The Trial Enrollment Diversity Dashboard for Acute Leukemia Clinical Research: Intervention Development and Cohort Analysis.

PURPOSE

Participation in acute leukemia clinical trials is inequitable across multiple sociodemographic categories. Tools that provide researchers with performance feedback on the representativeness of the patients they enroll are limited. We aimed to develop an electronic health record (EHR)-based dashboard to provide such feedback and to describe any enrollment inequities uncovered.

METHODS

We created a visual dashboard linking leukemia clinical trial registration and EHR data at the Dana-Farber Cancer Institute. Accuracy of a patient inclusion and assignment algorithm was tested with a target area under the receiver-operator curve (AUROC) of >0.90 against manual review. Demographic metric identification, visualization construction, and dashboard refinement were performed through stakeholder cognitive testing. Analysis of a recent 5-year cohort generated by the final algorithm assessed bivariate associations between enrollment and demographic metrics. Multivariable logistic regression included significant bivariate results.

RESULTS

The final algorithm assignment AUROC was 0.98. Metrics were identified and visualizations successfully constructed. Fourteen individuals participated in testing and identified areas for revision: category mergers, denominator filters, and data delivery preferences. In the initial cohort of 1,315 patients, 1,020 (77.6%) had enrolled in any study protocol: 553 (42.1%) in a treatment trial and 936 (71.2%) in a biobanking study. In a multivariable model, older age (odds ratio [OR], 0.83 [95% CI, 0.73 to 0.94]) and Non-Hispanic Black race-ethnicity (OR, 0.38 [95% CI, 0.18 to 0.82]) were associated with lower enrollment, and English primary language with higher enrollment (OR, 2.50 [95% CI, 1.30 to 4.79]).

CONCLUSION

We developed a research participation equity performance feedback dashboard for clinical researchers, and we identified actionable inequities. Next steps include feasibility and efficacy testing as well as implementation.