了编码，这些研究与人工智能实施的推动者和障碍有关。研究结果被映射到3视野框架，为医院整合AI分析提供路线图。
结果：在筛选的1247项研究中，26人(2.09%)符合纳入标准。总的来说,65%(17/26)的研究实施了人工智能分析，以加强对住院患者的护理，而其余35%(9/26)提供了实施指南。在最后的26篇论文中，21例(81%)的质量被评估为较差.总共确定了28个推动者；本研究中有8个（29％）是新的。总共确定了18个障碍；新发现了5个（28％）。这些新确定的因素大多数与信息和技术有关。通过将调查结果映射到3视野框架，提供了实施AI以实现LHS的可行建议。
结论：在医疗保健中实施人工智能存在重大问题。从验证数据集转向处理实时数据是一项挑战。本次审查将确定的推动者和障碍纳入一个3视野框架，为实施AI分析以实现LHS提供可操作的建议。这项研究的结果可以帮助医院引导他们的战略规划成功采用人工智能。
目标：第一个目标是进行系统的文献综述，以确定在医院环境中实施AI的推动者和障碍的证据。第二个目标是将确定的推动者和障碍映射到3-horides框架，以使医院的成功数字健康转型实现LHS。
方法：遵循PRISMA（系统评价和荟萃分析的首选报告项目）指南。PubMed,Scopus,WebofScience,和IEEEXplore被搜索了2010年1月至2022年1月之间发表的研究。包括有关使用EMR数据在医院环境中实施AI分析的案例研究和指南的文章。我们排除了在初级和社区护理环境中进行的研究。使用混合方法评估工具和ADAPTE框架对已识别论文进行质量评估。我们对纳入的研究中的证据进行了编码，这些研究与人工智能实施的推动者和障碍有关。研究结果被映射到3视野框架，为医院整合AI分析提供路线图。
结果：在筛选的1247项研究中，26人(2.09%)符合纳入标准。总的来说,65%(17/26)的研究实施了人工智能分析，以加强对住院患者的护理，而其余35%(9/26)提供了实施指南。在最后的26篇论文中，21例(81%)的质量被评估为较差.总共确定了28个推动者；本研究中有8个（29％）是新的。总共确定了18个障碍；新发现了5个（28％）。这些新确定的因素大多数与信息和技术有关。通过将调查结果映射到3视野框架，提供了实施AI以实现LHS的可行建议。
结论：在医疗保健中实施人工智能存在重大问题。从验证数据集转向处理实时数据是一项挑战。本次审查将确定的推动者和障碍纳入一个3视野框架，为实施AI分析以实现LHS提供可操作的建议。这项研究的结果可以帮助医院引导他们的战略规划成功采用人工智能。
