John George K, Favaloro Emmanuel J, Austin Samantha, Islam Md Zahidul, Santhakumar Abishek B
School of Dentistry and Medical Science, Faculty of Science and Health, 110481 Charles Sturt University , Wagga Wagga, NSW, Australia.
Sydney Centres for Thrombosis and Haemostasis, Institute of Clinical Pathology and Medical Research (ICPMR), Westmead Hospital, Westmead, NSW, Australia.
Clin Chem Lab Med. 2025 Jan 28;63(7):1243-1259. doi: 10.1515/cclm-2024-1277. Print 2025 Jun 26.
This scoping review focuses on the evolution of pre-analytical errors (PAEs) in medical laboratories, a critical area with significant implications for patient care, healthcare costs, hospital length of stay, and operational efficiency. The Covidence Review tool was used to formulate the keywords, and then a comprehensive literature search was performed using several databases, importing the search results directly into Covidence (n=379). Title, abstract screening, duplicate removal, and full-text screening were done. The retrieved studies (n=232) were scanned for eligibility (n=228) and included in the review (n=83), and the results were summarised in a PRISMA flow chart. The review highlights the role of healthcare professionals in preventing PAEs in specimen collection and processing, as well as analyses. The review also discusses the use and advancements of artificial intelligence (AI) and machine learning in reducing PAEs and identifies inadequacies in standard definitions, measurement units, and education strategies. It demonstrates the need for further research to ensure model validation, address the regulatory validation of Risk Probability Indexation (RPI) models and consider regulatory, safety, and privacy concerns. The review suggests that comprehensive studies on the effectiveness of AI and software platforms in real-world settings and their implementation in healthcare are lacking, presenting opportunities for further research to advance patient care and improve the management of PAEs.
本范围综述聚焦于医学实验室分析前误差（PAEs）的演变，这是一个对患者护理、医疗成本、住院时间和运营效率具有重大影响的关键领域。使用Covidence综述工具制定关键词，然后使用多个数据库进行全面的文献检索，并将检索结果直接导入Covidence（n = 379）。进行了标题、摘要筛选、重复项去除和全文筛选。对检索到的研究（n = 232）进行资格审查（n = 228），并纳入综述（n = 83），结果在PRISMA流程图中进行了总结。该综述强调了医疗保健专业人员在预防标本采集、处理及分析过程中的分析前误差方面的作用。该综述还讨论了人工智能（AI）和机器学习在减少分析前误差方面的应用和进展，并指出了标准定义、测量单位和教育策略方面的不足。它表明需要进一步研究以确保模型验证，解决风险概率指数化（RPI）模型的监管验证问题，并考虑监管、安全和隐私问题。该综述表明，缺乏关于人工智能和软件平台在实际环境中的有效性及其在医疗保健中的实施情况的综合研究，这为进一步研究以推进患者护理和改善分析前误差管理提供了机会。
