Layer Upon Layer: Developing Layered Modular Architectures for Data-Driven Health Platform

Data collection is a foundational aspect of research, as most research projects ultimately rely on their data. Quick and effective data overview is crucial, especially when working with extensive datasets. This paper seeks to add to the ongoing discourse about designing and developing layered-modular architectures for data-driven health platforms. We particularly emphasize data representation and address the research question: How can layered-modular architecture be used to present and filter research data in a digital platform? We designed and developed a layered-modular architecture for a digital platform aimed at data extraction and representation. We argue that this approach renders digital platforms more reusable, resilient to changes, and easily extendable. To evaluate its effectiveness, we evaluated the layered-modular architecture of the platform with ten experts and collected data on user experience and usability, along with semi-structured interviews. Furthermore, we examined the system’s scalability with increasing data loads. The paper’s key contribution revolves around the layered-modular architecture and design principles tailored for digital platforms, aiming to function effectively in socio-technical environments, particularly within intricate multidisciplinary research projects.