Laboratory device advancements are having a significant impact on the quality of work performed in clinical chemistry laboratories around the world. Clinical labs can now obtain data more quickly, more accurately, and consistently based on the progression of new instruments. In some cases, new devices are delivering scientific data that was previously unavailable. For pathologists, these progressions are providing invaluable means of obtaining breakthroughs; And for clinicians, substantially more trustworthy laboratory data, which is crucial in the diagnosis and treatment of disease.
Conventional analytical labs and clinical chemistry labs use similar devices such as colourimeters, spectrophotometers, flame photometers, fluorometers, pH meters, gas chromatographs, radiation counters. However, due to the recent digital disruption in healthcare, a need for devices and methods specifically catered for the clinical laboratory has led to diversification.
In the next decades, an enormous change is predicted to evolve the practice of clinical microbiology due to the maturation of sequencing and digital imaging tools. “Both of these applications require Big Data solutions, such as machine learning applications. In the 1900s, our understanding of the world was only bound by how much data we could obtain. However, we need tools like data reduction strategies, or data enhancement tools to help identify vital information in the sea of data and machine learning can help us with that,” says Dr Daniel Rhodes, section head for microbiology at the Cleveland Clinic.
Automation in bioanalytical laboratories enhances sample throughput and data integrity, reduces method development time, and speeds up sample data turnaround time, according to industry consensus. The degree of automation is determined by the laboratory's needs and resources, and the reasons for using automation differ depending on the application. Staff manipulation of biological samples is reduced with automation, particularly in sample transport, subsampling, analytical processes, and waste management. Furthermore, the automatic storage space protects sample integrity and is adequately guarded against unwanted access.
AI’s impact on healthcare has been transformative, and within clinical microbiology, it is predicted to accelerate diagnostics, with integration in total lab automation and machine learning. “One of the key diagnostic issues we have in microbiology is the time consumption. Most investigations can take hours or days, however, there has been a revolution in microbial identification in the last 10 years due to lab automation,” explains Dr Dietrich Mack, Microbiologist at Bioscientia.
Lab automation was adopted by Bioscientia in 2015, overhauling their approach to microbiology. “There was something new and important, a bi-directional interface between the laboratory automation and our laboratory information system was created, installed and tested. Finally, we were live in November 2017 and then the microbiology department transferred on a rolling track bench by bench over a period of five weeks, and that worked very well," says Dr Dietrich.
Regarding laboratory-testing capacity and methodologies, laboratory professionals have reported that existing methods were revalidated, with a 36.5 per cent relaying that new instruments were required in the wake of COVID-19 testing, according to Initial Clinical Laboratory Response to COVID-19: A Survey of Medical Laboratory Professionals, published in oxford academic. A variety of testing options were developed, which require results sourced from laboratories within a short span of time, showcasing the need to deliver quality results rapidly.