USING MACHINE LEARNING OR DEEP LEARNING MODELS IN A HOSPITAL SETTING TO DETECT INAPPROPRIATE PRESCRIPTIONS: A SYSTEMATIC REVIEW

Abstract

ABSTRACTObjectivesThe emergence of artificial intelligence (AI) is catching the interest of hospitals pharmacists. Massive collection of pharmaceutical data is now available to train AI models and hold the promise of disrupting codes and practices. The objective of this systematic review was to examine the state of the art of machine learning or deep learning models that detect inappropriate hospital medication orders.MethodsA systematic review was conducted according to the PRISMA statement. PubMed and Cochrane database were searched from inception to May 2023. Studies were included if they reported and described an AI model intended for use by clinical pharmacists in hospitals.ResultsAfter reviewing, thirteen articles were selected. Eleven studies were published between 2020 and 2023; eight were conducted in North America and Asia. Six analyzed orders and detected inappropriate prescriptions according to patient profiles and medication orders, seven detected specific inappropriate prescriptions. Various AI models were used, mainly supervised learning techniques.ConclusionsThis systematic review points out that, to date, few original research studies report AI tools based on machine or deep learning in the field of hospital clinical pharmacy. However, these original articles, while preliminary, highlighted the potential value of integrating AI into clinical hospital pharmacy practice.What is already known on this topicAI models for pharmacists are at their beginning. Pharmacists need to stay up-to-date and show interest in developing such tools.What this study addsThis systematic review confirms the growing interest of AI in hospital setting. It highlights the challenges faced, and suggests that AI models have a great potential and will help hospital clinical pharmacists in the near future to better manage review of medication orders.How this study might affect research, practice or policyAI models have a gaining interested among hospital clinical pharmacists. This systematic review contributes to understand AI models and the techniques behind the tools.