Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups.

Weidmann, Anita Elaine; Watson, Edward William

Abstract

The advent of artificial intelligence (AI) technologies has taken the world of science by storm in 2023. The opportunities of this easy to access technology for clinical pharmacy research are yet to be fully understood. The development of a custom-made large language model (LLM) (DELSTAR) trained on a wide range of internationally recognised scientific publication databases, pharmacovigilance sites and international product characteristics to help identify and summarise medication related information on delirium, as a proof-of-concept model, identified new facilitators and barriers for robust clinical pharmacy practice research. This technology holds great promise for the development of much more comprehensive prescribing guidelines, practice support applications for clinical pharmacy, increased patient and prescribing safety and resultant implications for healthcare costs. The challenge will be to ensure its methodologically robust use and the detailed and transparent verification of its information accuracy.

Citation

WEIDMANN, A.E. and WATSON, E.W. 2024. Novel opportunities for clinical pharmacy research: development of a machine learning model to identify medication related causes of delirium in different patient groups. International journal of clinical pharmacy [online], Latest Articles. Available from: https://doi.org/10.1007/s11096-024-01707-z

• Journal Article Type: Article
• Acceptance Date: Jan 22, 2024
• Online Publication Date: Apr 9, 2024
• Deposit Date: May 23, 2024
• Publicly Available Date: May 23, 2024
• Journal: International journal of clinical pharmacy
• Print ISSN: 2210-7703
• Electronic ISSN: 2210-7711
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s11096-024-01707-z
• Keywords: Artificial intelligence; Clinical pharmacy information systems; Delirium; Drug prescribing; Machine intelligence; Patient safety
• Public URL: https://rgu-repository.worktribe.com/output/2299339