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. 2022 Mar 9;5(1):ooac017.
doi: 10.1093/jamiaopen/ooac017. eCollection 2022 Apr.

Implementation of a data control framework to ensure confidentiality, integrity, and availability of high-quality real-world data (RWD) in the NeuroTransData (NTD) registry

Knut Wehrle  1 , Viola Tozzi  2 , Stefan Braune  1 , Fabian Roßnagel  1 , Heidi Dikow  1 , Silvia Paddock  2 , Arnfin Bergmann  1 , Philip van Hövell  2
Affiliations

Implementation of a data control framework to ensure confidentiality, integrity, and availability of high-quality real-world data (RWD) in the NeuroTransData (NTD) registry

Knut Wehrle et al. JAMIA Open. .

Abstract

Objective: To implement a dynamic data management and control framework that meets the multiple demands of high data quality, rigorous information technology security, and flexibility to continuously incorporate new methodology for a large disease registry.

Materials and methods: Guided by relevant sections of the COBIT framework and ISO 27001 standard, we created a data control framework supporting high-quality real-world data (RWD) studies in multiple disease areas. We first mapped and described the entire data journey and identified potential risks for data loss or inconsistencies. Based on this map, we implemented a control framework adhering to best practices and tested its effectiveness through an analysis of random data samples. An internal strategy board was set up to regularly identify and implement potential improvements.

Results: We herein describe the implementation of a data management and control framework for multiple sclerosis, one disease area in the NeuroTransData (NTD) registry that exemplifies the dynamic needs for high-quality RWD analysis. Regular manual and automated analysis of random data samples at multiple checkpoints guided the development and implementation of the framework and continue to ensure timely identification of potential threats to data accuracy.

Discussion and conclusions: High-quality RWD, especially those derived from long-term disease registries, are of increasing importance from regulatory and reimbursement perspectives, requiring owners to provide data of comparable quality to clinical trials. The framework presented herein responds to the call for transparency in real-world analyses and allows doctors and patients to experience an immediate benefit of the collected data for individualized optimal care.

Keywords: data accuracy; information technology; reference standards; registries; trust.

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Figures

Figure 1.
Figure 1.
Overview of the components of the registry and analytic site and the data flow in the field of multiple sclerosis (MS). Results from predictive modeling feedback into the DatabasE-aSsisted Therapy decIsioN support sYstem (DESTINY®) every 3 months to support treatment decision making in the real world. DPS: data processing site; LMU: Ludwig Maximilians Universität; NTD: NeuroTransData; PUID: patient unique identifier; SPMS: secondary progressive multiple sclerosis.
Figure 2.
Figure 2.
Journey of real-world data from capturing in the NTD registry to analysis at the DPS. Diamonds indicate checkpoints of the data control framework. (1) Already during the data collection process, dropdown menus ensure consistency during the input procedures and multiple automated checks ensure that accurate data are uploaded to the server. (2) The data transfer process from the registry to the data processing site (DPS) contains multiple checkpoints to ensure the integrity of the transfer. (3) After arriving at the DPS, the data are uploaded into a secure and strictly access-controlled structured query language (SQL) database and undergo additional control steps in a process that ends with a dataset ready for analysis. DESTINY®: DatabasE-aSsisted Therapy decIsioN support sYstem; DPS: data processing site; NTD: NeuroTransData.
Figure 3.
Figure 3.
Analysis process in an example from multiple sclerosis (MS). Diamonds indicate checkpoints of the data control framework. After arriving at the DPS, the files undergo multiple checks based on version-controlled scripts written in the programming language R. Errors discovered in these checks lead to exclusion of records from further processes. The clean data are then passed on to the next step and used to calculate the new model parameters. The processes that update the model also occur in a version-controlled (“GIT”) environment to ensure that a complete audit trail of changes is available. The updated App with new model predictions then undergoes series of tests for feasibility before the final version is uploaded to the public website.
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