{"title":"Research agenda for using artificial intelligence in health governance: interpretive scoping review and framework.","authors":"Maryam Ramezani, Amirhossein Takian, Ahad Bakhtiari, Hamid R Rabiee, Sadegh Ghazanfari, Saharnaz Sazgarnejad","doi":"10.1186/s13040-023-00346-w","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence.</p><p><strong>Method: </strong>This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases.</p><p><strong>Results: </strong>Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area.</p><p><strong>Conclusion: </strong>AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.</p>","PeriodicalId":48947,"journal":{"name":"Biodata Mining","volume":"16 1","pages":"31"},"PeriodicalIF":4.0000,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617108/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biodata Mining","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1186/s13040-023-00346-w","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background: The governance of health systems is complex in nature due to several intertwined and multi-dimensional factors contributing to it. Recent challenges of health systems reflect the need for innovative approaches that can minimize adverse consequences of policies. Hence, there is compelling evidence of a distinct outlook on the health ecosystem using artificial intelligence (AI). Therefore, this study aimed to investigate the roles of AI and its applications in health system governance through an interpretive scoping review of current evidence.
Method: This study intended to offer a research agenda and framework for the applications of AI in health systems governance. To include shreds of evidence with a greater focus on the application of AI in health governance from different perspectives, we searched the published literature from 2000 to 2023 through PubMed, Scopus, and Web of Science Databases.
Results: Our findings showed that integrating AI capabilities into health systems governance has the potential to influence three cardinal dimensions of health. These include social determinants of health, elements of governance, and health system tasks and goals. AI paves the way for strengthening the health system's governance through various aspects, i.e., intelligence innovations, flexible boundaries, multidimensional analysis, new insights, and cognition modifications to the health ecosystem area.
Conclusion: AI is expected to be seen as a tool with new applications and capabilities, with the potential to change each component of governance in the health ecosystem, which can eventually help achieve health-related goals.
背景：卫生系统的治理本质上是复杂的，这是由几个相互交织的多维度因素造成的。卫生系统最近面临的挑战反映出需要创新的方法，以最大限度地减少政策的不利后果。因此，有令人信服的证据表明，使用人工智能对健康生态系统有着独特的看法。因此，本研究旨在通过对现有证据的解释性范围审查，调查人工智能及其在卫生系统治理中的作用。方法：本研究旨在为人工智能在卫生系统治理中的应用提供一个研究议程和框架。为了从不同角度纳入更多关于人工智能在卫生治理中应用的证据，我们通过PubMed、Scopus和Web of Science数据库搜索了2000年至2023年发表的文献。结果：我们的研究结果表明，将人工智能能力融入卫生系统治理有可能影响健康的三个基本维度。其中包括健康的社会决定因素、治理要素以及卫生系统的任务和目标。人工智能通过各个方面为加强卫生系统的治理铺平了道路，即智能创新、灵活的边界、多维分析、新的见解和对卫生生态系统领域的认知修改。结论：人工智能有望被视为一种具有新应用和能力的工具，有可能改变健康生态系统中治理的每个组成部分，最终有助于实现与健康相关的目标。
期刊介绍：
BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data.
Topical areas include, but are not limited to:
-Development, evaluation, and application of novel data mining and machine learning algorithms.
-Adaptation, evaluation, and application of traditional data mining and machine learning algorithms.
-Open-source software for the application of data mining and machine learning algorithms.
-Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies.
-Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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