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

TL;DR

A study used a hidden alert in medical records to enroll patients in a research trial, aiming to speed up diagnosis of autoimmune diseases.

Contribution

A novel approach using a laboratory-based CDS alert for randomizing patients into a pragmatic study was implemented and evaluated.

Findings

The risk model assessed 3,961 individuals and successfully randomized 2,105 participants.

Technical challenges included changes in laboratory vendors and test names disrupting alert functionality.

Close collaboration with laboratory teams was crucial for successful implementation.

Abstract

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.

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.

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.

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.

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