JMIR Publications

ABSTRACT

Background:

Within the Network University Medicine (NUM) projects, which are an important part of the German COVID-19 research infrastructure, there are two important subprojects: The Corona Data Exchange platform (CODEX) and the Coordination on mobile pandemic apps best practice and solution sharing (COMPASS) exist. CODEX is a centralized and secure data storage platform for research data, while COMPASS entails a reference app framework that can be used free of charge by any researcher to capture patient reported outcomes (PROs) by any researcher, free of charge.

Objective:

The aim of this project was to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that it can be used by secondary researchers. Even though both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, the standard was not used in a way that data could be shared directly. Given the short timeframe and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was therefore required.

Methods:

We have developed means to facilitate and promote the use of the German Corona Consensus (GECCO) dataset, a core data set for COVID research in Germany. In this way, we ensure semantic interoperability for app-collected PRO data with the app. We also developed an interface component to sustain syntactic interoperability.

Results:

The use of different FHIR resource types by the COMPASS reference app framework (the general-purpose Questionnaire) and CODEX platform (other, more medical resource types) was found to be the significant obstacle to overcome. Therefore, an interface component that realigns the questionnaire items with the corresponding items in the GECCO datasets and emits the correct resources for the CODEX platform was developed. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO dataset for researchers.

Conclusions:

This paper shows how PRO data, which is collected across different studies conducted by different researchers, can be captured in a research compatible way. This means, that it can be shared with the central research infrastructure and reused by other researches to gain more insights about COVID-19 and its sequelae.