从错误到卓越:分析前的旅程,以提高诊断质量. 一个范围审查审查
George K John1, Emmanuel J Favaloro1,2, Samantha Austin3
1School of Dentistry and Medical Science, Faculty of Science and Health, 110481 Charles Sturt University , Wagga Wagga, NSW, Australia.
Clinical chemistry and laboratory medicine
|January 27, 2025
概括
医疗实验室的预分析错误 (PAE) 影响患者的护理和成本. 这篇评论强调了人工智能和机器学习.
科学领域:
- 临床实验室科学 临床实验室科学
- 医学诊断 医学诊断 医学诊断
- 改善医疗保健质量 改善医疗保健质量
背景情况:
- 预分析错误 (PAE) 显著影响患者护理,医疗保健成本和运营效率.
- 在样本采集,运输和处理过程中发生PAE,影响诊断准确度.
- 目前对PAE的标准化定义,测量单位和教育策略是不够的.
研究的目的:
- 审查医学实验室中预分析错误 (PAE) 的演变.
- 突出卫生保健专业人员在PAE预防中的作用.
- 探索人工智能 (AI) 和机器学习在减少PAE方面的进步和应用.
主要方法:
- 采用了一个范围审查方法.
- 使用多个数据库进行文献搜索,结果导入Covidence.
- 用于总结研究选择和纳入的PRISMA流程图 (n=83).
主要成果:
- 医疗保健专业人员在样本处理期间预防PAE方面发挥着至关重要的作用.
- 人工智能和机器学习在减少PAE方面表现有希望,但需要进一步验证.
- 对于PAE的标准定义,测量单位和教育策略存在不足.
结论:
- 对人工智能模型的验证和风险概率指数 (RPI) 模型的监管批准需要进一步的研究.
- 缺乏关于人工智能/软件平台的实际有效性和实施的全面研究.
- 通过改进的策略和技术来解决PAE对于推进患者护理至关重要.
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