Designing and Implementing an IT Architecture for a Digital Multicenter Dementia Registry: digiDEM Bayern

Abstract

Background Registries are an essential research tool to investigate the long-term course of diseases and their impact on the affected. The project digiDEM Bayern will set up a prospective dementia registry to collect long-term data of people with dementia and their caregivers in Bavaria (Germany) supported by more than 300 research partners. Objective The objective of this article is to outline an information technology (IT) architecture for the integration of a registry and comprehensive participant management in a dementia study. Measures to ensure high data quality, study governance, along with data privacy, and security are to be included in the architecture. Methods The architecture was developed based on an iterative, stakeholder-oriented process. The development was inspired by the Twin Peaks Model that focuses on the codevelopment of requirements and architecture. We gradually moved from a general to a detailed understanding of both the requirements and design through a series of iterations. The experience learned from the pilot phase was integrated into a further iterative process of continuous improvement of the architecture. Results The infrastructure provides a standardized workflow to support the electronic data collection and trace each participant's study process. Therefore, the implementation consists of three systems: (1) electronic data capture system for Web-based or offline app-based data collection; (2) participant management system for the administration of the identity data of participants and research partners as well as of the overall study governance process; and (3) videoconferencing software for conducting interviews online. First experiences in the pilot phase have proven the feasibility of the framework. Conclusion This article outlines an IT architecture to integrate a registry and participant management in a dementia research project. The framework was discussed and developed with the involvement of numerous stakeholders. Due to its adaptability of used software systems, a transfer to other projects should be easily possible.

Computerised cognitive training tools and online nutritional group counselling for people with mild cognitive impairment: study protocol of a completely digital, randomised, controlled trial

“…The IT architecture including the digital study administration and data collection was ‘inspired’ by the digiDem Bayern Registry. 75 Results of the study for scientific or other publications will be published only in aggregate form (mean values, etc). No published material will contain patient-identifying information.…”

“…REDCap is a secure, web-based software platform designed to support data capturing for research studies. The IT architecture including the digital study administration and data collection was ‘inspired’ by the digiDem Bayern Registry 75. Results of the study for scientific or other publications will be published only in aggregate form (mean values, etc).…”

Usability Evaluation of an Offline Electronic Data Capture App in a Prospective Multicenter Dementia Registry (digiDEM Bayern): Mixed Method Study

“…In digiDEM, we use REDCap as our EDC system [ 24 ]. REDCap is a secure, web-based software platform designed to support data capture for research studies.…”

“…Data collection often takes place in rural areas in the homes of people with dementia, where internet access is patchy or unavailable. In these cases, the project will gather electronic data offline using mobile devices and the app of the EDC system known as REDCap (Research Electronic Data Capture) [ 24 , 25 ].…”

Data silos undermine efforts to characterize, predict, and mitigate dementia-related missing person incidents

“…Standardized measurements, specifications regarding data standards and coding rules, training, reporting standards, cross-sector consistency, data quality mechanisms, and proper data architecture and technical infrastructure all have to be in place. 32,36 In order to facilitate data analytics and data integrity processes, it would be necessary to re-code existing data to a common data model. 37 The data systems should be structured to be compatible with global health data architecture, such as HL7 ® (Health Level 7) and FHIR ® (Fast Healthcare Interoperability Resources).…”