Trying to accomplish more with less is a challenge many labs face. A scientific session July 31 at the 70th AACC Annual Scientific Meeting & Clinical Lab Expo will spotlight three rapidly advancing technologies that seek to create efficiencies and reduce errors: machine learning applied to clinical prediction, mass spectrometry (MS), and clinical laboratory automation.
(33214) Solving Laboratory Diagnostic Challenges with Technology, Automation, and Innovation—Closing the “Brain-to-Brain” Loop will take place from 2:30 to 5 p.m. and is worth 2.5 CE hours.
“Laboratory automation and IT solutions present opportunities to create efficient workflows that help to ensure analytical quality while permitting well-trained staff to focus on complex problems that benefit from their skills and expertise,” session speaker Jonathan Genzen, MD, PhD, section chief for clinical chemistry at ARUP Laboratories in Salt Lake City and associate professor of pathology at the University of Utah, told CLN Stat.
Strategies may involve process improvement initiatives for relatively small tasks or total laboratory automation (TLA) solutions. “Furthermore, many laboratory processes are repetitive tasks. Automation of these activities reduces the risk of operators developing repetitive motion injuries,” said Genzen, who will speak on identifying and resolving preanalytic errors through technology, automation, and innovation.
Genzen joins two other speakers, Frederick Strathmann, PhD, DABCC, vice president of quality assurance at NMS Labs in Willow Grove, Pennsylvania, whose talk will focus on leveraging novel technologies and quality strategies, and Daniel Herman, MD, PhD, an assistant professor at the University of Pennsylvania, who will discuss data-driven approaches to improve test interpretation and identify patients for testing.
The session as a whole will highlight opportunities for simultaneously applying machine learning, MS, and automation to improve lab testing and reduce the risk of erroneous results.
Closely controlled IT rules largely drive laboratory automation. However, notable opportunities exist to improve test efficiency (e.g. algorithmic testing), identify errors (e.g. delta checks), and maximize patient impact (e.g. interpretive comments applied by middleware rules), according to Genzen. “We’re really at the early stages of enabling smarter systems to help clinicians in identifying important information that may otherwise get lost in the traditional modalities of result reporting,” he explained. “The presentations by Drs. Herman and Strathmann will show how these ideas can be advanced in even more exciting and impactful ways through machine learning and mass spectrometry.”
Genzen’s own presentation will address common sense strategies for maximizing the quality of preanalytic processes. “This is an area where most laboratory errors occur, and it’s where new technologies and approaches could really make a difference,” he suggested.
Automation strategies, especially preanalytic ones, help ensure that labs are testing the right specimen from the correct patient, and that the specimen integrity and condition are appropriate for the tests being ordered. “But these are efficiency goals for a present state,” Genzen noted. “A more exciting question is how can we create solutions for the future laboratory state, to help deliver quality testing at lower cost and greater clinical impact. That’s an undercurrent that extends across each of our presentations and will be highlighted throughout this session.”
Don’t miss this important session on lab IT and automation strategies. Register now to attend the 70th AACC Annual Scientific Meeting & Clinical Lab Expo July 29–August 2 in Chicago.