Workflow‑Based Information Management Framework for Multicenter Research Studies: Design and Development

Biological and health research is increasingly data-driven, with commercial and academic institutions generating data at unprecedented rates. The rapid pace of data generation, together with lessons learned during the COVID-19 pandemic, underscores the need for nimble, transparent, and dependable data infrastructures that enable rapid study execution and timely insights to inform public health policy and practice.

This paper describes the workflow-based information management (WIM) framework, a flexible research information management system designed to support diverse epidemiologic workflows and data-intensive research projects.

WIM was developed as a modular, workflow-oriented framework built on the open-source R (R Foundation) programming language and its extensive ecosystem of community-developed packages. The framework emphasizes reproducibility, adaptability, and transparency, enabling users to design and manage research workflows tailored to specific study requirements. We describe the architecture and core components of WIM and illustrate its application through representative epidemiologic research scenarios.

The framework supported high-volume, multiorganizational research; managing >3.7 million donation and testing records from 17 blood collection organizations across the United States. The WIM framework was readily adapted to a wide range of epidemiologic studies and research projects, demonstrating flexibility across varying data types, analytical needs, and operational contexts. By leveraging established R-based tools and workflows, WIM supported efficient data ingestion, processing, analysis, and reporting while promoting reproducible and collaborative research practices. The framework facilitated rapid iteration and reuse of workflows, addressing common challenges in managing complex and evolving research studies.

WIM provides a flexible, open-source, and extensible approach to research information management for modern biological and health research. By integrating workflow-based design principles with the R ecosystem, the framework supports reproducible analysis, scalable research operations, and rapid study execution. WIM offers a practical solution for institutions seeking adaptable data infrastructure to support epidemiologic research and inform public health decision-making.