The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study

Gierend, Kerstin; Freiesleben, Sherry; Kadioglu, Dennis; Siegel, Fabian; Ganslandt, Thomas; Waltemath, Dagmar

doi:10.2196/48809

Cited by 4 publications

(1 citation statement)

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“…Dabei ist die gleichzeitige Einhaltung der gesetzlichen Datenschutzanforderungen essenziell und muss durch geeignete Maßnahmen, wie z. B. eine zuverlässige Deidentifizierung, sichergestellt werden [ 9 , 10 ].…”

Section: Hintergrundunclassified

FAIR health data in the national and international data space

Waltemath,

Beyan,

Crameri

et al. 2024

Bundesgesundheitsbl

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ZusammenfassungGesundheitsdaten haben in der heutigen datenorientierten Welt einen hohen Stellenwert. Durch automatisierte Verarbeitung können z. B. Prozesse im Gesundheitswesen optimiert und klinische Entscheidungen unterstützt werden. Dabei sind Aussagekraft, Qualität und Vertrauenswürdigkeit der Daten wichtig. Nur so kann garantiert werden, dass die Daten sinnvoll nachgenutzt werden können.Konkrete Anforderungen an die Beschreibung und Kodierung von Daten werden in den FAIR-Prinzipien beschrieben. Verschiedene nationale Forschungsverbünde und Infrastrukturprojekte im Gesundheitswesen haben sich bereits klar zu den FAIR-Prinzipien positioniert: Sowohl die Infrastrukturen der Medizininformatik-Initiative als auch des Netzwerks Universitätsmedizin operieren explizit auf Basis der FAIR-Prinzipien, ebenso die Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten oder das Deutsche Zentrum für Diabetesforschung.Um eine FAIRe Ressource bereitzustellen, sollte zuerst in einem Assessment der FAIRness-Grad festgestellt werden und danach die Priorisierung für Verbesserungsschritte erfolgen (FAIRification). Seit 2016 wurden zahlreiche Werkzeuge und Richtlinien für beide Schritte entwickelt, basierend auf den unterschiedlichen, domänenspezifischen Interpretationen der FAIR-Prinzipien.Auch die europäischen Nachbarländer haben in die Entwicklung eines nationalen Rahmens für semantische Interoperabilität im Kontext der FAIR-Prinzipien investiert. So wurden Konzepte für eine umfassende Datenanreicherung entwickelt, um die Datenanalyse beispielsweise im Europäischen Gesundheitsdatenraum oder über das Netzwerk der Observational Health Data Sciences and Informatics zu vereinfachen. In Kooperation mit internationalen Projekten, wie z. B. der European Open Science Cloud, wurden strukturierte FAIRification-Maßnahmen für Gesundheitsdatensätze entwickelt.

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Section: Hintergrundunclassified

FAIR health data in the national and international data space

Waltemath,

Beyan,

Crameri

et al. 2024

Bundesgesundheitsbl

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Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study

Gierend,

Waltemath,

Ganslandt

et al. 2023

JMIR Form Res

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Background Secondary investigations into digital health records, including electronic patient data from German medical data integration centers (DICs), pave the way for enhanced future patient care. However, only limited information is captured regarding the integrity, traceability, and quality of the (sensitive) data elements. This lack of detail diminishes trust in the validity of the collected data. From a technical standpoint, adhering to the widely accepted FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship necessitates enriching data with provenance-related metadata. Provenance offers insights into the readiness for the reuse of a data element and serves as a supplier of data governance. Objective The primary goal of this study is to augment the reusability of clinical routine data within a medical DIC for secondary utilization in clinical research. Our aim is to establish provenance traces that underpin the status of data integrity, reliability, and consequently, trust in electronic health records, thereby enhancing the accountability of the medical DIC. We present the implementation of a proof-of-concept provenance library integrating international standards as an initial step. Methods We adhered to a customized road map for a provenance framework, and examined the data integration steps across the ETL (extract, transform, and load) phases. Following a maturity model, we derived requirements for a provenance library. Using this research approach, we formulated a provenance model with associated metadata and implemented a proof-of-concept provenance class. Furthermore, we seamlessly incorporated the internationally recognized Word Wide Web Consortium (W3C) provenance standard, aligned the resultant provenance records with the interoperable health care standard Fast Healthcare Interoperability Resources, and presented them in various representation formats. Ultimately, we conducted a thorough assessment of provenance trace measurements. Results This study marks the inaugural implementation of integrated provenance traces at the data element level within a German medical DIC. We devised and executed a practical method that synergizes the robustness of quality- and health standard–guided (meta)data management practices. Our measurements indicate commendable pipeline execution times, attaining notable levels of accuracy and reliability in processing clinical routine data, thereby ensuring accountability in the medical DIC. These findings should inspire the development of additional tools aimed at providing evidence-based and reliable electronic health record services for secondary use. Conclusions The research method outlined for the proof-of-concept provenance class has been crafted to promote effective and reliable core data management practices. It aims to enhance biomedical data by imbuing it with meaningful provenance, thereby bolstering the benefits for both research and society. Additionally, it facilitates the streamlined reuse of biomedical data. As a result, the system mitigates risks, as data analysis without knowledge of the origin and quality of all data elements is rendered futile. While the approach was initially developed for the medical DIC use case, these principles can be universally applied throughout the scientific domain.

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Provenance Information for Biomedical Data and Workflows: Scoping Review

Gierend,

Krüger,

Genehr

et al. 2024

J Med Internet Res

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Background The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research. Objective This scoping review aims to identify approaches and criteria for provenance tracking in the biomedical domain. We reviewed the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking. Methods This scoping review followed the methodological framework developed by Arksey and O’Malley. We searched the PubMed and Web of Science databases for English-language articles published from 2006 to 2022. Title and abstract screening were carried out by 4 independent reviewers using the Rayyan screening tool. A majority vote was required for consent on the eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading and screening were performed independently by 2 reviewers, and information was extracted into a pretested template for the 5 research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published. Results The search resulted in a total of 764 papers. Of 624 identified, deduplicated papers, 66 (10.6%) studies fulfilled the inclusion criteria. We identified diverse provenance-tracking approaches ranging from practical provenance processing and managing to theoretical frameworks distinguishing diverse concepts and details of data and metadata models, provenance components, and notations. A substantial majority investigated underlying requirements to varying extents and validation intensities but lacked completeness in provenance coverage. Mostly, cited requirements concerned the knowledge about data integrity and reproducibility. Moreover, these revolved around robust data quality assessments, consistent policies for sensitive data protection, improved user interfaces, and automated ontology development. We found that different stakeholder groups benefit from the availability of provenance information. Thereby, we recognized that the term provenance is subjected to an evolutionary and technical process with multifaceted meanings and roles. Challenges included organizational and technical issues linked to data annotation, provenance modeling, and performance, amplified by subsequent matters such as enhanced provenance information and quality principles. Conclusions As data volumes grow and computing power increases, the challenge of scaling provenance systems to handle data efficiently and assist complex queries intensifies, necessitating automated and scalable solutions. With rising legal and scientific demands, there is an urgent need for greater transparency in implementing provenance systems in research projects, despite the challenges of unresolved granularity and knowledge bottlenecks. We believe that our recommendations enable quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily tasks of biomedical scientists. International Registered Report Identifier (IRRID) RR2-10.2196/31750

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The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study

Cited by 4 publications

References 36 publications

FAIR health data in the national and international data space

FAIR health data in the national and international data space

Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study

Provenance Information for Biomedical Data and Workflows: Scoping Review

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