LAB MANAGEMENT :: AUTOMATION


Figure 1. Mayo Clinic, Central Clinical Laboratory.


test menu that includes therapeutic drug monitoring (TDM), en- docrine and hormone testing, vitamins, drugs of abuse, controlled substances, clinical toxicology, forensic toxicology, trace elements, and heavy metals. Mass spectrometry is a key methodology within Mayo Clinic and encompasses dozens of mass spectrometers coupled to high performance liquid chromatography and gas chromatography, along with Fourier-transform infrared spec- troscopy (FT-IR), and inductively coupled plasma (ICP) mass spectrometry. Of note, the test menu includes more than 2,000 laboratory-developed tests (LDTs). Automation presently is focused on pre-analytics—from the primary tube all the way to the 96-well plates, a standard specimen entry point for mass spec and many other analyzer platforms. Pre-analytical automation, as set up in our labs, is important not only in addressing labor shortage and increasing throughput, but also in increasing ac- curacy and precision with robotic liquid handling. The valuable staff time freed up by automation is one contributing factor in supporting menu expansion as well as increased test volume.


Getting the most out of automation


Bringing total lab automation online is just the beginning. Our ongoing efforts strive to optimize our processes in order to achieve even greater efficiency. The following are key areas we focus on to reap the greatest benefit possible from our automation solutions. Digital solutions: We rely heavily on data and analytics and use data to drive improvements. One example is rule-based logic for decisions such as whether a specimen needs to be further investigated, or a test result requires additional quality checks. This logic resides in cobas infinity, the customizable middleware solution we have used to create specific workflows based on our lab’s needs, which reliably triggers intervention by the technologist when needed. An example where Mayo has pushed care for patients forward for digital solutions is our AI-enabled kidney stone algorithm, as demonstrated in a recent initiative to apply AI in kidney stone FT-IR spectra analysis. Drawing on our database of more


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than 1 million kidney stones in our kidney stone FT-IR spectra library, AI algorithms were developed and validated, and their interpretations were compared with technologist interpretations. A comparison based on spectra data from >80,000 kidney stones showed an overall clinical concordance of 90% between technol- ogist and algorithm. Importantly, the AI-augmented workflow allows the lab to “identify and correctly report kidney stone constituents at a higher rate, which is crucial for treatment and recurrence prevention in the stone-forming patient.”1 Informatics: Customizing and fine-tuning workflows can result


in operational benefits, but it is important to assess if the desired outcome is being achieved. Laboratories need tools that easily and reliably provide critical laboratory data such as test result turnaround times, specimen rejection rates, and other quality metrics. We are working with our colleagues in the Division of Computational Pathology and Artificial Intelligence to assess newer tools with broader capabilities, such as navify Analytics. Next, we will take a close look at how this works in real time—i.e.,


assessing the frequency at which these rules are fired, and under- standing the types of specimens and patterns of specimens that trigger the system to preempt auto-verification. These insights will allow us to adjust the underlying rules and optimize the process. Another example is taking a closer look at workflow—identifying windows where we are running at close to capacity, versus lulls— again looking for ways to streamline and optimize. Specimen collection and handling: The ability of the new automation system to accept a multitude of different tube types has eliminated the need for manual presorting—saving staff time and time to results. Even more important is the ability of our automation line to handle a multitude of different spec- imen tube sizes. This enables us to collect only the amount of blood that is needed for the tests ordered, rather than the same specimen tube size for all patients, all tests. Because of the preanalytic automation’s ability to handle multiple blood tube sizes, we have recently observed a 50% reduction in the volume of blood required for testing on the system. This is a


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