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

Abstract

Objectives: The 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. Methods: A 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. Results: After 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. Conclusions: This 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.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Data Availability

All data produced in the present work are contained in the manuscript.

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