MAY 29, 2014 7:30 AM PDT

Ensuring Quality Using IT Algorithms

Speakers
  • Chemistry Automation Supervisor Chemical Pathology Laboratory, University of Michigan Health System
    Biography
      Eric Vasbinder , Chemistry Automation Supervisor  at the University of Michigan Medical Center has 26 years experience in the clinical diagnostics industry.   Eric is responsible for the automation section of chemistry, which performs almost 9 million tests a year and operates in a 24X7 environment.  The laboratory provides testing services for all three hospitals on campus, with a total of 865 beds and 1,600 physicians. The laboratory also provides services to 30 health centers and 120 outpatient clinics within a 60-mile radius.  Erics extensive laboratory experience includes toxicology where he developed new assay procedures, improved existing methods, and chemistry automation.  Erics laboratory has been involved in numerous human studies and clinical trials all performed on the automation line.  The laboratory is currently supporting the Hepcidin and Anemia in trauma clinical trials, an ongoing study for the last 3 years.  In his role as Automation Manager, Eric has developed innovative new workflows and procedures which he successfully integrated into the labs routine clinical operations.  The resulting efficiency and productivity gains have helped the University of Michigan Medical Center laboratory to reduce errors by 73% and increase volume 97%, while holding headcount flat over the past 7 years.  Eric has a B.S. in Medical Technology with a minor in Microbiology from Michigan Technological University

    Abstract:

    Case Example: University of Michigan Chemical Pathology Laboratory

    Every laboratory wants to achieve the highest quality possible and meet its commitment to deliver timely results. However, managing Quality Control (QC) is frequently a manual and time-consuming process which may raise turnaround time and the risk of errors. In this session, Eric Vasbinder from University of Michigan Hospital will demonstrate proven techniques such as patient moving averages and Westgard rules to proactively manage QC issues. Over the past 7 years, the University of Michigan has been able to reduce errors by 73% and increase volume 97%, while holding headcount flat. Learn the tips and techniques that have worked for this progressive laboratory to optimize QC with the CentraLink Data Management System.

    Learning Objectives:

    Learn how to proactively manage QC and prevent problems before they occur
    Evaluate the right batch size for patient moving averages
    Explore the benefits of integrating QC with your data management process
    Pinpoint the problem areas and redirect samples to minimize delay and reagent waste


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