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Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review
Florence Ranchon1, Sébastien Chanoine2, Sophie Lambert-Lacroix3
1CNRS, TIMC UMR5525, MESP, Université Grenoble Alpes, F-38041 Grenoble, France; Hospices Civils de Lyon, Hôpital Lyon Sud, unité de pharmacie clinique oncologique, Pierre-Bénite, France; Université Lyon-1, EA 3738 CICLY, Oullins cedex F-69921, France.
Objective:
Artificial Intelligence (AI) offers potential opportunities to optimize clinical pharmacy services in community or hospital settings. The objective of this systematic literature review was to identify and analyse quantitative studies using or integrating AI for clinical pharmacy services.
Materials And Methods:
A systematic review was conducted using PubMed/Medline and Web of Science databases, including all articles published from 2000 to December 2021. Included studies had to involve pharmacists in the development or use of AI-powered apps and tools..
Results:
19 studies using AI for clinical pharmacy services were included in this review. 12 out of 19 articles (63.1%) were published in 2020 or 2021. Various methodologies of AI were used, mainly machine learning techniques and subsets (natural language processing and deep learning). The datasets used to train the models were mainly extracted from electronic medical records (6 studies, 32%). Among clinical pharmacy services, medication order review was the service most targeted by AI-powered apps and tools (9 studies), followed by health product dispensing (4 studies), pharmaceutical interviews and therapeutic education (2 studies). The development of these tools mainly involved hospital pharmacists (12/19 studies).
Discussion And Conclusion:
The development of AI-powered apps and tools for clinical pharmacy services is just beginning. Pharmacists need to keep abreast of these developments in order to position themselves optimally while maintaining their human relationships with healthcare teams and patients. Significant efforts have to be made, in collaboration with data scientists, to better assess whether AI-powered apps and tools bring value to clinical pharmacy services in real practice.
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