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Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review

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  • João Lopes
  • Mariana Faria
  • Manuel Filipe Santos

Abstract

Objective: In 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. Methods: A 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. Results: The 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. Conclusion: The 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.

Suggested Citation

  • João Lopes & Mariana Faria & Manuel Filipe Santos, 2024. "Exploring trends and autonomy levels of adaptive business intelligence in healthcare: A systematic review," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-23, May.
    • Handle: RePEc:plo:pone00:0302697
    DOI: 10.1371/journal.pone.0302697
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    References listed on IDEAS

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    1. M. M. Malik & S. Abdallah & M. Ala’raj, 2018. "Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review," Annals of Operations Research, Springer, vol. 270(1), pages 287-312, November.
    2. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
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