Ye Jiancheng, Woods Donna, Jordan Neil, Starren Justin
Feinberg School of Medicine, Northwestern University, Chicago, USA.
AMIA Jt Summits Transl Sci Proc. 2024 May 31;2024:459-467. eCollection 2024.
This narrative review aims to identify and understand the role of artificial intelligence in the application of integrated electronic health records (EHRs) and patient-generated health data (PGHD) in clinical decision support. We focused on integrated data that combined PGHD and EHR data, and we investigated the role of artificial intelligence (AI) in the application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search articles in six databases: PubMed, Embase, Web of Science, Scopus, ACM Digital Library, and IEEE Computer Society Digital Library. In addition, we also synthesized seminal sources, including other systematic reviews, reports, and white papers, to inform the context, history, and development of this field. Twenty-six publications met the review criteria after screening. The EHR-integrated PGHD introduces benefits to health care, including empowering patients and families to engage via shared decision-making, improving the patient-provider relationship, and reducing the time and cost of clinical visits. AI's roles include cleaning and management of heterogeneous datasets, assisting in identifying dynamic patterns to improve clinical care processes, and providing more sophisticated algorithms to better predict outcomes and propose precise recommendations based on the integrated data. Challenges mainly stem from the large volume of integrated data, data standards, data exchange and interoperability, security and privacy, interpretation, and meaningful use. The use of PGHD in health care is at a promising stage but needs further work for widespread adoption and seamless integration into health care systems. AI-driven, EHR-integrated PGHD systems can greatly improve clinicians' abilities to diagnose patients' health issues, classify risks at the patient level by drawing on the power of integrated data, and provide much-needed support to clinics and hospitals. With EHR-integrated PGHD, AI can help transform health care by improving diagnosis, treatment, and the delivery of clinical care, thus improving clinical decision support.
本叙述性综述旨在识别并理解人工智能在综合电子健康记录(EHR)和患者生成的健康数据(PGHD)应用于临床决策支持中的作用。我们聚焦于结合了PGHD和EHR数据的综合数据,并研究了人工智能(AI)在该应用中的作用。我们使用系统评价和Meta分析的首选报告项目(PRISMA)指南在六个数据库中检索文章:PubMed、Embase、科学网、Scopus、美国计算机协会数字图书馆和电气与电子工程师协会计算机学会数字图书馆。此外,我们还综合了一些重要资料来源,包括其他系统评价、报告和白皮书,以了解该领域的背景、历史和发展情况。经过筛选,有26篇出版物符合综述标准。整合了EHR的PGHD给医疗保健带来了诸多益处,包括通过共同决策使患者和家庭能够更多地参与、改善患者与医疗服务提供者的关系以及减少临床就诊的时间和成本。人工智能的作用包括清理和管理异构数据集、协助识别动态模式以改善临床护理流程,以及提供更复杂的算法以更好地预测结果并基于综合数据提出精确的建议。挑战主要源于综合数据量巨大、数据标准、数据交换与互操作性、安全与隐私、解读以及有意义的使用等方面。PGHD在医疗保健中的应用正处于一个充满希望的阶段,但要广泛采用并无缝集成到医疗保健系统中还需要进一步努力。人工智能驱动的、整合了EHR的PGHD系统能够极大地提高临床医生诊断患者健康问题的能力,借助综合数据的力量在患者层面进行风险分类,并为诊所和医院提供急需的支持。通过整合了EHR的PGHD,人工智能可以通过改善诊断、治疗和临床护理的提供来帮助变革医疗保健,从而改善临床决策支持。
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