Neuro Data Hub: A New Approach for Streamlining Medical Clinical Research

Abstract

BACKGROUND AND OBJECTIVES: Neurosurgical clinical research depends on medical data collection and evaluation that is often laborious, time consuming, and inefficient. The goal of this work was to implement and evaluate a novel departmental data infrastructure (Neuro Data Hub) designed to provide specialized data services for neurosurgical research. Data acquisition would become available purely by request. METHODS: through collaboration between Department Leadership and Medical Center Information Technology, integrating it with Institutional Review Board workflows and an existing Epic electronic health record Datalake infrastructure. The system implementation included monthly departmental meetings and an asynchronous Research Electronic Data Capture-based request system. Data requests submitted between August 2023 and November 2024 were analyzed and categorized as basic, complex, or Natural Language Processing (NLP)-augmented, with optional visualization and database creation services. Request volumes, types, and execution times were assessed. RESULTS: The Hub processed 39 research data requests (2.6/month), comprising 3 basic, 22 complex, and 14 NLP-augmented requests. Two complex requests included visualization services, and one NLP request included database creation. Average request execution time was 36.5 days, with NLP-augmented requests showing increasing adoption over time. CONCLUSION: The Neuro Data Hub represents a paradigm shift from centralized to department-level data services, providing specialized support for neurosurgical research and democratizing access to institutional data. While effective, implementation may be limited by institutional information technology infrastructure requirements. This model could serve as a template for any form of medical-clinical research program seeking to improve data accessibility and research capabilities.

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