Publications

Journal Article Epublish

PubMed: 41180892 DOI: 10.1093/jamiaopen/ooaf125

Meeting clinical recruitment milestones in an academic center: a data-driven, visual approach.

JAMIA Open 8(5):ooaf125

Authors: Anna E Burns, John Tumberger, Mariah Brewe, Michael Bartkoski, Stephani L Stancil

Affiliations

  • Division of Clinical Pharmacology, Toxicology and Therapeutic Innovation, Children's Mercy Kansas City, Kansas City, MO 64018, United States.
  • University of Kansas School of Medicine, Kansas City, KS 66160, United States.
  • Division of Adolescent Medicine, Children's Mercy Kansas City, Kansas City, MO 64018, United States.
  • Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, MO 64018, United States.

Abstract

OBJECTIVES

Describing the development of a visual dashboard leveraging available tools for efficient recruitment for patient centered clinical trials in resource constrained settings.

MATERIALS AND METHODS

A real-time, visual dashboard was developed, facilitating interactive visualizations, detailed analyses, and data quality control. Daily automated REDCap data retrieval occurred via an R program using REDCap API and output was integrated into Power BI. An interrupted time series analysis was conducted evaluating effects of dashboard on clinical trial recruitment metrics.

RESULTS

The visual dashboard displayed key recruitment metrics, including individual participant progression and recruitment trends over time. Interrupted time series analysis showed improvements in screening rates upon implementation. The mean time to study completion decreased by 19 days following implementation.

DISCUSSION

Customizable metrics offer comprehensive view of recruitment data and granularity, identifying actionable issues, enhancing study timeliness and completion.

CONCLUSION

Clinical trials of all budgets can integrate dashboards for real-time monitoring and data driven improvements to promote more timely completion.

MeSH

Data Visualization, Biomedical Research, Interrupted Time Series Analysis