Recommended data elements for health registries: a survey from a German funding initiative.

BACKGROUND: The selection of data elements is a decisive task within the development of a health registry. Having the right metadata is crucial for answering the particular research questions. Furthermore, the set of data elements determines the registries' readiness of interoperability and data reusability to a major extent. Six health registries shared and published their metadata within a German funding initiative. As one step in the direction of a common set of data elements, a selection of those metadata was evaluated with regard to their appropriateness for a broader usage. METHODS: Each registry was asked to contribute a 10%-selection of their data elements to an evaluation sample. The survey was set up with the online survey tool "LimeSurvey Cloud". The registries and an accompanying project participated in the survey with one vote for each project. The data elements were offered in content groups along with the question of whether the data element is appropriate for health registries on a broader scale. The question could be answered using a Likert scale with five options. Furthermore, "no answer" was allowed. The level of agreement was assessed using weighted Cohen's kappa and Kendall's coefficient of concordance. RESULTS: The evaluation sample consisted of 269 data elements. With a grade of "perhaps recommendable" or higher in the mean, 169 data elements were selected. These data elements belong preferably to groups' demography, education/occupation, medication, and nutrition. Half of the registries lost significance compared with their percentage of data elements in the evaluation sample, one remained stable. The level of concordance was adequate. CONCLUSIONS: The survey revealed a set of 169 data elements recommended for health registries. When developing a registry, this set could be valuable help in selecting the metadata appropriate to answer the registry's research questions. However, due to the high specificity of research questions, data elements beyond this set will be needed to cover the whole range of interests of a register. A broader discussion and subsequent surveys are needed to establish a common set of data elements on an international scale.

Cross-Registry Benchmarking of Data Quality: Lessons Learned.

Feedback of data quality measures to study sites is an established procedure in the management of registries. Comparisons of data quality between registries as a whole are missing. We implemented a cross-registry benchmarking of data quality within the field of health services research for six projects. Five (2020) and six (2021) quality indicators were selected from a national recommendation. The calculation of the indicators was adjusted to the registries' specific settings. Nineteen (2020) and 29 results (2021) could be included in the yearly quality report. Seventy-four per cent (2020) and 79% (2021) of the results did not include the threshold in their 95%-confidence-limits. The benchmarking revealed several starting points for a weak-point analysis through a comparison of results with a predefined threshold as well as through comparisons among each other. In the future, a cross-registry benchmarking might be part of services provided through a health services research infrastructure.

FAIR and Quality Assured Data - The Use Case of Trueness.

The FAIR Guiding Principles do not address the quality of data and metadata. Therefore, data collections could be FAIR but useless. In a funding initiative of registries for health services research, trueness of data received special attention. Completeness in the definition of recall was selected to represent this dimension in a cross-registry benchmarking. The first analyses of completeness revealed a diversity of its implementation. No registry was able to present results exactly as requested in a guideline on data quality. Two registries switched to a source data verification as alternative, the three others downsized to the dimension integrity. The experiences underline that the achievement of appropriate data quality is a matter of costs and resources, whereas the current Guiding Principles quote for a transparent culture regarding data and metadata. We propose the extension to FAIR-Q, data collections should not only be findable, accessible, interoperable, and reusable, but also quality assured.

[Germany-wide, Web-based ParaReg Registry for Lifelong Monitoring of People with Spinal Cord Injury: Data Model, Ethico-legal Prerequisites and Technical Implementation]. / Das deutschlandweite, webbasierte ParaReg-Register zur lebenslangen Dokumentation von Querschnittgelähmten ­ Datenmodell, rechtlich-ethische Voraussetzungen und technische Implementierung.

OBJECTIVE: In Germany, treatment paths for patients with acute spinal cord injury (SCI) differ considerably depending on intrinsic, disease-specific and extrinsic factors. Which of these factors are associated with improved outcome with fewer subsequent complications and inpatient re-admissions is not clear. The German-wide, patient-centered, web-based ParaReg registry will be implemented to improve the long-term quality of patient care and the planning of treatment paths with increased cost-effectiveness. METHODS: In the 2017-18 conceptualization phase, the data model of the registry was developed in an iterative process of the ParaReg steering committee together with the extended DMGP board and patient representatives. In ParaReg, routine social and medical data as well as internationally established neurological, functional and participation scores will be documented. The assignment of a unique patient ID allows a lifelong, cross-center documentation of inpatient stays in one of the 27 SCI centers organized in the German-speaking Medical Society for SCI (DMGP). The ParaReg data protection concept and patient information/consent are based on the Open Source Registry for Rare Diseases (OSSE) which were extended by GDPR-relevant aspects. RESULTS: In the realization phase, which started in 2019, the information technology infrastructure was implemented according to the clinical ID management module of the Technology and Methods Platform for Networked Medical Research (TMF). In parallel, the legal and ethical prerequisites for registry operation under the patronage of the DMGP were created. Recommendations of the working group data protection of the TMF were integrated into ParaReg's data protection concept. Based on the feedback from the alpha test phase with documentation of the hospitalization data of 40 patients, the ergonomics of the electronic case report forms were improved in particular for data entry on mobile devices. CONCLUSION: After completion of the monocentric alpha test phase, the multicenter data acquisition was started in 5 DMGP-SCI centers. The sustainability of ParaReg is ensured by the structural and financial support of the DMGP after expiry of the funding by the German Federal Ministry of Education and Research (BMBF).