Ledeboer Nathan A, Dallas Steven D
Department of Pathology, Medical College of Wisconsin, and Dynacare Laboratories, Milwaukee, Wisconsin, USA
Department of Clinical Laboratory Sciences, UT Health Science Center San Antonio, and Microbiology Laboratory, University Hospital, San Antonio, Texas, USA
J Clin Microbiol. 2014 Sep;52(9):3140-6. doi: 10.1128/JCM.00686-14. Epub 2014 Mar 19.
Automated chemistry laboratories dependent on robotic processes are the standard in both academic and large community hospital settings. Diagnostic microbiology manufacturers are betting that robotics will be used for specimen processing, plate reading, and organism identification in the near future. These systems are highly complex and have large footprints and hefty price tags. However, they are touted as being more efficient, rapid, and accurate than standard processes. Certain features, such as image collection, are highly innovative. Hospital administrators may be swayed to institute these new systems because of the promise of the need for fewer skilled workers, higher throughput, and greater efficiency. They also may be swayed by the fact that workers with the requisite clinical microbiology skills are becoming more difficult to find, and this technology should allow fewer skilled workers to handle larger numbers of cultures. In this Point-Counterpoint, Nate Ledeboer, Medical Director, Clinical Microbiology and Molecular Diagnostics, Dynacare Laboratories, and Froedtert Hospital, Milwaukee, WI, will explain why he believes that this approach will become widespread, while Steve Dallas of the University of Texas Health Science Center San Antonio explains why he thinks that this automation may not become widely used.
在学术和大型社区医院环境中,依赖机器人流程的自动化化学实验室已成为标准配置。诊断微生物学设备制造商押注,在不久的将来,机器人技术将用于样本处理、平板读数和微生物鉴定。这些系统高度复杂,占地面积大,价格昂贵。然而,它们被吹捧为比标准流程更高效、快速和准确。某些功能,如图像采集,具有高度创新性。医院管理人员可能会被引入这些新系统,因为有望减少对熟练工人的需求、提高通量和效率。他们也可能会受到这样一个事实的影响,即具备必要临床微生物学技能的工人越来越难找到,而这项技术应该允许更少的熟练工人处理更多数量的培养物。在这场正方与反方的辩论中,威斯康星州密尔沃基市迪纳凯尔实验室及弗罗伊德特医院临床微生物学与分子诊断医学主任内特·勒德博尔将解释他认为这种方法会广泛应用的原因,而圣安东尼奥德克萨斯大学健康科学中心的史蒂夫·达拉斯则解释他认为这种自动化可能不会被广泛使用的原因。