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A Case Report in Using a Laboratory-Based Decision Support Alert for Research Enrollment and Randomization.

April Barnado1,2, Ryan P Moore3, Henry J Domenico3

  • 1Division of Rheumatology and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States.

Applied Clinical Informatics
|October 24, 2025
PubMed
Summary
This summary is machine-generated.

Implementing a custom clinical decision support (CDS) alert for pragmatic studies involving antinuclear antibody (ANA) positive individuals faced technical hurdles. Close collaboration with laboratory teams is crucial for successful alert execution.

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Area of Science:

  • Clinical Informatics
  • Rheumatology
  • Health Services Research

Background:

  • Positive antinuclear antibody (ANA) tests are common but do not always indicate autoimmune disease.
  • Predictive models can identify individuals at higher risk for developing autoimmune conditions.
  • Pragmatic studies require efficient methods for participant randomization within electronic health records.

Purpose of the Study:

  • To identify barriers in implementing a custom clinical decision support (CDS) alert for participant randomization in a pragmatic study.
  • To evaluate the feasibility of using a CDS alert triggered by positive ANA tests for autoimmune disease risk prediction and early referral.

Main Methods:

  • Integrated a validated logistic regression model into the electronic health record (EHR) to predict autoimmune disease risk in ANA-positive individuals (titer ≥ 1:80).
  • Developed a silent, custom CDS alert to randomize eligible individuals into a pragmatic study.
  • Randomized participants into an intervention arm (risk score review, provider notification, expedited referrals) and a control arm (standard care).

Main Results:

  • Successfully assessed 3,961 individuals and randomized 2,105 since June 2023.
  • Encountered technical challenges including laboratory vendor changes, equipment failure, and altered test names, which impacted CDS alert functionality.
  • Demonstrated the feasibility of a laboratory-based CDS alert for randomization across a large healthcare system.

Conclusions:

  • Laboratory-based custom CDS alerts can facilitate participant randomization in pragmatic studies within large healthcare systems.
  • Close collaboration with laboratory teams and a deep understanding of laboratory testing workflows are essential for successful CDS alert implementation.
  • Addressing technical challenges proactively is key to ensuring the continuous operation of CDS alerts in research settings.