Jessica Hatzimanolis, Britney Riley, Sarira El-Den, Parisa Aslani, Joe Zhou, Betty B Chaar
School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia.
School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Camperdown, Australia.
Res Social Adm Pharm. 2025 Mar;21(3):134-141. doi: 10.1016/j.sapharm.2024.12.007. Epub 2024 Dec 17.
Artificial intelligence (AI), a branch of computer science, has been of growing research interest since its introduction to healthcare disciplines in the 1970s. Research has demonstrated that the application of such technologies has allowed for greater task accuracy and efficiency in medical disciplines such as diagnostics, treatment protocols and clinical decision-making. Application in pharmacy practice is reportedly narrower in scope; with greater emphasis placed on stock management and day-to-day function optimisation than enhancing patient outcomes. Despite this, new studies are underway to explore how AI technologies may be utilised in areas such as pharmacist interventions, medication adherence, and personalised medicine. Objective/s: The aim of this study was to identify current use of AI in measuring performance outcomes in pharmacy practice.
A scoping review was conducted in accordance with PRISMA Extension for Scoping Reviews (PRISMA-ScR). A comprehensive literature search was conducted in MEDLINE, Embase, IPA (International Pharmaceutical Abstracts), and Web of Science databases for articles published between January 1, 2018 to September 11, 2023, relevant to the aim. The final search strategy included the following terms: ("artificial intelligence") AND ("pharmacy" OR "pharmacist" OR "pharmaceutical service" OR "pharmacy service"). Reference lists of identified review articles were also screened.
The literature search identified 560 studies, of which seven met the inclusion criteria. These studies described the use of AI in pharmacy practice. All seven studies utilised models derived from machine learning AI techniques. AI identification of prescriptions requiring pharmacist intervention was the most frequent (n = 4), followed by screening services (n = 2), and patient-facing mobile applications (n = 1). These results indicated a workflow- and productivity-focused application of AI within current pharmacy practice, with minimal intention for direct patient health outcome improvement. Despite this, the review also revealed AI's potential in data collation and analytics to aid in pharmacist contribution towards the healthcare team and improvement of health outcomes.
This scoping review has identified, from the literature available, three main areas of focus, (1) identification and classification of atypical or inappropriate medication orders, (2) improving efficiency of mass screening services, and (3) improving adherence and quality use of medicines. It also identified gaps in AI's current utility within the profession and its potential for day-to-day practice, as our understanding of general AI techniques continues to advance.
人工智能（AI）作为计算机科学的一个分支，自20世纪70年代引入医疗保健学科以来，一直受到越来越多的研究关注。研究表明，此类技术的应用在诊断、治疗方案和临床决策等医学领域提高了任务的准确性和效率。据报道，其在药学实践中的应用范围较窄；相较于改善患者结局，更侧重于库存管理和日常功能优化。尽管如此，新的研究正在探索如何在药剂师干预、药物依从性和个性化医疗等领域利用人工智能技术。目的：本研究的目的是确定人工智能在衡量药学实践绩效结果方面的当前应用情况。
根据《系统综述与Meta分析扩展版：范围综述》（PRISMA-ScR）进行范围综述。在MEDLINE、Embase、IPA（国际药学文摘）和科学网数据库中进行全面的文献检索，以查找2018年1月1日至2023年9月11日期间发表的与该目的相关的文章。最终的检索策略包括以下术语：（“人工智能”）与（“药学”或“药剂师”或“药学服务”或“药房服务”）。还对已识别的综述文章的参考文献列表进行了筛选。
文献检索共识别出560项研究，其中7项符合纳入标准。这些研究描述了人工智能在药学实践中的应用。所有7项研究都使用了源自机器学习人工智能技术的模型。人工智能识别需要药剂师干预的处方最为常见（n = 4），其次是筛查服务（n = 2）和面向患者的移动应用程序（n = 1）。这些结果表明，在当前的药学实践中，人工智能的应用主要集中在工作流程和生产力方面，对直接改善患者健康结局的意图最小。尽管如此，该综述还揭示了人工智能在数据整理和分析方面的潜力，有助于药剂师为医疗团队做出贡献并改善健康结局。
本范围综述从现有文献中确定了三个主要关注领域：（1）非典型或不适当用药医嘱的识别和分类；（2）提高大规模筛查服务的效率；（3）提高药物依从性和合理用药质量。随着我们对通用人工智能技术的理解不断进步，它还确定了人工智能在该专业当前应用中的差距及其在日常实践中的潜力。
