Automation eliminates need for laborious protocols
In an environment of shrinking budgets, reimbursement uncertainty, and rising demand for diagnostic testing, there is one safe bet for clinical labs: automation. Replacing labor-intensive protocols with automated ones at every stage of the diagnostic workflow is a tried-and-true approach to lowering costs, expanding capacity, improving the accuracy of results, and minimizing errors.
Many clinical labs have already adopted automated platforms to run molecular diagnostic assays, and some of these platforms do an excellent job of incorporating steps from sample preparation all the way to generating results. But most workflows—from simple laboratory-developed tests (LDTs) based on polymerase chain reaction (PCR) to more complex next-generation sequencing (NGS)-based assays—include a mix of automated and manual procedures. These hodgepodge pipelines are especially problematic because the use of any automation makes them seem more robust and reproducible than they actually are. Manual steps such as DNA extraction, library size selection, and even pipetting samples into plates are weak links in these pipelines. Automating these steps would ensure higher-quality results and keep test-to-test variability to a minimum.
We are rapidly entering an era in which diagnostics will be used for far more types of medical situations than they are now, giving physicians critical guidance in cases that for too long have relied on educated guesswork. As we prepare for this expansion of diagnostics, though, we must lock down lab workflows to make them as bullet-proof as possible. Automating the last of the manual processes will be an essential step in achieving this.
– Todd Barbera
Chief Executive Officer
AI in the microbiology lab amplifies human ingenuity
Labs across the world have adopted automation for upfront specimen processing of microbiology samples. The next frontier in full laboratory automation focuses on the software, and it includes artificial intelligence (AI) algorithms to automatically read and interpret growth on plates, count colonies, and recognize morphology.
The essential algorithms for artificial intelligence for microbiology can be grouped into four categories:
- Colony counting with growth/no growth discrimination: This quickly screens negative plates by colony count and segregates no growth or no significant growth plates from those with growth.
- Chromogenic detection: This automatically detects color of colonies on chromogenic media plates
- Phenotypic colony recognition: This examines colonies on non-chromogenic plates, comparing against a library of thousands of colony images to match the phenotypic
characteristics and assign predictive value
- User-defined expert rules algorithm: This filters reporting, using patient demographics to determine if growth is significant and relevant.
Two multi-center studies1,2 validated the automatic detection and segregation of positive MRSA and VRE samples using chromogenic agar. With sensitivity at 100 percent and specificity between 89.5 percent and 96 percent, both studies showed that unique artificial intelligence algorithms accurately segregated negative from non-negative plates.
Another study3 with more 5,000 samples looked at the use of algorithms on blood and MacConkey plates and showed agreement between the software and manual interpretation was 99.96 percent for positive, 92.6 percent for negative.
Notably, sensitivity can increase to 100 percent by using the user-defined expert rules algorithm, applying the patient demographic and location source to filter reporting.
The software could improve laboratory workflow by removing more than 40 percent of urine cultures that fall below the growth threshold set by the laboratory.
– Amanda Schmidt
Sr. Channel Marketing Manager
- Faron ML, Buchan BW, Coon C, et al. Automatic digital analysis of chromogenic media for vancomycin-resistant Enterococcus screens using Copan WASPLab. J Clin Microbiol. 2016;54(10):2464-2469.
- Faron ML, Buchan BW, Vismara C, et al. Automated scoring of chromogenic media for detection of methicillin-resistant Staphylococcus aureus by use of WASPLab image analysis software. J Clin Microbiol. 2016;54:620-624.
- Faron ML, Buchan BW, Relich RF, et al. Digital image analysis to interpret urine cultures on blood and MacConkey agar. Poster presented at the 2017 ASM Microbe. http://www.copanusa.com/education/scientific-studies/use-digital-image-analysis-interpret-urine-cultures-blood-and-macconkey-agar/.
Automation for everyone—well almost everyone…
The challenges faced by today’s laboratorians are many—decreasing budgets, higher testing volumes, and the constant need to measure, assess, and improve performance. However, in my conversations with lab managers, what keeps most up at night is the challenge in hiring, training, and retaining their staff. And the challenge is equally acute in both urban healthcare centers and small hospitals that serve a rural patient population.
Over the last 20 years, large high-volume laboratories have adopted track-based automation solutions with a range of pre- and post- analytical capability as a means of improving work flow, reducing errors, and dedicating valuable laboratorian time to value-added activities. In 2018, every laboratory needs to improve workflow, reduce errors, and manage staff efficiently and effectively, and that is driving the adoption of automation into even mid-sized hospital laboratories. As healthcare delivery networks—generally comprised of both big and small hospitals—evaluate their needs, it’s clear that the challenge of finding and training qualified staff is universal. As a result, automation as a solution is moving down the test volume continuum, as IVD manufacturers build flexibility and scalability into their automation offerings and forward-thinking healthcare delivery networks start real projects to future-proof their labs. Every laboratory director needs to find ways to do more with less, and laboratories both big and small are choosing automation as a solution.
– Jay Snyder
V.P. of Clinical Labs, Platforms
Ortho Clinical Diagnostics