Artificial intelligence in acute care: A systematic review, conceptual synthesis, and research agenda

• Published in: Technological forecasting & social change Vol. 206; p. 123568
• Main Authors: Meyer, Lea Mareen, Stead, Susan, Salge, Torsten Oliver, Antons, David
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-09-2024
• Subjects: Artificial intelligence; Healthcare; Patient journey; Service-dominant logic; Systematic literature review; Healthcare; Patient journey; Artificial intelligence; Service-dominant logic; Systematic literature review
• ISSN: 0040-1625
• DOI: 10.1016/j.techfore.2024.123568

Artificial intelligence (AI) is emerging as a promising healthcare technology. Especially in critical, data-driven, and complex environments such as acute care, the use of AI can significantly improve treatment processes and support clinical staff. To date, AI applications in healthcare are scarce. Research remains fragmented across individual applications. To address this gap, we conduct a systematic literature review. In this review, we map the status quo of AI research in acute care and use service-dominant logic (SDL) from service science as a framework to integrate our analysis. Using a multilayered lens, we (1) identify intended beneficiaries of AI, (2) identify relevant activities supported by AI, and (3) determine those steps of the patient journey that have been in the spotlight for AI research. Our findings suggest that researchers have focused on hospital staff members as intended beneficiaries during the first three steps of the patient journey: admission, diagnosis, and treatment. The patient's perspective, however, remains underexplored. Moreover, 96 % of the research papers we reviewed focus on AI development and proof-of-concept studies, while only 4 % employ and test AI applications in the field. We identify three priorities for future AI research in acute care and provide suitable research methods.
•First approach to combine systematic literature review with key determinants of SDL•SDL as an appropriate means to assess existent AI research in acute care•Four analysis determinants (micro level, meso level, activities, patient journey)•96 % of the research papers focus on AI development and proof-of-concept.•4 % of the research papers employ field study testing.