Outcomes of ABI components.

ObjectiveIn order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan’s Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context.MethodsA comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study.ResultsThe outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions.ConclusionThe study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field.研究目标：为全面解析医疗健康领域自适应商业智能（Adaptive Business Intelligence, ABI）的特性，本研究旨在深入剖析该领域内的共性特征与演化规律。本研究以谢里丹分类法（Sheridan’s Classification）为分析框架，评估各类ABI组件所展现的自主程度。上述研究目标将有助于深化对ABI在医疗健康场景下的落地应用及其影响的理解。 研究方法：本研究通过对学术数据库开展全面检索以筛选相关文献，选取的信息源包括AIS电子图书馆（AIS e-library, AISel）、《决策支持系统期刊》（Decision Support Systems Journal, DSSJ）、《自然》（Nature）、《柳叶刀·数字健康》（The Lancet Digital Health, TLDH）、PubMed、《专家系统及其应用》（Expert Systems with Application, ESWA）以及《npj数字医学》（npj Digital Medicine）。根据预设的纳入排除标准，最终纳入2006年至2022年间发表的相关研究。本研究遵循PRISMA声明（PRISMA statements）进行报告撰写。 研究结果：结果显示，ABI系统呈现出差异化的发展水平、自主程度与实际部署情况。高自主程度的ABI系统主要与预测类组件高度相关，但此类系统完全自主做出决策的可能性完全不存在。同时也观察到低自主程度的ABI系统，尤其是在与规范类组件结合的场景中，此类系统将决策生成的责任交由用户承担。 研究结论：本研究强调了预期目标与这类解决方案固有自主程度之间的关键关联，同时指出亟需针对ABI系统及其组成要素的影响开展更多研究。各组织机构应结合自身目标与价值理念部署此类系统，同时需警惕潜在的负面影响。本研究可为旨在理解ABI系统落地应用多样化水平的研究人员、从业者与政策制定者提供宝贵参考，助力该动态领域内的科学决策。