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Waters Simplifies the Clinical Data Review Process With New Waters_connect™ for IVD and QUAN Review Application

A scientist pipetting into a test tube.
Credit: iStock.
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Waters announces the release of waters_connect™ for In Vitro Diagnostic (IVD), a software platform designed for maximum data integrity, compliance, security, and accessibility that provides a backbone for clinicians to securely share data. With QUAN Review, a waters_connect™ for IVD application, laboratories can now simplify their clinical LC-MS data processing with a layered approach to data analysis in an easy-to-use software package.

 

Introduced at the Mass Spectrometry and Advances in the Clinical Lab (MSACL) 2024 14th Annual Conference & Exhibits in March, waters_connect™ for IVD software and QUAN Review application are available now in the United States, the United Kingdom, Canada, and Europe.

 

Key features of the Waters QUAN Review Application include:

  • Reduce data review time by up to 50% (as compared to Waters TargetLynx™ Software).
  • Offers a one-screen dashboard that summarizes key parameters such as standards, calibration, and quality control (QC) – grouped together for a streamlined view.
  • Allows users to easily see and quickly address errors that need attention via Exception-Focused Review (xfR).
  • PanoGramic Display, a view that enables users to quickly identify problems or ambiguous data by showing multiple chromatograms, calibrations, and QC at the same time.
  • Built-in connectivity to the user’s laboratory information management system (LIMS).

 

The software allows laboratories to set flags or alerts according to their standard operating procedures (SOPs). Additionally, users can:

  • Leverage the default result set to highlight data that is out of range such as R2, standard deviation or signal-to-noise values.
  • Select which rules are to be applied and the range/tolerance for each (e.g., the internal standard must be within 20% of the median response).
  • Reduce turnaround time for labs with high sample volume and complex data sets.