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Using Business Intelligence Tools to Support Medical Validation of Laboratory Tests.

Gian-Andrea Degen1,2, Viola Günther, Jürgen Holm1

  • 1Bern University of Appl. Sciences, Dept. Medical Informatics, Switzerland.

Studies in Health Technology and Informatics
|June 24, 2020
PubMed
Summary
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Clinical laboratories can improve test result validation efficiency using an R-based business intelligence (BI) tool. This digital approach streamlines manual processes, enhancing reliability and reducing errors in medical validation.

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Health Informatics

Background:

  • Clinical laboratory testing requires reliable and valid procedures to ensure accurate patient diagnoses.
  • Sources of error and interference can compromise the integrity of laboratory test results.
  • Medical validation is crucial for identifying implausible results that may indicate sample issues or procedural errors.

Purpose of the Study:

  • To develop an integrated R-based business intelligence (BI) tool to enhance the efficiency of medical validation processes.
  • To digitalize manual validation steps previously performed using Excel worksheets.
  • To improve the reliability and validity of clinical test results at the Institute of Clinical Chemistry (ICC).

Main Methods:

  • Development of a business intelligence (BI) software environment utilizing the R programming language.
Keywords:
Clinical validationRbusiness intelligencedelta checkextreme value analysis

Related Experiment Videos

  • Digitalization of manual tasks including data import, percentile calculation, and graphical output generation.
  • Integration of the BI tool into the medical validation workflow at the University Hospital Zurich's ICC.

    • Main Results:

    • The developed BI tool successfully digitalized and streamlined manual validation steps.
    • Implementation led to increased efficiency in data processing and result verification.
    • The R-based BI environment facilitated the calculation of percentiles and the production of graphical outputs for enhanced validation.

    Conclusions:

    • An integrated R-based BI tool can significantly increase the efficiency of medical validation in clinical chemistry.
    • Digitalization of manual laboratory processes using BI tools enhances data management and result reliability.
    • The developed tool offers a scalable solution for improving quality control in clinical laboratory settings.