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CC BY 4.0 · Appl Clin Inform 2025; 16(05): 1439-1444
DOI: 10.1055/a-2702-1770
Special Issue on CDS Failures

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

Authors

  • April Barnado

    1   Division of Rheumatology and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States
    2   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

  • Ryan P. Moore

    3   Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

  • Henry J. Domenico

    3   Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

  • Emily Grace

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

  • Sarah Green

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

  • Ashley Suh

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

  • Nikol Nikolova

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

  • Bryan Han

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

  • Allison B. McCoy

    2   Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States

Funding This work was supported by the National Institutes of Health/National Institute of Arthritis and Musculoskeletal and Skin Diseases (U.S. Department of Health and Human Services, National Institutes of Health, National Institute of Arthritis and Musculoskeletal and Skin Diseases, grant no.: R01 AR080629, A.B.); National Institutes of Health/National Center for Research Resources (U.S. Department of Health and Human Services, National Institutes of Health, National Center for Research Resources, grant no.: UL1 RR024975, VUMC); National Institutes of Health/National Center for Advancing Translational Sciences (U.S. Department of Health and Human Services, National Institutes of Health, National Center for Advancing Translational Sciences, grant no.: ULTR000445, VUMC); Vanderbilt University Medical Center Department of Biomedical Informatics Catalyzing Informatics Innovation Program.
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Abstract

Objectives

Our objective was to identify barriers to implementing a custom clinical decision support (CDS) alert to randomize individuals in a pragmatic study, specifically those with a positive antinuclear antibody (ANA) test.

Methods

We integrated a validated logistic regression model into the electronic health record to predict the risk of developing autoimmune disease for individuals with a positive ANA (titer ≥ 1:80). A custom CDS alert was created to randomize eligible individuals into a pragmatic study evaluating whether the risk model reduces time to autoimmune disease diagnosis. The custom CDS alert runs silently in the background and is not visible to providers. Individuals were randomized to either an intervention or control arm. In the intervention arm, the study team reviewed risk model results, notified providers of high-risk scores, and offered expedited rheumatology referrals to high-risk individuals in addition to standard of care. The control arm received standard care only. The study team accessed a daily Epic report containing randomization assignments and model variables.

Results

Starting in June 2023, the risk model assessed 3,961 individuals and successfully randomized 2,105 individuals to date. Technical challenges that prevented the custom CDS alert from firing included an unanticipated change in the laboratory testing vendor and reporting due to a broken laboratory machine, followed by a change in the laboratory test name.

Conclusion

This case report showcases the successful implementation of a laboratory-based custom CDS alert to randomize individuals for a pragmatic study. This approach enabled our study to be feasible across a large health care system. Key lessons learned included the importance of close collaboration with the laboratory team and thorough understanding of the laboratory testing, workflow, and reporting to ensure successful execution of the laboratory-based custom CDS alert.

Keywords

electronic health records and systems - clinical decision support - implementation and deployment - clinical care - precision medicine

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the Vanderbilt University Medical Center Institutional Review Board (IRB approval no.: 230636).




Publikationsverlauf

Eingereicht: 13. Januar 2025

Angenommen: 13. Juli 2025

Artikel online veröffentlicht:
24. Oktober 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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